PYRREGULAR: A Unified Framework for Irregular Time Series, with Classification Benchmarks
Authors: Francesco Spinnato
Cristiano Landi
Venue: ICLR 2026 Poster (2026)
Keywords: irregular time series
classification
Primary area: datasets and benchmarks
TL;DR: This work introduces a unified framework and the first standardized repository for irregular time series classification, enabling consistent evaluation of 12 classifiers across 34 datasets to address fragmented approaches to irregular temporal data.

Abstract: Irregular temporal data, characterized by varying recording frequencies, differing observation durations, and missing values, presents significant challenges across fields like mobility, healthcare, and environmental science. Existing research communities often overlook or address these challenges in isolation, leading to fragmented tools and methods. To bridge this gap, we introduce a unified framework, and the first standardized dataset repository for irregular time series classification, built on a common array format to enhance interoperability. This repository comprises 34 datasets on which we benchmark 12 classifier models from diverse domains and communities. This work aims to centralize research efforts and enable a more robust evaluation of irregular temporal data analysis methods.

Published as a conference paper at ICLR 2026
PYRREGULAR: A UNIFIED FRAMEWORK FOR IRREGULAR TIME SERIES, WITH CLASSIFICATION BENCHMARKS
Francesco Spinnato', Cristiano Landi² University of Pisa, Pisa, "tranccsco.ssinaattolnmmaaa "cristianollande wawaadddddddda.eeee unp...uipp..i
ABSTRACT
Irregular temporal data, characterized by varying recording frequencies, differ- ing observation durations, and missing values, presents significant challenges across fields like mobility, healthcare, and environmental science. Existing re- search communities often overlook or address these challenges in isolation, leading to fragmented tools and methods. To bridge this gap, we introduce a unified framework, and the first standardized dataset repository for irregular time series classification, built on a common array format to enhance interoperability. This repository comprises 34 datasets which we benchmark 12 classifier models from diverse domains and communities. This work aims to centralize research efforts and enable more robust evaluation of irregular temporal data analysis methods.
INTRODUCTION
High-dimensional temporal data is increasingly accessible to decision-makers, domain experts, and researchers (Shumway et al. 2000). It is vital in fields like mobility, healthcare, and environmental science to capture dynamic changes over time. Yet, variations in recording frequencies, durations sensors, and occasional failures lead signals with unequal lengths, gaps, and missing values (Harvey et al. 1998). These traits make real-world temporal data irregular and hard to manage.
Several research communities address the challenge of irregular temporal data from different per- spectives, as its analysis depends heavily on the task, application setting. and modeling approach. As result, the problem spans multiple fields, including mobility analytics (da Silva et al., 2019).
irregular time series (ITS) classification (Kidger et al. 2020). forecasting (Weerakody et al. 2021).
and imputation (Luo et al., 2018) [i & Marlin 2020). , to name a few. Due to this vast amount of tasks, and despite some shared challenges, communities working on irregular temporal data tend to be separated, each relying on its own set of techniques, such as traditional statistical or data mining models (Hamilton) 2020),, neural networks (Wang al.) 2024), or differential equations (Rubanova et al. 2019). often resulting in domain- -spuciii tools and libraries. This not inherently drawback, but can lead to fragmented research efforts. The challenges of irregular temporal data are amplified in supervised learning, where standardized benchmarks are notably lacking. While repositories exist for regular time series classification (Dau et al. (2019), truly irregular datasets, capturing real-world missingness and variability, remain scarce. Researchers often resort to artificially manipulated datasets (Weerakody et al. 202T), introducing assumptions that overlook structural missingness tied to data collection (Mitra et al. 2023). As result, and given that many studies rely on a narrow range of datasets, the generalizability of their methods often remains untested.
We bridge this gap by proposing pyrregular (github. com/ wtw.nnaa/aaa///aa/aaaaaaaaaia haanaa/yyrrggarrrrem a unified frame- work for irregular time series. (I) We introduce a taxonomy of irregularities and a dataset structure in a common array format that improves interoperability across libraries while supporting the handling, visualization, and modeling of irregular time series using existing analysis methods. (2) We introduce the first standardized dataset repository for irregular time series classification, and (3) we leverage this repository to propose the first generalized benchmark for sfate-of-the-art classifiers from different research domains, in an effort to centralize research this topic. Specifically, curate 34 irregular time series datasets and evaluate 12 time series classifiers. Our goal is to empower users to seamlessly explore and evaluate wide range of libraries to address the challenges of irregular temporal data.

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Published as a conference paper at ICLR 2026
UNEVENLY SAKPLED
PARTEALLY OBSERVED
BAIRGRD
0-00 00
OOOKO -00
0O0O P
D-0---0
D 000 0000
Figure 2: Different kinds of irregularity shown on multivariate time series with 2 signals and containing up to 5 timestamps. Missing values are depicted as faded red if they were expected to be recorded, while they are omitted they are caused by raggedness.
Figure I: An example of irregular time series, X, comprising two signals X2 with indices ?,, f2, and the combined shared index t.
2 ORGANIZING IRREGULARITY
As our first contribution, we propose a systematic taxonomy that clearly distinguishes among different forms of irregularity. We begin by defining time series signal.
Definition 2.1 (Time Series Signal). A signal (or channel) is sequence of т observations, each associated to timestamp, i.e., ((I1,t),..... (.) [xte....,, Ft, (
single signal can be irregular for two reasons: uneven sampling, when at least one interval f&+1 - tx differs from constant At, and partially observed, when expected values missing and marked as NaN. The set of real numbers extended with the NaN symbol is here represented as R. We denote with = [4,,-..,44 17] E RR",, the sorted collection of all timestamps where an observation of signal x was, or should have been recorded, and with t = [t] the number of observations.
Definition 2.2 (Time Series). A time series is collection of d signals, X = (x),..., xa} E i
Time series timestamps the sorted union of all signal timestamps, i.e., f = U - R", with
T = jū], as shown in Figure 1 In addition to these intrinsic irregularities, tensor representations introduce a third, structural type: raggedness, that is the necessity of padding due to length, sampling, or alignment mismatches between signals. Hence, there are three independent irregularity causes: wneven sampling, partial observation, and raggedness, as depicted in Figure 2 While these categories have appeared informally in prior literature, here show that they are independent: none implies the others. Unevenly sampled time series do not necessarily imply the presence of partially observed data, as seen in Figure Z (left). This commonly happens in trajectory data, where the timestamps are usually highly uneven, but shared across the latitude and longitude signals. Vice versa, the presence of unobserved data does not imply uneven timestamps, as an observation may be accidentally missing from an overall constant sampling. Finally, neither unevenly sampled nor partially observed data imply raggedness. In particular, the two leftmost time series shown in Figure 2 could be stored in 2 x 4 and 2 x 5 matrices, respectively, without requiring any padding.
Raggedness arises because of different issues created when storing a multivariate time series in an array-like structure. As so, single, univariate signal cannot be ragged by itself. In general, raggedness arises when at least two signals, a and b, do not share the same timestamps, i.e. to + tp. We identify three independent fundamental reasons why this can happen. The first is ragged length, when a and have a different number of observations: Ta + Th. The second is shift, where at least one signal starts and ends before another: (fa,1 < to.1) A (ta.r, to. ). The third is ragged sampling. when at least one element of the sampling intervals differs between two signals, i.e., Ala # Alg.k for some k. where Ala,k fu,k+1 la,k and Als,a 14,4+1 LA Again, none of these, by itself, implies the other, as shown in Figure and, in more detail, in Appendix [B] Combinations of these issues yield highly irregular data, where NaV can indicate either a missing value in a partially observed time series or padding due to raggedness. Moreover, raggedness can exist also in a time series dataset, i.e., a collection of time series, X X= (X (X1..... X,) E xx as all instances share the same sorted timestamps, t = Un_ E, RT. with T = |t]. The timestamp index for the whole dataset is denoted as k [1....;
Associated with time series datasets are often static attributes, which refer to information linked to individual instances that remain independent of the time dimension. These attributes can also serve as targets in supervised tasks. Specifically, we focus on classification, i.e., targets categorical.
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3 RELATED WORK
Datasets and Benchmarks. There is a significant divide in the literature in the availability of datasets and benchmarking efforts, between regular and irregular time series data. Supervised learning for regular time series data is extensively addressed in the literature, with numerous "bake-offss (Bagnall jet al. 2017] Ruiz et al. 2021; Middlehurst et al.; 2024b) benchmarking state-of-the-art classifiers on hundreds of standard datasets from the UEA and UCR repositories (Dau et al., [019 Bagnall et al., 2018). On the contrary, the benchmarking literature on irregular time series remains limited. While secondary sources, such as [Weerakody et al. [021]] Wang al. 2024), offer surveys specific tasks like ITS' imputation, comprehensive benchmarks for downstream tasks like classification are largely confined to primary studies (Kidger et al. 2020 2 [Shukla & Marlin [221] Du et al., (023). Even within these studies, evaluations are often performed on a small number of datasets. Moreover, benchmark datasets are not always inherently irregular; instead, they are commonly derived from regular datasets through simulation, i.c., dropping valid observations ( (Weerakody et al. 202T). Although this strategy can create ITS, introducing missingness is a non- con-trivial process requiring careful decisions about the type of missingness to simulate (Rubin| 1976). Adding to these challenges, recent study (Mitra jtt al. 2023) highlighted that most research neglects structural missingness, referring to non-random, multivariate patterns of missingness within datasets. Such patterns can be faithfully preserved only by maintaining the original data with minimal modifications, which is the central focus of this proposal.
Libraries. Regarding regular time series data, Python libraries such as sktime (Löning et al. 2019). aeon (Middlehurst et al., 2024a), and tslearn (Tavenard et al. 2020) provide a wide range of classifier implementations, along with access to the UEA and UCR repositories, enabling systematic and reproducible evaluations. Although some of these datasets contain irregularities, the typical approach involves imputing missing values and discarding timestamps during downstream tasks. The most prominent Python library for irregular time series analysis is pypots (Dul (023). pypots offers several classifiers, few partially observed time series datasets, and provides an interface for adding missingness in regular datasets. A limitation of pypots is that it overlooks irregularity from uneven sampling, ignoring timestamps. It also operates within its own ecosystem, lacking interfaces for cross-librar comparisons. This makes using ITS with libraries like aeon and sktime difficult, due to incompatible data formats and requirements, hindering standardization efforts. The primary reason for these challenges is the difficulty in managing ITS due to high dimensionality, missing values, and timestamps. Most libraries for time series prediction require dense 3D tensors to represent time series, signals, and identifiers (IDS), often demanding extensive padding and increased memory usage. To mitigate this, special arrays to represent missing values or variable-length instances are often used. For example, numpy masked arrays (Harris et al. 2020) indicate valid entries with masks but are memory- arrays (Pivarski al.] 2020), jagged pytorch arrays (Paszke et al., (017)) ragged tensorflow arrays (Abadi et al. 2015). zarr, pyarrow, or sparse arrays (Abbasi (2018). Although efficient in managing varied-sized data, these structures cannot inherently handle timestamps. Forecasting libraries like nixtla or gluonTS (Alexandrov et al. 2020) typically use a long format, representing data as tuples (i,j,t,z) z) with instance and signal IDs, timestamps, and observed values. While efficient for forecasting, this format requires pivoting for classification tasks, and static variables are either duplicated or stored separately, causing inefficiencies. Lastly, xarray (Hoyer & Hamman 2017) supports timestamped multi- muuli-diiensional arrays but lacks native support for sparse ITS.
seamory-nefficiemt inefficient since they store both data and masks. Alternatives include awkward
In summary, to the best of our knowledge, no existing array format is capable of representing irregular time series data in all their nuances. To address this limitation, we propose framework that serves as a compatibility layer based on a unified array format, facilitating comprehensive benchmarking across a wide range of datasets and methods from diverse time series communities.
4 A UNIFIED FRAMEWORK FOR IRREGULAR TIME SERIES
This work addresses the gap in the literature on irregular time series by introducing an efficient container specifically designed for such data. This facilitates the integration of methods and datasets from various research communities into a unified framework. We outline key aspects of this solution. (i) Ease of Use: the framework supports several stages of the data science workflow, including visual- ization, preprocessing with classical and temporal slicing, and seamless conversion to dense arrays used in leading machine learning libraries. (ii) Robustness: the implementation leverages established
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PREPROCESSING
HANDLING
CONVERTING
.yYi
.CSV
.py
sparse
numpy
xts
pki
xarray
tarch
Json
custom accessor & backend
time series libraries
malplollib
pooch
CE sktime
Jpypots
aeon
tar
sesbom
HF haffs
diffrax
tslcarn
ARCHIVE
MODEL
Figure 3: A simplified schema of our framework. (left) Data from different sources is preprocessed and represented in our proposed array container (center), which combines xarray with an underlying sparse tensor via custom accessor and backend. This container can be easily manipulated, plotted, and stored. (right) Finally, it can also be converted into a more common dense representation, which can be used for downstream tasks with any standard time series library.
and well-maintained libraries, there is no point in reinventing the wheel. (iii) Flexibility: the container supports several types of time series irregularities. (iv) Replicability: to ensure comparable results, preprocessing is standardized, addressing the variability in ITS. A depiction of the three steps of pyrregular is shown in Figure preprocessing. where the original irregular data is transformed into proposed container; handling. where the data can be explored, manipulated, and stored; and converting, where the data is prepared for downstream tasks.
Preprocessing. The first step in our framework involves transforming raw irregular datasets into the proposed representation. /7S can be found a wide variety of sources and formats (Figure B left), presenting unique challenges in terms of preprocessing. Regardless of the original data structure, our framework requires only a function capable of yielding the data in the standardized long format. In this representation, each row captures the time series ID, signal ID, timestamp, and observed value: (i,j,t,z) The core intuition behind our approach is that the long format closely resembles the sparse coordinate (COO) representation (Duff et al., 2017).
The COO format, as implemented by sparse (Abbasi 2018), can efficiently encode sparse 3D tensors, by using indices for the time series, signal, and timestamp, accompanied by an observed value entry, formally (i, J, k; .z. The key distinction between the long format and the COO representation lies in the handling of the timestamps: while the COO format requires discrete timestamp indices, k; the long format uses real-valued timestamps, l. An example is reported in Figure ( (left). This difference, however, can be easily bridged by mapping the timestamps, t, to discrete positions within the coo array, k. Formally, given the timestamps vector t = [t,.... tT], each timestamp can be mapped its corresponding position (index), in the COO format as = .1....,77 'i (and vice-versa), as depicted in [ With this mapping, converting between the long format and the COO representation can be easily accomplished, as the time series dataset is read once to construct the mapping and a second time to incrementally build the COO matrix by yielding each row as is generated (Figure 4 right). Practitioners need only to define a custom function that, given their own data, incrementally produces rows in the long format. Even when the initial dataset is not organized in this manner, the conversion to the long format is typically straightforward. This process ensures uniformity across input formats and transparency, as the preprocessing steps are explicitly documented this function, and can be reproduced at any time. Though it may be runtime- runtime-intensive, this step needs to be performed only once, after which the library streamlines all subsequent transformations and processing. The output after preprocessing is a sparse tensor, denoted as X E fxndxX
Handling. The COO representation offers advantages over the classical long format. First, it supports airay-like operations with reasonable performance, including reshaping and slicing. Moreover, allows for rapid conversion to task-specific array structures, such as other sparse formats like GCXS
'Code: http://///giiiiu.ccc///g///////p
Examples available in Appendix G
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(Shaikh & Hasan 2013). Compared to classical dense arrays, its primary advantage lies in memory efficiency, as only the recorded observations are stored. All padding is represented by a fill value and remains implicit, meaning it is not directly stored but is generated only when the sparse array is transformed into a dense form. We propose setting such value to NaN to capture raggedness. Further, the COO format naturally accommodates partially observed data by explicitly storing fill value. This allows for distinguishing between the two types of missing data previously discussed. Specifically, an explicitly stored fill value, i.c., a row [i k, NaN), can indicate missing entry that should be present, while implicit NaNs reflect missingness due to data raggedness. In this sense, the COO tensor by itself is enough to represent both ragged and partially observed time series.
However, to capture an unevenly sampled time series, it is also essential to store the timestamps. To achieve this, we leverage the timestamp to COO (t k) mapping using zarray (Figure 3 center). In par- ticular, we use zarray (Hoyer & Ham- man 2017) to store the timestamps and extend it to utilize an underlying sparse COO tensor. These functionalities are pos- sible through our custom backend and ac- cessor, which extend the xarray library, support sparse arrays. Further, xarray naturally facilitates the storage of static at- tributes linked to any dataset dimension, such as class labels in classification tasks. Overall, this approach offers significant storage efficiency, particularly given the typically high data sparsity (see Section 5.2). and en- sures ease of use by supporting all existing xarray functions like timestamp range queries. Further, our accessor enables plotting, while our backend allows direct saving and loading to hierarchical data format, locally online, eliminating the need to perform the preprocessing step again.
Long Format
Mapping
12005
3
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07)0112005
Figure 4: Long format to COO tensor conversion process. Each row of the long format is processed to retrieve the absolute position & of a given timestamp t. The triplet, instance ID (i 1), signal ID (j 2), and timestamp index (k- = 7), is used to populate the sparse COO tensor.
Despite its advantages, xarray is not directly supported by most libraries for supervised learning tasks. Therefore, it is crucial demonstrate how this array structure can be efficiently prepared for such applications Fl Specifically, for classification tasks, X E RaxdxT should be transformed into a dense tensor that minimizes raggedness while preserving the inherent missingness from partially observed time series and maintaining the order of observations within the same time series, This conversion is important because, in classification tasks, raggedness is typically irrelevant to the target and would otherwise result in vast dense arrays filled predominantly with NaNs. For instance, the specific starting dates of time series, such as beginning on January 23rd and b on January 30th, are typically uninformative with respect to the output class, so we generally want to avoid introducing 7 leading NaNs in time series b to account for the shift. For CoO array, this transformation corresponds to dense ranking operation on the timestamp index, ki, performed time series-wise. Formally, for each COO entry (i, ki ær), we produce (i,j. J. rank, (k:), 2)), where:
Converting,
rank;;(k = 1 + HK [1, T:]: K < k)].
This process shifts the timestamp indices within each time series, X,, into a consecutive sequence ranging from 1 to its length, T, As a result, the tensor E R=ndx can be densified into a more compact, X' E ÆuxdxT where T = max," (T)). This ensures minimal raggedness, with the timestamp dimension set to the maximum number of timestamps in any time series. X' can be used by downstream libraries such as sktime (Löning et al. 2019). aeon (Middlehurst et al. 2024a), tslearn (Tavenard et al. 2020), pypots (Du) 2023 and diffrax (Kidger 2021).
CLASSIFICATION BENCHMARKS
We present comprehensive benchmark enabled by pyrregular, in which we evaluate 12 classifiers from a variety of time series libraries on a curated collection of 34 ITS datasets. We assess model per- formance from multiple perspectives, including dataset characteristics, robustness across irregularity types, and the potential for performance improvement through fine-tuning.
We report summary of the main formats used represent regular and irregular time series in Appendix
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Table 1: Datasets used for our benchmarks, divided by irregularity type: unevenly sampled (US),
partially observed (PO), unequal length (UL), shift (SH), ragged sampling (RS).
health
human activity recognition
mobility
sensor
other
synth
GN1
GH2
GP
GP2
GX
GZ
LPA
PAM
PGZ
SGZ
AN
ADC
APT
ARG
GS
Mp
724
RA
DO
TM
JV
PGE
PL.
SAD
ABF
US
X
PO
UL
SH
RS
X
X
Table 2: Summary of evaluated classifiers
Library
Model
Type
Domain
Spinnato et
BORF
dietionary-basee based transform 1.BBM classifier interval- baaedll-baeed transform LGBM classifier
regulur, ragged partially observed
aeon
RIPC
diffrax
Kidger et ml.
2020
NCDE
neural controlled differential equations
unevenly sampled
Cao et al.
2018
BRITS
bidirectional recurrent imputation network
partially observed partially observed
2018
GRU-D
gated recurrent unit with decay
pypots
Zhang al.
2022
RAINDROP
graph neural network
partially observed
Du ct 2023
self-attention-based imputation transformer temporal 2d 2d-vatiaioon inception
partially observed
Wu et
7022
TIMESNET
partially observed
ee ml. 2017)
LGBM
gradient boosted tree kernel-based transform LGBM classifier support vector machine with distance kemel
tabular
sktine
Dempster et al.
2021
ROCKET
regular
[Baghen et
2016
SVM
regular, ragged
telearn
(akoe & Chiba
1978
KNN
distance-based with dynamic time warping
regular. ragged
Datasets. Following established repositories such as UEA and UCR, we compile a diverse collection of datasets that vary in size (small to large), length (short long), and dimensionality (univariate to multivariate), ensuring broad representativeness. We solely focus on naturally irregular datasets, without artificially inducing irregularity (Tables Tandls [ First, our collection contains widely used ITS classification datasets: PhysioNet 2012 (P12) (Silva et al. (012)), PhysioNer 2019 (P19) (Reyna et al. 2020), and the MIMIC-III (MI3) clinical database (Johnson et al. 2016) from the medical domain, as well as Pamap2 (PAM) (Reiss & Stricker (012) for physical activity monitoring. Additionally, we include the 11 variable-length univariate time series classification problems (Guna al., [2014] Caputo et al. 20188 Mezari & Maglogiannis, 2018 Gao et al. 2014) from (Bagnall et al. 2022)). the partially observed datasets (Thler et al.) 2006 City of Melbourne 2020) from (Middlehurst et al.
2024b), and the variable-length multivariate time series classification problems (de Souza 2018
Williams et al. 2006: [hen et al. 2014 Kudo et al.] 1999; Hammami & Bedda 2010) from (Ruiz et 2021) }. We also provide datasets that, to the best of our knowledge, were never used in these kinds of benchmarks. These include data for trajectory classification of entities such as mammals (Fereeo et al.) | 2018). birds (SE) (Browning et al. (2018). and vehicles like buses and trucks (VE), taxis (Moreir- -Mtias et al. 2013) (TA) and combinations of the previous (Zheng et al. 2010)
(GS). Further, we include a small dataset about the productivity prediction for garment employees (Imran et al. 202T) (PGE), and a human activity recognition dataset (Vidulin et al. 2010) (LPA).
Finally, inspired by the classical Cylinder-Bell. Funnel benchmark (Saito) 1994) for regular time series classification, we introduce an irregular version called Alembics-Bowls- Bowbs-cs-BBoss-llsss (ABF), in which the class depends on the skewness of the time sampling. Where available, we use the default train/test split for training and inference, else we set them based on each dataset description and original paper.
Models. The objective of these experiments is to benchmark methods capable of naturally handling ITS without introducing bias through imputation. For this reason, and to keep the benchmarks to reasonable amount, we limit our evaluation to classifiers that inherently support irregular inputs and available in the aforementioned libraries (Table and Appendix (C) As classical baselines, use K-Nearest Neighbors (KNN) with Dynamic Time Warping (Sakoe & Chiba 1978). a time series Support Vector Machine (SVM) with Longest Common Subsequence (I.CSS) kernel (Bagheri et al.

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12
11
10
9
DRUHD
mean F1 rank
RAINDROP
SAITS
BRITS
SAITS
CNN
RAINDROP
NCDE 9.5588
SVM 10:4412)
1,4788
ROCKET
BORF
LGBM
BORE
TIMESNET
TIMESNET
LIBMM
BORF
GRU-D 7.7059
0735
LGBM
SAITS
6.7206
0,5294
RIFC
ROCKET
ROCKET
BRITS
5.7059
6. 8.0598
TIMESNET
KNN
RAINDROP
maan traising rurkima (S)
10"
10
10
10
mean Inderance nundime (G)
10
30"
10
10
3PP
Figure 5: CD plot for the benchmarked models in terms of F1. Best models to the right. Connected models statistically tied.
Figure 6: Mean FI rank against training and in- ference runtimes for the top 11 models across all datasets. The best models on the bottom left.
2016), and a LightGBM classifier (LGBM) trained directly raw ITS, ignoring temporal dependencies. For regular time series models, we include the Bag-Of- OffReccptive--iill. (BORF) (Spinnato et al.)
2024) from aeon, ROCKET (Dempster al.) 2020 2021 via its MINIROCKET version in sktime, and a Random Interval Feature Classifier (RIFC). These models transform the data and rely on downstream classifiers; we use LGBM to handle possible NaNs. For partially observed data, we test
GRU-D (Che et al. 20188) BRITS (Cao et al. 2018), RAINDROP (Zhang ct al. 2022). transformer,
SAITS (Du et al. 2023), and an inception model, TIMESNET (Wu al. al.| 2022)) from pypots, and Neural Controlled Differential Equation model (NCDE) (Kidger et al. 2020) from diffrax.
Experimental Setup. Following standard practice in similar benchmarking studies (Bagnall et al.. 2017 Middlehurst et al. 2024b). all models are trained using the default hyperparameters provided by their respective libraries or those recommended in the original papers. The goal of this benchmark, consistent with prior bake-offs, is to identify the model that best generalizes with single, reasonable parameter configuration rather than fine-tuning each model for individual datasets. For this reason, the results of these benchmarks do not necessarily highlight the best possible model for given task, but the model that generalizes best in many. Each model is allocated two weeks (== 20000 minutes) for training and inference each dataset, with access 32 cores and 512 GB of memory, and to GPU when the model can use i Experiments are repeated three times for highly stochastic models, and the average performance is maintained. We use the Fl score with macro averaging as the primary performance metric, as it is robust in the presence of unbalanced data (Japkowicz) 2013), which occurs in some of our datasets. Accuracy results, along with additional metrics and statistical tests, are reported in Appendix D and are consistent with the following findings.
5.1 RESULTS AND DISCUSSION.
We present a comparative analysis of the aggregate results of the benchmark outcomes. We report critical difference (CD) plot in Figure which ranks models in terms of F1. Models are arranged from right to left, with lower ranks indicating better performance. Models connected by a horizontal bar are statistically tied under a one-sided Holm-corrected Wilcoxon signed-rank test with a significance threshold of 0.05. ROCKET emerged the clear top-performing model, demonstrating consistent superiority across the datasets. Even if this result aligns with its established reputation as one of the best models for regular time series classification (Middlehurst et al.) 2024b). its efficacy irregular data is somewhat surprising, as ROCKET does not exploit any information about said irregularity. Following ROCKET, cluster of methods, including BORF, I.GBM, RIFC, TIMESNET, exhibits statistically tied performance. Lower ranks are occupied by RAINDROP, KNN, BRITS, followed by GRU-D and NCDE, with SVM distinctly identified as the worst-performing model.
Performance vs. Time. Besides predictive performance, runtime is also significant factor. In Figure we compare the average Fl rank against training and inference runtimes, discarding SVM for better readability. The better-per cinttr-perfominggr faster models appear in the bottom-left region of the plot. In terms of training, LGBM is the fastest, followed by RIFC and ROCKET, with ROCKET also being also very fast during inference. For this reason, ROCKET emerges as the best tradeoff between F1 and runtime. Interestingly, despite being designed for tabular data, LGBM performs
SSystem: IBM SYSTEM POWER AC922 Compute Nodes with 2 x 16-core 7.7 POWER9 CPUs, 512GB of RAM. NVIDIA Tesla V100 32GB GPU
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Size
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Figure 7: Mean Fi rank (lower is better) against dataset size in terms of instances (top), number of signals (center), and time series length (bottom).
Figure 8: Mean Fl (higher is better) of the 5 best- performing models for each type of irregularity.
well. This finding aligns with observations in (Tan et al., 2020 where gradient-boosting trees showed strong performance in regular time series regression. LGBM compelling choice due its decent performance and exceptionally fast training time, making it attractive for practitioners needing solid baselines. Neural network- -bared methods, though designed for ITS, underperform in these bake-off-style benchmarks, except for their competitive inference runtime. Similar patterns appear in regular time series classification (Middlehurst al,, 2024b). We hypothesize that simpler, generalist, models, like ROCKET, excel in bake-off settings due to their low-variance, high-bias inductive bias, making them robust across a wide range of tasks, contrary to specialized models, which exhibit strong performance on specific types of irregularity or dataset characteristics, especially after fine-tuning.
Performance vs. Dimension. Figure (top) shows the mean ranks of all benchmarked models (lower is better), stratified by dataset size: small (at most 500 instances) and large (more than 500 instances). KNN and RIFC exhibit a noticeable worsening in rank on larger datasets, indicating limited scalability or reduced robustness the number of training examples increases. In contrast, LGBM, and especially TIMESNET, improve significantly in rank, suggesting that more complex models, particularly inception-basee based ones, benefit from greater data availability to better exploit their capacity. Figure 7 (center) shows the mean Fl ranks for univariate and multivariate time series. While the best-ranked model is again ROCKET, all neural network-based approaches benefit from increased dimensionality, making them particularly suitable for multivariate time series. Figure 7 (bottom) reports the mean Fl ranks stratified by time series length: short (at most 360 observations) and long (more than 360 observations). Here, recurrent models such as GRU-D and BRITS, along with several other neural architectures, tend struggle on longer sequences. R RAINDROP stands out as an exception, likely owing to its graph-based design. Meanwhile, models that rely on localized or interval-based features, such as ROCKET, RIFC, and especially BORF, show improved performance on longer time series, indicating that in this case, simpler is better (more details available in Appendix C
Performance vs. Irregularity. In Figure g we report the average Fl score the top-5 performing models within each irregularity group (higher is better). ROCKET, BORF, and LGBM consistently rank among the top three across unevenly sampled, unequal length, shifted, and ragged sampling time series. GRU-D, while generally ranking lower overall, appears among the top five models in three out of the five groups, showing solid average performance. Partially observed time series exhibit markedly different behavior: here, models designed handle missing data, such as SAITS and BRITS, outperform ROCKET, BORF, and LGBM. This suggests that explicitly modeling missingness can be highly beneficial, particularly for datasets with structured patterns of missing values.
Performance after Fine-tuning. In Table 3 we present the average performance of the top three generalist models, ROCKET, BORF, and LGBM. evaluated in terms of area under the Receiver Operating Characteristic curve (auc) and area under the Precision- Recall curve (aupr) following hyperparameter tuning. These evaluations follow the same 5-fold cross-validation setup and are compared against reference results from (Li et al.) 2023 Liu et al.) 2024 Zheng et al., 2024) on the two most commonly used irregular medical datasets: P12 (Silva et al. 2012) and P19 (Reynal et al. 2020). This benchmark aims to assess whether generatist classifiers can also be effectively fine-tuned for specific tasks, and to compare them with of-te-of-the-ee
specialist deep learning models such CONTIFORMER (Chen et al.) 2024), GRU-D (Che et al. 2018). MUSICNET (Liu et al.) 2024), MISFORMER Zheng et 2024)) and RAINDROP (Zhang et al. 2022). Results indicate that, when optimally fine-tuned, deep learning-basee based algorithms outperform simpler regular time series
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Table 3: Comparison of best-performing models from the bake-off, against baseline reference results (higher is better). Best values in bold, second best underlined.
CONTI FORMER
MTS FORMER
MUSIC NET
RAIN DROP
BORF
GRU-D
I.BBM
ROCKET
P12
auc
74.9=0.0
81.2+0.8
81.9+2.1
78.4+0.0
84.9+1144
86.1+0.4 54.1+2222
82.8+1.7 0.04
aupr
33.4+0.0
43,9+3.0
46.1
38.1=0.0
51.1+3.7
44.0+3.0
15.8+0.0
P19
80.110.0
79.2.2..3
83,911.7
85.2.1..0
88.8.1.5
86,811444
87.012.3
77.310.0
aupr
38. 10.0
35.8+2.3
46.9+2.1
44.1=0.0
57.7+444
45,4+2.77
51.8+5.5
35.2=0.0
classifiers. However, except for ROCKET, which underperforms in this test, this advantage is not always substantial; for instance, LGBB achieves the fourth-best score on P19, outperforming models like CONTIFOR MER and GRU-D. Another advantage of models such as ROCKET, BORF, and LGBBM is that the performance is very stable, with near-zero standard deviation to a single decimal place. This underscores the value of being able to readily apply standard approaches, they can offer fast, stable, and non- aoiiiiiiial baselines. However, deep learning offers more flexibility for optimizing on specific tasks, with reasonable inference times when aiming for performance for deployment purposes.
Performance vs. Trustworthiness. Though not the main focus of this work, we briefly address model trustworthiness, crucial in high-stakes fields like healthcare, where ITS are common. The most interpretable models our benchmark are BORF, which relies on subsequence presence/absence, and RIFC, which uses simple interval-based features, both followed by tree-based model. Neural models can be interpreted with gradient- gradient-based methods, though the reliability of their explanations on ITS is unexplored. The top-performing model, ROCKET, offers little interpretability and depends on expensive model-agnostic techniques (Theisleer et al. 2022). Robustness to random initialization also matters: models with high variance across seeds hinder reproducibility. Stable methods like LGBM, BORF, and KNN may be preferable in sensitive settings, even at some cost in performance.
5.2 COMPLEXITY AND SCALABILITY
The end-to-end cost of the aforementioned classification pipelines in pyrregular consist of: (i) loading the dataset from disk into memory, (ii) converting it into dense representation, and (iii) run- ning the models. The latter component, previously discussed, an external cost, since pyrregular wraps existing state-of-ther tort-----tt--ttteee classifiers from other libraries, and is reported separately in Table In Table we report instead the internal costs for datasets with a size greater than 10MB. The first two columns show the empirical times needed for dataset loading and conversion. Theoretically, the dominant cost arises when converting the sparse COO representation into dense form, which requires ranking the timestamps (Section 4 This amounts to sorting within each time series, leading to complexity that scales linearly with the number of time series and log-linearly with the number of non-null observations per series. Thus, in practice, runtimes are efficient: for example, P19 takes around 3 seconds end-to-end, while the largest dataset, PA2, is converted in under one minute.
The third and fourth columns of Table compare disk usage of our proposed array format with that of the raw data. In most cases, the proposed format either matches or substantially reduces disk requirements. For instance, GS decreases from 0.24GB raw form to 0.09GB with our approach, while the reduction is even more pronounced for TA, which shrinks from 1.81GB to only 0.08GB. These reductions are especially valuable for large-scale datasets where disk I/O is a bottleneck.
The last three columns of Table 4 detail the memory footprint of different representations. The sparse COO representation (ours) incurs a cost of four times the number of non-null observations, accounting for the storage of coordinates and values. Conversion into a minimally ragged dense format leads to a worst-case memory complexity of Onn x T),, where T = max;" (T;) is the longest series length. If the dataset is instead expanded into a fully ragged dense array, the worst-case complexity becomes O(n x d x T), which grows quickly with irregularity. For example, on PA2, the sparse representation required only 3. 3.93GB, compared to 5.33GB for a minimally ragged dense format. The largest savings are seen in highly irregular datasets: for TA, the sparse format used 0.34GB, while the fully ragged dense array would require over 4TB of memory, impractical cost.

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Published as a conference paper at ICLR 2026
Table 4: Loading and conversion times (in seconds) for datasets using the proposed array format, along with disk size consumption (GB) compared to the raw data. Memory usage (GB) of the sparse representation also reported relative dense alternatives. Lower better, best values in bold.
time
disk size (GB)
memory (GB)
dense min raggedness
dense with raggedness
loading
conversion
ours
raw
ours
ABF
0.03
0.06
0.00
0.01
~0.00
~0.00
0.81
AOC
0.09
0.12
0.01
~0.00
0,02
0.01
0.01
APT
0.30
0.42
0.02
0.02
0.08
0.11
0.11
ARC
0.21
0.28
0.01
0.01
0.05
0.14
0.14
CT
0.12
0.16
0.01
0.01
0.03
0.01
0.01
GS
1.25
3.62
0.09
0.24
0.29
8.58
377.15
IW
6.93
13.01
0.36
0.31
2,00
1.64
1.64
LPA
0.07
0.10
0.00
0.02
0.01
0.07
4.02
MI3
0.13
0.00
0.04
0.00
00.00
0,03
P12
0.35
0.65
0.01
0.08
0.1
0.45
6,35
P19
0.96
2.08
0.03
0,24
0.31
3.41
3,43
PA2
13.46
21.35
0.83
1,61
3.93
5.33
21.47
PL
0.06
0.07
~000
00.0
0.01
0.01
0.01
SAD
0.49
0.65
0.02
0.02
0.08
0.08
SE
0.15
0.17
0.01
0.06
0.03
0.02
0.84
TA
1.49
2.34
0.08
1.81
0.34
0.22
4135.02
VE
0.05
0.08
00.0
0.01
0.01
0.01
0.17
CONCLUSION
In this work, presented pyrregular, unified framework for addressing the challenges of ITS. By introducing standardized repository for ITS classification and structuring the datasets a common array format, provided cohesive way work with varying forms of irregularity. Our extensive empirical evaluation of state-of-the-art classifiers and baseline methods on 34 datasets emphasizes both the complexity of this domain and the benefits Results indicate that, with appropriate configuration and tuning, specialist models such as neural networks still attain state-of-the-e diverse tasks remains a significant challenge. Interestingly, simple generalist classifiers originally designed for regular time series data, such as ROCKET, perform remarkably well on irregular time series in bake-off-style benchmarks, even without leveraging the irregularity itself. This observation reveals a crucial research gap: the need to develop generalist methods capable of explicitly exploiting irregularities, such missingness and timestamp information.
shared benchmarking resource.
performance. However, extending their applicability across
The construction of this extensive set of benchmarks was greatly facilitated by pyrregular, which abstracts the complexities of ITS across diverse libraries. While aimed provide a diverse and representative selection baseline models, our choices were also guided by practical considerations such library availability and interface compatibility, rather than exhaustive coverage. We acknowl- edge that several other relevant baselines could further enrich the comparison, such as emerging generalist temporal models, including LLM-based time series frameworks and multimodal pretraining approaches. Our goal was not to be fully comprehensive, but to establish a robust and extensible starting point for benchmarking within unified framework. Further, we deliberately limited the scope of the benchmarks to classification, as achieving the same level of detail for other tasks, such as forecasting, anomaly detection, or imputation, would require an effort comparable in scale to what we present here, and is therefore left for future work. Nevertheless, because the proposed array format is task-independent and some curated datasets already include additional target variables, our framework naturally enables exploration of these tasks (see Appendix F for details). Going forward, pyrregular ar will be extended such additional tasks and integrated with more datasets, In this direction, will also explore dataset- specific analyses for complex clinical data, addressing missingness patterns and label imbalance. pyrregular will further be enriched with methods from broader selection of time series libraries, increasing its relevance across diverse research domains.
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ACKNOWLEDGMENTS
This study has been partially funded by the Italian Project Fondo Italiano per la Scienza FIS00001966 "MIMOSA", by the European Community Horizon 2020 programme under the funding schemes ERC- 2018-ADG G.A. 834756 "XAI". G.A. 101070212 "FINDHR", G.A. 101 120763 "TANGO", by the European Commission under the NextGeneration EU programme- National Recovery and Resilience Plan (Piano Nazionale di Ripresa Resilienza, PNRR) Project: "SoBiggData.it Strengthening the Italian RI for Social Mining and Big Data Analytics" Prot. IR0000013 Av. n. 3264 del
28/12/2021, and M4C2 Investimento 1.3, Partenariato Esteso PE00000013 "FAIR" Future Artificial Intelligence Research" Spoke "Human--entered Al".
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A SUMMARY OF NOTATION
We have adopted tensor-like notation inspired by (Kolda & Bader 2009). The time series dataset structured along three dimensions: the instance dimension, which consists of n instances (e.g., X; denotes the i-th time series in the dataset X); the signal dimension, which includes channels (e.g.. Xi.j represents the j-th signal in time series X;); and the time dimension, spanning 7 points (e.g.. Fi.j.l, represents the ig observation of j-th signal in time series Xi). We use tildes to specify the index being referenced (e.g., t corresponds the k-th timestamp at the dataset's level, while
t & corresponds to the k-th timestamp the time series's 's level). For improved readability, indices are omitted when they are not relevant.
Table 5: Summary of notation.
Notation
X. x,
time series dataset, instance, signal, entry timestamps for time series dataset, instance, signal, entry timestamp index number of instances in dataset number of signals in time series number timestamps in time series dataset, instance, signal indexes for instances, signals, timestamps
t, Lit,, 1
T, T.T.7 1.
B TAXONOMY OF TIME SERIES IRREGULARITIES
In addition to the well-known missingness taxonomy introduced in (Rubin) 1976) (MCAR, MAR, and MNAR), Mitra al. (2023) proposed additional category: structural missingness (SM). While Rubin's 's framework is typically formulated in terms of univariate patterns, SM highlights situations where missingness is organized across multiple variables and exhibits systematic structure. Our primary aim, distinct from previous works, is to preserve such structural patterns of missingness.
Consider, for instance, daily heart rate signals collected by wearables over three months. Data may be missing completely at random (MCAR) when some days are absent because the device randomly fails to sync, in which case missingness unrelated to any variable. It may be missing at random (MAR) when data are more frequently absent on weekends, particularly for users with low recorded activity. It may be missing not at random (MNAR) when users remove the device precisely when feeling unwell, so missingness coincides with unrecorded spikes in heart rate. Finally, it may exhibit structural missingness (SM) when devices differ in recording frequency, such as once per second versus once per millisecond, or when firmware update produces week-long gaps.
In this last case, missingness follows clear temporal patterns tied to device characteristics or design flaws, rather than single variable. Addressing such missingness (or raggedness) should therefore be an intentional modeling choice by the practitioner, not the result routine preprocessing. We pro- vide here formal definitions for each type of time series irregularity and use minimal counterexamples to show that none of these irregularities implies the others.
Definition B.1 (Uneven Sampling). A signal x [.t,,.... .,, s... ] शे is said to be unevenly sampled if there exists at least one index l: E (1,..., ...... - 1} such that the time interval between successive observations not constant, i.c., t 1 th # At for some fixed At e R.
The same definition applies to time series instances and datasets, using their respective indices i, Definition B.2 (Partial Observation). A signal observed if at least one value Tı, is missing and represented by special symbol NaN, indicating the absence of an observation at timestamp where one was expected, i.c. It. NaN for some A E 7I.
( æt, शें is said be partially
Again, the same definition applies to time series instances and datasets. Definition B.3 (Raggedness). Raggedness is structural irregularity that arises in multivariate time series X (xi,...>> xa) d when the component signals do not share common timestamp
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index. Formally, raggedness is present when there exist at least two signals Xa and x such that ta o to. manifests in three independent forms:
(a) Ragged Length: Ta + Tb.
(b) Shift: (la,1 < (b,1) A (la,T. < (b,,.))
(c) Ragged Sampling: Ata,k # At &s for some k. where Atj.k 12,4+1 - -z,k- The index k ranges from to min(Ta, Tb) 1, only intervals that exist in both signals are compared.
The same definition applies to time series datasets.
We now show that the five forms of time series irregularity are mutually independent: none implies any of the others. This is shown through minimal examples of time series that satisfy one irregularity condition while exhibiting none of the others.
B.1 UNEVEN SAMPLING
Let X {xa, } be time series where both signals share the same timestamp index, i == to io (2,12,,,), and assume that the sampling intervals are not constant, i.e., l2 + 12. Then X is unevenly sampled.
UNEVEN SAMPLING PARTIAL OBSERVATION. Suppose that all values in both Xa and x are observed (i.e., none are NaN). Then X is unevenly sampled, but not partially observed.
UNEVEN SAMPLING + RAGGEDNESS. Since i. ig. both signals are aligned the same timestamps. Therefore, X is not ragged.
B.2 PARTIAL OBSERVATION
Let X {xa, } be time series where both signals share the same timestamp index, i -= to t, [t,, t2,tt] ty]. Suppose that one observation is missing, e.g., Fa,t, NaN. Then X is partially observed.
PARTIAL OBSERVATION UNEVEN SAMPLING. Let the timestamps be equally spaced, i.e., 12 -1-11- fg - 1/ = At. Then X is partially observed but evenly sampled.
PARTIAL OBSERVATION RAGGEDNESS. Since both signals defined over the same set of timestamps, i, L. X is not ragged.
B.3 RAGGED LENGTH
Let X = (Xu, be time series exhibiting ragged length, with t., [t1, t2] and i. [t1, 12, t3].
Then the unified timestamp index = [1,,42 42, 1s). and X satisfies Ta = 2 + Tb-
RAGGED LENGTH * UNEVEN SAMPI.IGG Let the timestamps be evenly spaced, i.e., t2 - 11 13 122 At. Then X exhibits ragged length, but is evenly sampled.
RAGGED LENGTH * PARTIAL OBSERVATION. Suppose that all values in both xa and x are observed (i.e., no NaNs). Then X exhibits ragged length, but not partially observed.
RAGGED LENGTH * SHIFT. Although the signals have different lengths, they both start at the same time, 41. Hence, X not shifted.
RAGGED LENGTH * RAGGED SAMPLING. The sampling intervals are identical across both signals, ie., Aa.1 = A t2 - f1. Therefore, X is not raggedly sampled.
B.4 SHIFT
Let X = (xa, xb} be time series exhibiting shift, with i, = [t1, (2] and i. = [/2, 4s). Then the unified timestamp index is f = [t,, t2, tz]. and X is shifted, x,, starts and ends before Xt.
SHIFT # UNEVEN SAMPLING. Let the timestamps be evenly spaced, i.c., to - t1 = ts tt = At.
Then X exhibits shift, but is evenly sampled.
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SHIFT / PARTIAL OBSERVATION. Suppose that all values in both xa and x observed (i.e., NaNs). Then exhibits shift, but is not partially observed.
SHIFT * RAGGED LENGTH. Both signals have the same number of observations, i.c., Ta 7b 2.
Hence, X cxhibits shift but not ragged length.
SHIFT RAGGED SAMPLING. The sampling intervals within each signal are equal, i.e., Afa,
1
Atb,1 13 t2. Therefore, XX is not raggedly sampled.
B.5 RAGGED SAMPLING
Let X = (xx: be a time series exhibiting ragged sampling, with t. = [t1, and éo = [&1, ta]-
Then the unified timestamp index is i t2, t3]. and the sampling intervals differ across signals: A
Ai., 12 41 + t3
RAGGED SAMPLING # UNEVEN SAMPLING. Let the global timestamps satisfy l2 1 1 = 13 l2 A. Then X is raggedly sampled but not unevenly sampled.
RAGGED SAMPLING # PARTIAL OBSERVATION. Suppose that all values in both x, and x5 are observed (i.e., no NaNs). Then X exhibits ragged sampling, but is not partially observed.
RAGGED SAMPLING + RAGGED LENGTH. Both signals contain the same number of observations, Tb = 2. Thus, X is not ragged in length.
RAGGED SAMPLING + SHIFT. Both signals start at the same time, 41, and have the same length. Therefore, X is not shifted.
These examples are minimal and can be easily extended to longer signals and time series. They suffice to establish that all forms of irregularity discussed, both in the main and raggedness subtypes, are pairwise independent. None of them implies any other, as illustrated also in Figure To the best of our knowledge, this taxonomy accounts for all known forms of structural time series irregularity relevant to data modeling and representation.
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C EXPERIMENTAL DETAILS.
In this section, we summarize experimental details regarding the models and datasets.
C.I MODELS
The objective of these experiments is to benchmark methods capable of naturally handling irregular time series without introducing bias through imputation techniques. To achieve this, we limit our evaluation to classifiers that inherently support missing data in their input and are available in major time series libraries. Below, we describe the implementation details and hyperparameters for each method. Parameters that are not mentioned are left to their default in their library implementation.
Beee-f--Reeepive--Fiee (BORF) The Bag of Receptive Fields (BORF) algorithm (Spinnato et al. 2024) from the aeon library extracts discretized subsequences and counts their appearance in the time series, allowing the presence of missing data. downstream LightGBM classifier with default param- eters is used to handle transformed features. For the fine-tuned benchmark, the hyperparameter was on performed on the giggg sio_siinalssd___e_e in the interval [0, 0.2) with 0.05 increments.
Bidirectional Recurrent Imputation for Time Series (BRITS) The BR/TS algorithm (Cao et al. 2018). also from the pypots library, employs bidirectional recurrent network for imputing and classifying incomplete time series. It uses a hidden layer size of 256 and a batch size of 32, Training runs for up to 1000 epochs, with early stopping after 50 epochs of no improvement.
Gated Recurrent Unit with Decay (GRU-D) The GRU-D model (Che et al. 2018). available in the pypots library, extends the Gated Recurrent Unit architecture by introducing decay mechanisms that account for missing data paiterns. The recurrent hidden layer size is set to 256, with a batch size of 32. Training uses maximum of 1000 epochs, with early stopping triggered after 50 epochs of no improvement.
K-Nearest Neighbors with DTW (KNN) This baseline model employs the tslearn K-Nearest Neighbors algorithm, configured to use the Dynamic Time Warping (DTW) distance measure. DTW incorporates temporal alignment to handle time series of varying lengths effectively. The distance computation uses a Sakoe-Chiba band (Sakoe & Chiba 1978) of 10 points, which limits the warping window to fixed radius.
LightGBM (LGBM) LightGBM (Ke et al. 2017) is a gradient-boosting framework optimized for speed and efficiency, and can naturally handle missing values. In this baseline, it is trained directly with default parameters on raw time series data transformed into a tabular format using the sktime Tabul ar aziee For the fine-tuned benchmark, hyperparameter optimization was conducted over a predefined search space that included the number of leaves (num_leaa (eaveeeees) E {31, 63, 127], maximum tree depth (max_dejp mnax_detth) E {-1,7 7,, 10), (learning_rate) E (0.05, 0.1). and the minimum number of samples per leaf (min data in leaf) E {20, 100).
Neural Controlled Differential Equation (NCDE) The Neural CDE model (Kidger et al., 2020), implemented via the diffrax library, learns continuous-time representations of time series data using differential equations. It employs an Euler solver with a maximum of 100 steps, with step size equal to the minimum time difference between any two adjacent observations, hidden layer size of 8, and a width size of 32. Training uses a maximum of 1000 iterations, using Adam as optimizer, with starting learning rate of 0.01, patience of 200 for early stopping, and a learning rate reduction factor of 0.5 after 50 stagnant iterations.
Raindrop (RAINDROP) The Raindrop model (Zhang et al. 2022 graph-based neural network from pypots, handles irregular time series by sending messages over graphs that are optimized for capturing varying dependencies among sensors. This model uses 2 layers, feed-forward network size of 256. 2 attention heads, and a dropout rate of 0.3. Training employs a batch size of 32, with early stopping after 50 epochs of improvement.
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Figure 9: Three examples of instances from the (ABF) dataset, from left to right, Alembic, Bowl, and Flask.
Random Interval Feature Classifier (RIFC) The Random Interval Feature Classifier (RIFC) from the sktime library to generate simple
leverages the statistical summaries (mean, standard deviation, minimum, maximum, median, skewness, and kurtosis) from randomly selected intervals within the time series, with the number of intervals being the logarithm of the time series length. These features are subsequently used by downstream LightGBM classifier to perform classification.
Minimally Random Convolutional Kernel Transform (ROCKET) Rocket, in its Minirocket implementation (Dempster et al., 2021) from the sktime library, employs 10000 fixed con- volutional kernels extract features from time series data. This implementation includes www.kockeetulliivviiattt//eeeeee
which handles multivariate time series while tolerating missing data. The transformation could include missing data; therefore, instead of the most common ridge classifier, LightGBM with default parameters is used. For the fine-tuned bench- mark, hyperparameter optimization was conducted over the number of kernels, num kernels {100, 500, 1000, 5000, 10000, 50000}.
Self-Attention Imputation for Time Series (SAITS) The SAITS model (Du et al. 2023 ) imple- mented in the pypots library, employs transformer-basee architecture specifically tailored for time series imputation. utilizes a dual self-attention mechanism across temporal dimensions, enabling it capture both global and local patterns despite missing values. In this configuration, SA/7S is trained with attention layers, a model dimension of 256, attention heads, and hidden dimensions dę 64, d, 64, and din 128. dropout rate of 0.1 is used for both the transformer blocks and attention layers. The model is optimized over maximum of 1000 epochs, with early stopping triggered after 50 stagnant epochs. Training performed with a batch size of 32.
Support Vector Machine with LCSS Kernel (SVM) This method uses the aktime implementation of a Support Vector Machine, enhanced with the Longest Common Subsequence (LCSS) distance kernel (Bagheri et al. 2016). LCSS is robust to missing values and temporal distortions, it matches time series subsequences with allowable gaps. The kernel uses a Sakoe-Chiin constraint with a radius of 10. Each time series is standardized using z-score normalization. The model is trained for maximum of 1000 iterations.
TimesNet (TIMESNET) TimesNet (Wu et al. 2023) is a modern inception- based architecture designed for multivariate time series modeling, emphasizing temporal receptive fields through learnable convolutional kernels. Its implementation here leverages layers and convolutional kernels with dynamic top-k temporal selection. The model dimension is set to 64, with feed- forward network size of 128. Training conducted using a batch size of 32 over 1000 epochs, with early stopping after 50 epochs without validation improvement.
C.2 DATASETS
The repository includes 34 datasets, each briefly described below, along with the data preparation steps applied. For datasets without predefined train-test split, we created a stratified, instance-hased 70-30% train-test split.
*Data is hosted at httpss///riiiiiieeeedaaaa/////aa
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Alembics Bowls Flasks. (ABF) This dataset is inspired by the classical Cylinder--elll-ummee (CBF) benchmark (Saito) 1994) for regular time series classification. Similarly to CBF, there are three classes, which are Alembics, Bowls, and Flasks. The classes differ by how much the temporal axis is skewed, i.e., if it has positive (Alembie), negative (Flask), or no skewness (Bowl). For each time series, 128 values are sampled from circumference and then standardized. There are 10 instances for each class in the training set and 300 for each in the test set. An example is presented in Figure 2
Animals (AN) This dataset, generated during the Starkey project (Ferrero et al. 2018). consists of trajectories from three animal species-ele deer, and cattle. The classification task commonly used in the literature (Ferrero et al. [2018] Landi et al. (023b a) involves inferring the species based on movement patterns. The target classes in the dataset are balanced, with 38 trajectories for the elk, 30 for the deer, and 34 for the cattle.
Geolife (GS) This dataset was collected during the Gef Project (Microsoft Research Asia) from April 2007 to August 2012 (Zheng et al., 2009; 2008 2010). It contains the trajectories of 182 users and has been preprocessed as detailed in the public User Guide-1.3. One of the most common supervised machine-leaming tasks using this dataset is to identify (a subset) of the 11 means of transportation. We defined three target variables with a decreasing number of classes. The first target variable includes all the means of transportation in the dataset: airplane, bike, boat, bus, car, motorcycle, run, subway, taxi, train, and walk. The second target variable, used in (Ferrero et al. 2018). groups the transportation modes into six classes: bike, bus/taxi, car, subway, train, and walk. The third target variable, used in (Landi et al. 20236), simplifies the classification into two categories: private (bike, boat, car, motorcycle, run, walk) and public (the remaining modes of transportation). In Section S we benchmark the models against the first target variable. In this setting, each class accounts for approximately 9.1% of the total instances, but the standard deviation is 12.7%, i.e., the target variable is highly imbalanced.
GPS Data of Seabirds (SE) This dataset, introduced in (Browning et al. 2018), consists of GPS data collected from 108 seabirds spanning three species: European shag (15), common guillemot (31), and razorbill (62). Similar to the Animals dataset, the species has been used to evaluate model performance in inferring species. The target variable imbalanced, with the majority class (razorbill) comprising 62 individuals, while the minority class (European shag) includes only 15.
Localization Data for Person Activity (LPA) Introduced in (Vidulin et al. 2010), this dataset contains data from individuals performing 11 different actions: falling, lying. lying down, on all fours, sitting, sitting down, sitting on the ground, standing up from lying, standing up from sitting, standing up from sitting on the ground, walking. Each action was recorded by tracking the positions of the body's 'ss right and left ankles, chest, and belt in 3-dimensional space, resulting in 12 distinct signals per time series.
MIMIC-III Clinical Database Demo (MI3) Introduced by (Johnson et al. 2016 2019) on the
Physionet platform (Goldberger et al. 2000), the dataset contains health-related data associated with 40,000 patients critical care the Beth Israel Deaconess Medical Center from 2001 2012. Since the full version is available to credentialed users under strict requirements, we use the publicly available demo version in our work. We preprocess the data in accordance with (Harutyunyan et al.. 2019. The classification target involves predicting in-hospital mortality.
PAMAP2 Physical Activity Monitoring (PA2) This dataset, introduced in (Reiss & Stricker 2012). contains data from 9 subjects (I female, 8 male) performing 19 different physical activities: ascending stairs, car driving, computer work, cycling, descending stairs, folding laundry, house cleaning, ironing, lying, nordie walking, playing soccer, rope jumping, running, sitting, standing, transient, vacuum cleaning, walking, watching TV. The data includes measurements from 3 inertial measurement units (IMUs) positioned on the dominant arm, chest, and dominant side's ankle. Specifically, from each IMU sensor, the dataset contains information about the temperature, the 3-dimensional acceleration, gyroscope and magnetometer data, and the sensor orientation. Additionally, heart rate observations are included. The two types of sensors record data at different sampling rates: 100 Hz for the IMUs and Hz for the heart rate monitor. We preprocess the data according to the authors' guidelines when downloading the dataset. Data from the "transient" activity, i.e., movements between the end of one
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activity and the start of another, was excluded. The remaining 18 activities serve as classification target classes.
PhysioNet 2012 (P12) Published as data for the "Predicting Mortality of ICU Patients: The Physionet/Computing in Cardiology" challenge in 2012 (Silva et al., 2012), the data contains information about the patient, like age, gender, height, and weight, and 37 different types of time series. Similar to the MIMIC-III dataset, the classification target is about predicting in-hospital death.
PhysioNet 2019 (P19) Published as data for the "Early Prediction of Sepsis from Clinical Data: The PhysioNet/Computin in Cardiology" challenge in 2019 (Reyna et al. 2220). the dataset contains demographic information about the patients, such as age, gender, height, and weight, alongside 34 other time-series variables for vital signs and laboratory test values. The classification task involves predicting whether a patient has sepsis or not.
Productivity Prediction of Garment Employees (PGE) Introduced in (Imran al. 202 , this dataset contains information about garment manufacturing processing on a per-team level. Addition- ally, this dataset contains a team productivity performance index, which ranges between and 1. As suggested by the authors, we use this index as a classification target. Specifically, we defined a team efficient the productivity performance index strictly greater than 0.75.
Taxi (TA) This dataset, introduced as part of the "ECML/PKDD 15: Taxi Trip Time Prediction (II) Competition" Moreira-Matias et al., 2013) consists of 121,312 trajectories of Taxis in Porto (Portugal). The classification task to predict the type of call that generated the run. The types of calls could be: A if this trip was dispatched from the central, B if this trip was demanded directly to taxi driver on a specific stand C otherwise. The classes are balanced.
Vehicles (VE) GPS trajectories about two different types of vehicles -buses and trucks- moving in Athens. This dataset is available from download from the Chorochronos Archive (ChoroChronos Archive).
UEA and UCR Irregular Datasets. The other 22 irregular time-series datasets were downloaded from the UEA and UCR dataset repository. In particular, we included the following datasets:
11 variable- univariate time series classification problems from BBannal et al. 2020):
AllGesture WinntteXe WelGestureWimmtee and AllGesture WimmotZZ (GX, GY, GZ) from
(Guna et al. 2014); GestureMidAirD)) MrdArreMMi and GestureMidAArrD3 (GM1, GM2, GM3) from (Caputo et al., 2018); GesturePebble771 and GesturePebble722 (GP1, GP2)
from (Mezari& & Maglogiannis 2018;; PickupGesture WiimoteZ and ShakeGestureWimmtte
(PGZ, SGZ) from (Guna et al. 2014); PLAID (PL) from (Gao et al. 2014):
4 fixed length univariate time series with missing values from (Middlehurst et al. 2024b):
DodgerLLoppDay, DodgerlLoop and BodgerrLoopWWeeenne (DD, DG, DW) from (Ihler
et al. 2006); MelbourmePddestrian (MP) (City of Melbourne 2020 extracted from the City of Melbourne website;
7 variable-length multivariate time series from (Ruiz al. 2021 AsphallObsttalleSCoor dinates, SsphaltPavementTypeee TyypCooodoneettsee and Ssphallkegllartyyooddnna
(AOC, APT, ARC) from (de Souza| 2018); CharacterTrajectoriiee (CT) from (Williams et al., [006]; In- sectWingbeat (IW) from (Chen et 2014)) Japanese Vowels (JV) from (Kudo et al. 1999):
SpokenArabicDigiis (SAD) from (Hammami & Bedda 2010);
Table Ccnottinn the full list of curated datasets at the moment of publication on our repository. The list additionally contains some information about the datasets: the number of instances, #lnst, number of signals, #Sign, and number of observations, #Obs (maxç" (T:)), the number of target classes #TC and the standard deviation between the number of instances per class (CU), Additionally, the dataset contains information about the time series, like the percentage of missing values -ommuuuee as the ratio between the NaN observations divided by the total number of observations- and the sampling coefficient of variation (SCV), alongside information on the different kind of irregularity in the dataset.
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Table 6: Summary of dataset characteristies: the number of instances (#Inst), signals (#Sign), and observations (#Obs); target classes (#TC) and class imbalance (CU); as well as seee--eeresssppciiee metrics like missing values (MV) and sampling coefficient of variation (SCV), and each type of irregularity, i.e., unevenly sampled (US), partially observed (PO), unequal length (UL), shift (SH), ragged sampling (RS).
Cat Name
Source
#inst
#Sigu
#Obs
#IC
(o)
MV (%)
SVC
US
PO
UL
SH
RS
healóh
MI3
Johnson
2016
17
145
2
0.20
0.83
0.60
P12
7990
37
203
2
0.36
0.94
0.59
Reyna el
0.43
0.98
0.18
CT
Williams ml
2006
2858
182
0.01
0.34
0.00
X
GM1
2018
338
360
0.00
0.54
0.00
human artivily recognition
GM2
Capulo ot g1
2018
338
360
26
0.00
0.34
0.00
X
X
X
Eapolo
0.54
0.00
GP1
Mezáni Maplogiannis
2018
304
455
6
0.01
0.52
0.00
X
GP2
Mezain & Maploniannns
2018
304
455
6
0.01
0.32
0.00
GX
1000
385
0.00
0.68
0.00
GY
Cuina et m.
2014
1000
385
0.00
0.68
0.00
X
GZ
385
0.68
0.00
LPA
Vidulan et
2010
273
12
2870
11
0.00
0.95
9.04
V
Sinc
0.82
PGZ
Guna et
10
100
361
0.00
0.60
0.00
X
X
SG2
Guna ct
2014
100
385
0.00
0.37
0.00
AN
Ferrero ot
al
2018
102
291
3
0.03
0.50
1.21
X
V
2013
0.59
0.00
APT
Je Souz4
2018
2111
2371
0.06
0.83
0.00
X
modality
ARC
de Souze
2013
1502
4201
2
0.01
0.91
0.00
GS
2010
5977
96282
0.13
0.99
10.27
MP
Try of Mellhiins
2020)
3633
24
0.00
0.00
0.01
SE
108
6048
3
0.18
0.60
0.00
TA
Moreira Mallas
00
2013
121312
119
3
0.13
0.61
0.00
Chorol brondss Archive
0.57
5.29
DD
Thler et al.
2006
158
288
7
0.01
0.01
0.00
DG
[nter
2006
158
288
2
0.02
0.01
0.00
DW
Thter et
2000
158
288
0.21 2
0.01
0.00
X
IW
Chen
2014
50000
200
22
0.00
0.70
0.00
other
JV
Kudo al.
1999
640
12
29
9
0.03
0.46
0.00
PGE
2021
599
2
0.13
0.19
0.68
/
PL
Gao ct al
211
1074
1344
11
0.05
0.76
0.00
X
x
X
2010
0.37
0.00
synth
ABP
new!
930
128
3
0.00
0.00
1.95
X
Given yn as the labels vector containing only the h-th class, CU is defined as follows:
CU = Oh-o(uh 14)
where a the average number of observations. Given At the vector of differences between consecutive timestamps of a signal, the SCV is computed as the coefficient of variation (the ratio of the standard deviation the mean) for each signal, averaged first across each time series and then over the entire dataset.
We divided the dataset into 6 categories based on the type of phenomena captured: healthcare, human activity recognition, mobility (or more generically, geo-temporal motion), sensors, synthetic data, and others for datasets that don't 't fall in any of the previous categories (like the UCR audio and speech categories).
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D ADDITIONAL RESULTS AND STATISTICAL TESTS
The full result table in terms of Fl is available in Table 8
Further, we provide several other statistical
tests, using a diverse range of metrics, and with respect to different dataset subgroups.
Critical Difference Plots. Figure 10 shows the CD-plots for common performance metrics and runtimes. FI, accuracy, roc-auc, precision, and recall yield consistent rankings for the top four models, ROCKET, BORF, LGBM, and RIFC, as well as for the three lowest-performing ones: GRU-D, NCDE, and SVM. In the mid-range, rankings vary slightly across metrics: for instance, KNN performs notably worse in terms of Fl compared to accuracy, whereas TIMESNET shows the opposite trend. As for training time, KNN, being lazy learner, is the fastest, followed by RIFC and ROCKET. Although LGBM ranks fourth, the previous results in median runtime (Figure O suggest that it may be slightly slower on smaller datasets but highly efficient on larger ones, which contributes to its overall strong performance. Neural network- based models generally exhibit longer training times but benefit from faster inference; nevertheless, ROCKET and LGBM maintain a performance edge across both phases.
F/ CD-plots computed for subsets of datasets with specific characteristics, are shown in Figure These plots provide additional and complementary insights to those Figures 7 and [ Notably, they reinforce the observation that models explicitly designed for partially observed data tend to outperform more general-purpose purpose approaches, even though the top rankings remain closely contested among SAITS, RIFC, IGBM,, BRITS, and ROCKET. BRITS and TIMESNET, in particular, show strong performance on shorter datasets, ranking second and third, respectively, and closely trailing ROCKET. The remaining plots are similar to those discussed in Section 5
Multiple Comparison Matrices. While the widely used CD-plot is effective, it has been criticized in (Ismail- et al.) (023) for its susceptibility to manipulation, as the average rank of a model can be influenced by the performance of other comparators. For this reason, we also propose MCM matrix for several metrics in Figures 12 to [4] However, in our case, results are consistent with the CD-plots presented the previous paragraph, and the main text, and are presented here in the appendix only due to space limitations. Again, the top four models are always ROCKET, BORF, LGBM, and RIFC, and the lowest-perte perffss-erformingg are GRU-D, NCDE, and SVM, with mid-range models rankings changing slightly from metric to metric.
Additional Performance Runtime Plots. We report in Figures 15 19 the performance rankings across multiple metrics, dataset subsets, and with respect to both training and inference times. addition to the insights discussed in the main text, these figures reveal that neural network- based models tend to cluster together in terms of both runtime and performance, regardless of the dataset subset or evaluation metric. This suggests that, although their relative rankings may vary. their overall behavior remains consistent.
Rank Correlation. We report in Figure 20 the Fl rank correlation among models. Models are hierarchically clustered using average linkage applied to the rank correlation matrix. Positive correlations indicate that models tend to perform similarly across datasets, reflecting comparable strengths or weaknesses, while negative correlations suggest divergent performance, highlighting complementary behaviors or differing inductive biases. Reinforcing the categorization proposed in the main text, the plot reveals a strong cluster of generalist methods, I.BBM, ROCKET, RIFC, and BORF, which group together at the top hierarchical level. The second major cluster includes the remaining models, with specialist approaches like BRITS and GRU-D showing high correlation, which is expected given their shared RNN architecture. Similarly, TIMESNET and SAITS also form a coherent transformer/inceptionn -tyyle subgroup. Notable exceptions to the generalist/specialist categorization are SVM, likely due to its overall poor performance across datasets, and KNN, which we hypothesize behaves differently due to its lazy learning paradigm based on distances, which could be more prone to sensitivity to dataset-specific characteristics.
Model Failures and Limitations. From these experiments, several model weaknesses become apparent, particularly in relation to specific data characteristics. For example, Figure S highhlghts how RNN-based methods fail to handle long time series effectively, while Table 3 shows that ROCKET underperformed relative its baseline results after fine-tuning.
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Additional insights arise from the CD plots in Figure 11 Comparing the general rankings in Figure Ila with those on specific subsets reveals which models are most sensitive to dataset properties. For instance, Figure Ti shows that the inception- based TIMESNET performs worse on smaller datasets, point also observerd in Section 5 BORF, despite its strong overall performance, ranks third-to-last on partially observed data and declines significantly on short time series (Figures and KNN also struggles under shift and ragged sampling conditions (Figures IIc and Iif. Notably, KNN was the weakest model in terms of memory consumption, which explodes with longer series (Table (
To provide a more fine-grained view, we report in Table model's worst performance in terms ratio between that worst-case rank and its average rank across all datasets. Higher ratios indicate greater variabiliity, phenomenon most pronounced among models that otherwise perform strongly on average, such as ROCKET, BORF, and LGBMM Several notable cases emerge. ROCKET, for instance, performs poorly on ABF, a dataset with highly uneven sampling. Similarly, BORF ranks 2.4 times worse than its average on the Mimic3 dataset, which also highly irregular. Interestingly, L.GBM performs unexpectedly poorly the Garment dataset, whose small size would normally favor tree-based models.
These findings highlight that strong average performance does not necessarily imply robustness across all dataset types. In particular, models often fail on datasets with structural irregularities or atypical sampling patterns.
Table 7: Worst-case dataset performance for each model, along with the ratio between its worst rank and average rank across all datasets. Higher ratios indicate greater variability compared to average performance.
model
worst dataset performance
worst-to-average rank ratio
BORF
Mimic3
2.4
BRITS
WiGGestureWimmte
1.8
GRU-D
Character Trajectories
1.6
KNN
Physionet2012
1.9
LGBM
Garment
2.3
NCDE
ShakeGesture Z
1.4
RAINDROP
InsectWingbeat
1.8
RIFC
GeolifeSupervised
2.2
ROCKET
Abf
3,0
SAITS
Animals
1.5
SVM
AllGestureWimmoe
1.2
TIMESNET
Dodgerl.oopDay
2.0
Impact of irregularity on explanations. As discussed in Section 5 XAI for irregular time series remains largely unexplored. pyrregular allows researchers to work directly with data while preserving its irregularities, avoiding the bias introduced by imputation choices, which is fundamental since explanations are known to be highly sensitive to input variations (Ych et al. 2019). This, however, is only first step. Even when the data retains its irregularity (as in approach), and even when models can handle irregular inputs, the explainers themselves typically cannot. In line with the observations of Cinquini et al. (2023), we argue that this is primarily implementation gap on the explainer side. Addressing this limitation would enable our taxonomy of irregularities be applied to more fine-grained interpretability. For example, it could help distinguish whether model assigns importance to a missing value because of partial observation or because of raggedness, offering deeper insights into the model's behavior under irregular conditions.
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SVM 19.4412
3.4706
ROCKET
SVM
ROCKET
NCDE
BORF
NCDE
8.0118
4,4599
BORF
GRU-D 7.7059
5,0735
LGBM
GRU-D
2.6324
4.7791
LGBM
SAITS
RIFC
SAITS
2,2288
RIFC
BRITS 6.7059
6.0588
TIMESNET RAINDROP
TIMESNET 5.9422
6.2529
KNN
KNN
BRITS
RAINDROP
(a) FI
(b) Accuracy.
SVM 20.2353
3.5244 ROCKET
10.5254
33324 ROCKET
NCDE
BORF
NCDE
BORF
GRU-D
LGBM
GRU-D 2,4539
LGBM
SAITS 6.77M4
5.677
RIFC
BRITS 6.8853
5.3024
RIFC
BRITS
KNN
SAITS
KNN
RAINDROP
TIMESNET
RAINDROP
TIMESNET
(c) Precision.
(d) Recall.
20.6755
3,2500
ROCKET BORF
BRITS
ROCKET
NCDE
SAITS 30.3971
2.1765
RIFC
GRU-D 2.2575
4.5441
LGBM
TIMESNET 2.205
LGBM
SAITS 2.0443
RIFC
GRU-D
BORF
RAINDROP 6,8529
6.5294
KNN
SJ/91
6.2353
NCDE
TIMESNET 6.8212
6.5735
BRITS
KNN
RAINDROP
(c) AUC-AUC,
(f) Total Runtime.
31
10
BRITS 11.4365
KNN
KNN
ROCKET
SAITS 10.4853
2.671
RIFC
SVM 9.3971
LGBM
TIMESNET GRU-D 8,0735
ROCKET
BRITS
7353
NCDE
5.0892
LGBM
BORF
RIFC
RAINDROP NCDE 6.9708
BORF
TIMESNET
RAINDROP
5-1188
SVM
SAITS 6.5147
5.7206
GRU-D
Train Runtime.
(h) Inference Runtime.
Figure 10: Critical Difference plot for the benchmarked models in terms of different metrics, for all datasets. Best models to the right. The performance of models connected by the bar statistically tied, using one-sided Holm-corrected Wilcoxon sign rank test with a critical value of 0.05.
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SVM 10.4412
3,4006 ROCKET
SVM
RIFC
NCDE 8.5588
BORF
KNN
2.0127
.250
LGBM
GRU-D 7.7059
5,2735
LGBM
TIMESNET
ROCKET
SAITS
RIFC
GRU-D
5.0367
5.3750
BORF
BRITS 6.7059
6,8588
TIMESNET RAINDROP
BRITS
6.8333
6.5417
RAINDROP
KNN
NCDE
SAITS
(a) All datasets.
(b) Unequal sampling.
NCDE
SAITS
SVM 11,211
2 B5D0
ROCKET
2,5000
5,4285,
RIFC
NCDE 8,9332
BORF
BORF
2,2877
5.5714
LGBM
GRU-D 7.3833
4 4,00
LGBM
KNN
7,2133
5.7857
BRITS 2233
RIFC
TIMESNET 7.714
6,2000
ROCKET
SAITS
6.8367
5,8000
TIMESNET
GRU-D
5,5000
RAINDROP
KNN
RAINDROP
(c) Partially observed.
(d) Unequal length.
SVM 10.1667
RIFC
SVM 30.5000
3.7727
RIFC.
KNN
LGBM
KNN 8.3182
4,0903
LGBM
TIMESNET
6.5059
5005
ROCKET
TIMESNET
2.3636
ROCKET
NCDE BRITS
BORF
NCDE 7,727
5,509
BORF
RAINDROP
BRITS
RAINDROP
GRU-D
6.7778
62778
SAITS
GRU-D 5.9911
6.7727
SAITS
Shift.
Ragged sampling.
12
11 10
12 11 10
SVM 3.9706
5582 ROCKET
SVM
ROCKET
NCDE GRU-D TIMESNET 7.1765
BORF
NCDE 9.2235
4.6176
RIFC
GRU-D
BORF
5,5588
LGBM
KNN 7,0000
4.9412
TIMESNET
SAITS 5.6471
KNN
BRITS 6.7647
6.2059
RIFC
BRITS
6.3355
RAINDROP
SAITS 5.6471
6.5000
RAINDROP
(g) Small (< 500 instances).
(h) Big (> 500 instances).
12
11 10
SVM
ROCKET
SVM 11.2300
ROCKET
NCDE 8:944
49167
BRITS
GRU-D 2,4062
7,750
BORF
KNN BORF 5.778
TIMESNET
BRITS
RIFC
5.558
LGBM
NCDE 8.4062
5655
LGBM
RIFC
GRU-D
TIMESNET
KNN
SAITS
6.41.77
RAINDROP
6 6,9588
6-4052
RAINDROP
Short ( 360 observations).
Long (> 360 observations).
Figure 11: Critical Difference plot for the benchmarked models in terms F1, divided into different groups. Best models the right. The performance of models connected by the bar is statistically tied, using a one-sided Holm-corrected Wilcoxon sign rank test with critical value of 0.05.
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Figure 12: Summary performance statistics for 12 classifiers on 34 datasets, generated using multiple comparison matrix (MCM). The MCM shows pairwise comparisons. Each cell shows the mean difference in performance, wins/draws/losses. and Wilcoxon p-value for two comparates. The best models on the top left are sorted based the average performance. The more intense the color, the higher the mean accuracy difference w.r.t. the comparate, positive (red) or negative (blue).
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Figure 13: Summary performance statistics for the 12 classifiers on 34 datasets, generated using the multiple comparison matrix (MCM). The MCM shows pairwise comparisons. Each cell shows the mean difference in performance, wins/draws/losses, and Wilcoxon p-value for two comparates. The best models on the top left are sorted based on the average performance. The more intense the color, the higher the mean accuracy difference w.r.t. the comparate, positive (red) or negative (blue).
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Figure 14: Summary performance statistics for the 12 classifiers on 34 datasets, generated using the multiple comparison matrix (MCM). The MCM shows pairwise comparisons. Each cell shows the mean difference in performance, wins/draws/losses, and Wilcoxon p-value for two comparates. The best models on the top left are sorted based on the average performance. The more intense the color, the higher the mean accuracy difference w.r.t. the comparate, positive (red) or negative (blue).
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10
NCDE
m7en sccurasy rank
GRU-D
GRUID
SAITS
SAITS
TIMESNET
JIMESNET
RAINDR88'S
JANNBAOP
JOHN
AIFC
JGBM
yORF
pORr
BOCKET
ROCKET
305
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inference runtima
Accuracy.
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NCDE
NCDE
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JFCC
3ORF
GGM
BORF
ROCKLT
ROCKET
16*
10"
mean training runtime (5)
mosh inference runtime (s]
(b) AUC-
Figure 15: Average performance rank (lower is better) vs. training and inference runtimes (lower is better). Best values are on the bottom-left of each plot.
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5VM
10
NCDE
NCDE
mosn prociion rank
GRU-D
GRU-D
BRITS TIMESET
SAIBRITS
RAINDROP
TIMESNET
JOHN
1GUM
BORF
SORF
ROCKLT
ROCKET
training runtimm
(a) Precision.
11
JVM
10
9-
NCDE
NCDE
m123 recall rank
8-
GRU-D
GRUUD
BRITS
SAIPRITS
TIMESNET
TIMESNET
JOHN
RAINDROP
RAINDROP
LGBM
LGBM
BORF
BORF
ROCKET
ROCKET
mean training runtime
mosn inference runtime (st
(b) Recall.
Figure 16: Average performance rank (lower is better) vs. training and inference runtimes (lower is better). Best values are on the bottom-left of each plot.
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10
KNN
moan F2 rank
TIMESNET
IMESMET
7-
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SAITS DRITS RAINDROP
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NCDE
NCDE
moan rank
SVM
SUM
BORF
BORF
JNN
TIMESNET GRU-D
TIMESNET GRUDD
ROCKET
RAIDDROP BIITS
ROCKET
RAINDROP BAITS
SAI
SAITS
mean training runtime
mosh inference runtime tst
(b) Partially Observed.
Figure 17: Average Fl rank (lower is better) vs. training and inference runtimes (lower is better) for subsets of datasets. Best values are on the bottom-left of each plot.
34

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Published as a conference paper at ICLR 2026
SVM
10
NCDE
NCDE
GRU-D
GRI-D
f2 F2 rank
BRITS
BRITS
SAITS RAINDROP
SAITS
JNN
MAINDROP
TIMESNET
TIMESNET
RIC
CFC
LGBM
LGBMM
DORF
yORF
ROCKET
ROCKET
training runtime
inference
(a) Unequal Length.
11
SVM
SVM
10
9-
moan rank
NCDE
BRITS
NCDE
GRUDD
BAITS
SAITS
SAITSRAINDROO
RAINDROP
poRr
yORF
LGBM RICC
LGBM
BICC
mean training runtime (5)
mosh inference runtime tst
(b) Shift.
Figure 18: Average Fl rank (lower is better) vs. training and inference runtimes (lower is better) for subsets of datasets. Best values are on the bottom-left of each plot.
35

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Published as a conference paper at ICLR 2026
11
SVM
SVM
10
JONN
NCDL
TIMESNET
TIMESNET
moan 71 1a/k
BRITS
NCDE
BRITS
GRSARTS
RAINDROP
RAINDROP
6-
5-
PORF
BOCKET
ROCKET
LGBM
LGBM
4-
34
mean training
(a) Ragged Sampling.
Figure 19: Average Fl rank (lower is better) training and inference runtimes (lower is better) for subsets of datasets. Best values are on the bottom-left of each plot.
-0
LGGBM
RIFC
BORF
ROCKET
BRITS
GRU-D
NCDE
KNN
RAINDROP
TIMESNET
LGBM
RIFC
BORF
ROCKET
BRITS
GRU-D
NCDE
SVM
KNN
RAINDROP
SAITS
TIMESNET
Figure 20: rank correlation between models. Models are hierarchically clustered using average linkage applied to the rank correlation matrix. Positive correlations indicate that models tend to perform similarly across datasets, reflecting comparable strengths or weaknesses. Negative correlations suggest that models excel different datasets, revealing complementary behaviors or distinct inductive biases.
36

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Published as a conference paper at ICLR 2026
Table 8: FI score on the test set for each dataset and each classifier. The average of 3 runs is taken for methods that highly depend upon initialization, i.e. all approaches besides BORF, KNN, LGBM, and SVM. Missing values are due exceeding memory maximum runtime. The best values for each dataset are in bold.
TIMESNBT
0.31
0.61 00001
0.62
0.86.1002
0.9910000
0.95=0002
0.20=
0.42+0003
0.63+
0.50:0004
0.45
0.32
0.73
0.58+0004
0.44=0000
0.44
0.33
0.60
0.97
0.27
0.42
0.93=0001
0.56
0.71
0.43
0.57
0.45
0.99:
0.24
0.49=0.09
0.25
0.59
SVM
0.32
0.07
0.02
0.05
0.10
0.68
0.23
0.85
0.96
0.04
0.13
0.01
0.16
0.43
0.11
0.13
0.06
0.02
0.47
0.02
0.35
0.44
0.46
0.05
0.40
0.16
0.20
0.62
0.26
0.13
0.40
0.30
0.391:0.03
0.75
0.8410001
0.9910.00
0.941:0.08
0.43+00.6
0.57+0110
0.96
0,411002
0.2410025
0.2740.11
0.75+.02
0.54+0.10
0.334.009
0.43
0.191012
0.131007
0.9610.01
0.26110.5
0.46+010
0.6740.34
0.55
0.724:0.0
0.651022
0.65
0.3910.06
0.95
0.27+0022
0.574011
0.25
0.624:0.03
SAITS
ROCKET
0.17=+0.00
0.90010.04 04
0. 0.801:001
0.96 1.000
0 0.99 10.01
0.98 10.00
0.54
0.34+
0.2:::0000
0.6611002
0.57110.5
0.48
0.89 +0002
0.85 +9.05
0.31
0.70++000
0.70
0.69
0.53
0.9410001
0.024:001
0.35
0.94 0.94+0.00
0.47
0.7140.01
0.661:010
0:001:000
0.73
0.85
0.98 +0000
0.80 +0000
0 0.88 +0002
0.57+:001
0.94120002
0.17+0000
0.88+1002
0.681-003
0.881:003
0.77:10.28
0.94:0002
0.49=-0.44
0.34+
0.42
0.491002
0.361-00
0.2611005
0.80+:004
0.76++005
0.07+
0.47+40111
0.4910.14
0.44100113
0.391:334
0.8910.10
0.32+-200
0.56:
0.90+
0.63#0011
0.661:003
0.37
0.40
0.60
0.47:1002
0.65+:0422
0.82+:004
0.75
0.38+
0.901-002
RIFC
RAINDROP
0.27=0001
0.64=0005
0.66
0.77
0.9710001
0.97=0000
0.45=004
0.60
0.78
0.46
0.3020.15
0.31
0.81 =005
0.63--.110
0.44
0.46
0.33
0.02=0000
0.9410002
0.33
0.36
0.74#000
0.56=0002
0.69
0.481026
0.46
0.53
0.98
0.40=015
0.46=0004
0.25=
0.65
NCDR
0.41
0.43
0,51
0.69.1000
18110000
0.87+001
0,29
0.51#004
0.80
0.231002
0.201008
0.17
0.32
0.35+0.1
0.52+0.29
0.13
0.26
0.11
0.101002
0,57
0.441005
0.34
0.63+002
0.49+0.01
0.571020
0.30
0.281001
0.74#001
0.33
0.12:005
0.33+001
0.631.008
LGBBM
0.17
0.80
0.68
0.80
0.95
0.95
0.52
0.34
0.42
0.57
0.39
0.25
0.78
0.73
0.13
0.50
0.55
0.48
0.71
0.93
0.53
0.41
0.96
0.55
0.75
0.33
0.40
0.54
0.71
0.97
0.42
0.65
0.77
0.94
KNN
0.31
0.80
0.64
0.34
0.36
0.98
0.44
0.90
0.97
0.33
0.26
0.14
0.75
0.74
0.65
0.65
0.62
0.96
0.02
0.35
0.88
0.12
0.78
0.61
0.64
0.97
0.38
0.77
0.88
GRU-D
0.28 28.00 09
0.68
0.31
0.491103/
0.63 1..10010 10
0.64
0.46:
0:71-
0.63
0.24 10008
0.401:.08
0.06
0:
0.56+0..4
0.114:0.4
0.13
0.10=0004
0.61
0.21
0.35
0.92
0.602
0.55
0.78 10000
0.26 14
0.20 10
0.49--0.17
0.38
0.23
0. 10.0010001
0.96
0.99
0.
BRITS
0.33+0001
0.65::0.00
0.301:0.00
0.7810002
0.9910.00
0.9610005
0.52++0.2
0.724-0.7
0.93
0.471:004
0.3210005
0.2210002
0.274:0.06
0.31+000
0.094:0.9
0.18+0007
0.1740009
0.6510001
0.9610.00
0.281:020
0.42+:0111
0.92+000
0.46+00.00
0.4910000
0.7810.00
0.3410003
0.361
0.99+:0.00
0.48+0115
0.34
0.23+0000
0.501:0.08
BORF
0.17
0.80
0.82
0.91
0.95
0.94
0.51
0.34
0.42
0.58
0.50
0.34
0.88
0.79
GS 0.41
0.55
0.64
GZ 0.58
IW 0.48
0.71
0.73
0.27
0.85
0.51
0.71
0.53
0.40
0.46
0.87
SAD 0.98
0.47
0.75
0,42
0.97
ABF
AN
AOC
APT
ARC
CT
DD
DG
DW
GM1
GM2
GM3
GP1
CP2
GX
GY
JV
LPA
MI3
MP
P12
P19
PAM
PGE
PCZ
PL
SE
SCZ
TA
VE
37

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Published as a conference paper at ICLR 2026
Table 9: Total runtime (seconds) for each dataset and each classifier The average of 3 runs is taken for methods that highly depend upon initialization, i.e. all approaches besides BORF, KNN, LGBM, and SVM. Missing values are due exceeding memory maximum runtime. The best values for each dataset are in bold.
TIMESNET
65
2031:20
2601 2601 1-438
2128311 1363
18775 1290
1989
232+:30
64:12
310:330
5861104
1078
6341-26
575
797+149
1202+373
1151
1194
5825 1006
3371142
35634:126815
81::12
4894-90
7817+1145
51343-6196
21
1971:1
7031
699 +9936
31224559
2394:34
92470=220583
23391119
SVM
20
11
1196
14178
8021
2898
22
7
102
103
103
105
307
311
306
279477
41
99
950
5764
144163
8
2666
17106
2019
10
976
SAITS
76
3441-40
5764
113432117044
24825311008
35161 590
404484
174+7
223
1156
1528
1228 122
1450
1253+78
2015
2519
2864-647
5257
197.112
3993915076 5576
113
1171=2241
9444+1250
42208
39
3651:6
26156 12239
6956-:56
59800
325
197788430338
1698
ROCKET
1=0
140
23
52
19113
7
0
25
2611
28
5
5+0
26010=1136
17
17=0
17:11
11812
610
27
0:0
142+1
22
2061:2
229631122
0
1
40
103+1
39
2+:0
350:19
6
38
RIFC:
5:
24-0
21
7816
68115
72
2+-0
1+00
14:0
41:0
41.0
41.0
3+t0
3+0
1826
74-0
74:0
74:0
39775
3512
4611
61
18+1
1907
9243--919
1179 1979197
2110
1::0
11:0
675
9
14:0
1305
8:1-
RAINDROP
1642
23
9412
13014122220
8837611.7613
472
39:t9
1949
40
76119
156139
80118
56.6
50+9
85
78
9713
1937-1196
8917
10318
1412
550+
7582+11005
294468=72194
9+1
30415
5162
1966. 398
20918 4-225
25:55
20576++433
108129
NCDE
1290::2051
93424
924-28
18015
175160
243
111
111
243
1581-42
188--00
1361339
91
90+25
4815:16530
524:25
594-11
87424
18867
92-127
1861:0
76
205
1204+200
1971-223
110128
106112
930=1145
123
115
10592+6609
115
LGBM
2
12
139
302
144
613
32
346
364
440
61
71
1358
373
374
230
4533
133
278
3
306
239
751
4004
1
452
82
28
352
32
KNN
10
8
1318
32482
26775
2563
3
95
94
93
82
79
942
939
879
48
447
816
40226
11
4017
17309
164
12
362
GRU-D
334-
121+24
1274
9416.13923
16236115490
5:90
1542-1119
90+224
41+6
67:139
477
466186
467
338
998+:53
430
612-77
533-67
1460
5110
3854
411:10
122+13
4182=998
45115
504099
245
4.122
1829=133
1893-
267
+-0024
1589
46781
BR ITS
230
59521-510
5572-1906 1-906
175011110144 10144
246725 1140003
18061 4777
1989
10634-215
1526--904
10400 2849
1574611939
744221118
6802+5500
6579+1563
4846-1652
53141-447
643913050
706711138
2891199
11204111204
1421
6230+7
72508432259
244147+112157
7841166
218216600
56920-12375
33021 5648
1011113415
1821
861475 461475-86400
76064-5886
BORF
16
18
295
1000
679
300
20
12
12
18
20
22
25
27
5718
62
54
63
31587
23
411
12
26
5455
30858
77647
6
8
108
8487
204
16825
127
ABF
AN
ADC
APT
ARC
CT
DD
DG
DW
GM1
GM2
GM3
GP1
GP2
GS
GX
GZ
IW
JV
LPA
MI3
MP
P12
P19
P42
PGE
PCZ
PL
SAD
SE
SCZ
TA
VE
38

---

Published as a conference paper at ICLR 2026
E ARRAY STRUCTURES
We report summary of the main formats used to represent regular and irregular time series data in the literature in Table 10
Table 10: Overview of the main formats used to represent regular and irregular time series data in the literature, categorized by tensor type. The table details the underlying data structures (classes), the software libraries that implement them, their usage across the time series libraries considered in this study, and their support for timestamps and tensor operations.
Type
Format
Library
Class
Usage
Timestamps
Tensor Ops.
numpy
Array
numpy
Array
sktime
Dense
numpy
Array
talearn
X
3D Tensor
numpy
MaskedArray
X
jax
Anry
diffrax
V
tensorflow
Array
X
torch
Tensor
pypots
X
aukward
AwkwardArray
X
Ragged
tensorflow
RaggedTeensor
X
3D Tensor
torch
NestedTensso
X
zarr
RaggedArray
pyarrow
ListArray
X
Sparse
sparse
GCXS
Tensor
sparse
DOK
X
sparse
COO
x
Nested List
python
List(Array]
aeon
X
Other
3D te
xarray
Dataset
Long
pandas
DataFrame
sktime
Multilndex
pandas
DataFrame
aktime
only separate channel
** with additional tensors for static variables
39

---

Published as a conference paper at ICLR 2026
F EXTENDING PYRREGULAR TO OTHER TASKS
As noted Section 6 our framework is designed to extend naturally to several additional tasks beyond classification. In particular, highlight regression, forecasting, and anomaly detection, which are already supported at the representation level and require only minor adjustments to dataset metadata or the inclusion of auxiliary variables.
Regression. This task involves predicting continuous outcomes and is directly supported by our framework. Typical targets include SAPS-I (Simplified Acute Physiology Score) in PhysioNet 2012 or raw productivity in the Garment dataset. To demonstrate feasibility, we provide an early, illustrative benchmark in Table 11 covers four datasets (MI3, P12, P19, PGE) and evaluates the top three generalist methods from the classification experiments (changing the head to regression one), alongside a NAIVE baseline that returns the mean target value. No tuning was performed, and the target definitions have not yet been curated; this example is intended only an initial proof of concept. preliminary code snippet for regression is included in Appendix
Table 11: Pilot regression benchmark in terms of rmse on a selected number of datasets and classifiers.
Lower is better, best results in bold.
targer
NAIVE
BORF
LGBM
ROCKET
MI3
Age SAPS-I
20.55
21.44
16.67
20.21
P12
5.97
4.35
4.11
5.67
P19
Age
16.79
16.56
14.62
16.68
PGE
Raw Prod.
0.09
0.09
0.09
0.09
Forecasting. Here the objective is to predict future values time series given its history. We plan to introduce static variable with cutoff point indicate the train/test split, and to extend the accessor method to provide users with a straightforward mechanism for performing this split.
Anomaly detection. This task aims identify unusual irregular patterns in the data. Since anomalies may have the same shape as the underlying dataset, they cannot be indicated via static variables. Instead, leveraging the support for additional data arrays in xarray, will represent anomalies using sparse binary masks that flag anomalous regions in the time series.
of representative models for such tasks. non- comprehensive list includes recent work introducing dynamic graph networks for medical data (Loo al. 2024). y basee- transformers for irregular series (Li 2023), channel harmony strategies (Luu et al. 2025a), graph neural flows (Mercatali et al. 2024), temporal graph ODEs (Gravina et al.J (2224) , state space models (Gu al.j 2022). patching graph neural networks for forecasting (Zhang et al. 2024), and TabPFN with feature engineering (Hollmann et al., 2023) to name a few. We further aim incorporate (at least from an inference perspective) emerging pre- trained generalist frameworks designed operate across diverse temporal domains. These include large-lagguage-modde -eoog---be-eell--aeeeeeeeeeee-ed temporal reasoning architectures (e.g., Time-LLM, (Jin et al. 2024)),
Model support. We also plan support
cross-modal alignment frameworks such CALF (Liu et al. 2025b), and multimodal pretraining strategies capable of unifying sequential signals from heterogeneous sources (King et al. (202)). Such models highlight trend toward universal temporal representations that can handle varying sampling rates, modalities, and tasks within a single foundation model.
40

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Published as a conference paper at ICLR 2026
G
QUICK GUIDE
Extensive documentation and examples are available at Bittps://githut com/ faniinapi pyrregular Below, provide quick start guide and simple workflow notebooks.
pip install pyrregular mmdelss]
G.1 LIST DATASETS
you want to see all the datasets available, you can use the kist_dataseto function:
from pyrregular import list datasets df 1ist_ datasets()
G.2 LOAD A DATASET
To load a dataset, you can use the Load_datases function. For example, load the "Garment" dataset, you can do:
from pyrregular import dat_set df coad_dataset "GGammen.hh
G.3 CLASSIFICATION
To use the dataset for classification, you can just "densify" it:
from pyrregular import load dataset
df Leac dataset ("Garment.hs)
x, = ss..irr.o_ddnne wtt.rreetttsaktttettttoe
y, split df. df.irr.ee..
()
Xttrain, Xeest X(split != "test"), X(split == "test"] y_train, y_test (split != "test"], ysplit "test"]
We have ready-to-go models from various libraries: import rocket pipeline
from www.rguular.mdelss.se.
model = rocket_pipeling ine
model. fit ix_train, y_train) model. score (X_test, y_test)
The dataset can be also easily used in pytorch
from torch, utils. data import DataLcader, TensorDataset import torch
data TensorDataset (X,
dataloader DataLoader (data, size-1,, shuffle-True)
41

---

Notebook: Basic Workflow
[nn : import pandas as pd
import xarray
List available datasets
To view available datasets, you the list_datasets function.
In]
from pyrregular import iist_datasets
[In]
print (1istt_attasets(()
('Abf.h5', SintessttoWWiimoteel hh',, 'AllGestureWLinootY..b
A11GesstureWLinntt...htt h5', 'Animals.h5
'Asphal "Asphal cbsaaaaoobaaceeeoooddanteeeeee h5',,
AappaallPeeeeentyyyoorrrriaaaas h5', 'Asphal Segphaalttagularttyoorrrnntee..
Charaacte7raajectoriees. h5', 'DodgerLoopDay.hs h5'', 'Dodgerl.oogGame.. h5', "DodgerLoopweskeend h5', Carment.hh5' (GeolifeSupervieed.
'GestureMidhirD1..e
'GestureMidMirD2..t
"GestureMidkirD3.. hb', 'GesturePebble21. h5", "GesturePebble22.tt
JJpaneseVowelss. h5', 'Ldfpa.h5', h5' "MelbournePedestriaan..t 'Mimic3.h5
'PLAID 15' 'Pamap2.h5" 'Physionet2012.bb h5', 'Physionet2019 h5',
Piickup@estureWiinmote..hh h5', 'Seabirds.ht" "ShakeefestureeW1motto...
"Spoken.Arabicoiiittss.b 'Taxi.h5', 'Vehicles.hs h5']
Loading the dataset from the online repository
Loading dataset from the online repo simple calling the dataset function with the dataset name.
[In]
from pyrregular import load_ doaa_dataset
[In]
ds
("Garment.
The dataset loaded an xarray dataset. The dataset is saved in the default cache directory, which can be found with:
import pooch
print (pooch.os. os_ ca__c www.cccchhhppcceece ("pyrregular"))
You can also use xarray to directly load a local file. In this case, you have to specify our backend as pyrregular in the engine argument.
import xarray as xr da load_ dataset 1rad__aatttte ("path//oo/flle.nt engine= "pgrenelpyrrruularr"
You can view the underlying DataArray by calling the data variable.
[I]]
ds data
[I]]
da
42

---

[Out]:
Ccarray.Datakee 'data' (ta_id: 24, sigmal_id: 9, time_id: 59)> Size: 329kB <C00: 224, 9, 59), dtype=float64, mnz=10267, fill_value-nan
Coordinates:
day
(time_id) <09 2kB 'Thursday'
'Wednesday'
department
(ta_id) <U9 864B 'finishing'
'sweing'
broduettivity.bnarry
(ta_id) int32 96B 0 10110 1111
0
productiváby_class
(ta_id) <04 384B 'high' 'low' 'low" 'high'
productivvty_numerrl.e
(ts_id) float32 96B 0.8126 6.6283 0.7005 0.7503 (time_id) <08 2kB 'Quarter1'
quarter
'Quarter2'
signal_id
(signal_id) <U21 756B 'idle_men'
'wip'
split
(ts_id) 480B 'train' 'train'
'train' 'train'
team
(ts_id) int32 96B 10 11 12 3 3 4
time_id
(time_id) datetime64[ns) 472B 2015-01-01101:00000....
te_id
(ta_id) <U12 1kB 'finishing_1
'sveing_9'
Attributes:
_fixed_at:
04: 4087990-2220
_is_fixed:
True [Abdullah Al Imran, Xd Shamsur Rahim, Tanvir Ahmed]
author:
confige:
{ ('default': {'task': 'classification', 'split': 'split', 'tar...
license:
cc BY 4.0
source:
title:
Productivity Prediction of Garment Employees
[In] # the shape is (n, series, channels, n. timestamps)
shape
[Out]
(24, 9, 59)
[In] : # the array is stored as sparse array da. data
[Out]: <c00: shape-(24 (24, 9, 59), ftype-float64, nnz-10267, fill aill_vale--nan>
[In]
dimensions contasn the time series ids, signal ids and tamestamps
da. dims
[Out]
"t'_idd, 'sigmal_id" id', 'time_id')
[In] :
e.g., these are the time series ids da ["ta_id"] data
[Out] array([ (''finishing..' 'finishing. 10', 'finishing.11', 11', 'finishing.12',
'finishing_2', 'finishing_3" 3', 'finishing_4", 'finishing_57 'finishing_6', 'fimishing_7', 'finishing_8', 'finishing_9',
ssweing_'', 'sweing_10', 'sweing_11", 11', 'sweing_12', 'sweing_2',
'wweing33', 'sweing_4', 'sweing_5', 'sweing_6', 'sweing_7', sweinng8', 'sweing_9"], dtype='<U12")
43

---

[In] : # there are also static variables, such as the class
paroductiiitty.inaary" data
[Out] array([1, 0, 1, 1, 1, 1, 1, O, 0, o, O, 1, 1, 1, 1, 1, 1,
0, 11, dtypo-int32)
[n] # the train/test split [a[sspit"]] data
[Out] array( (('tain',,, 'train', 'teat', 'train', 'train', , 'teat', 'train', 'train', 'train', 'teat', 'train', 'train', 'teat', 'train', 'train' 'teat', 'train', 'train', 'train', 'train', "teat', 'train', 'train', 'train'], stype='<U51
[In] all the coordinates can accessed via "coords" variable
da. coords
[Out:]
Coordinates:
day
(time_id) <09 2kB 'Thursday' 'Wednesday'
department
(ts_id) <U9 864B 'fimishing'
'sweing'
productiiiity_biarr
(ts_id) int32 96B 101
11 11000
productivity__lass
(ts_id) <U4 384B 'high' 'low' 'low" 'high'
productivity
(ts_id) float32 96B 0.8126 0.6283 0.7005 0.7503 'Quarter''
quarter
(time_id) <U8 2kB 'Quarter1'
signal_id
(signal_id) <U21 756B 'idle_men'
'wip'
split
(ts_id) <U5 480B 'train' "train' 'train' 'train'
(ta_id) int32 96B
12 ... 4 5
time_id
(time_id) datetime64[ns] 472B 2016-01-01101:00000.
ts_id
(ta_id) <U12 1kB "finishing_1"
'sweing_9'
[n]
# metadata contains informations about the datasets and tasks da. attrs
[Out]:
{ "_fixed_at''
_ix_fixed'; True,
'author': ['NA'],
'configa': {'default'; {'task': 'claasification',
'split'; 'split',
'target': prooucctivitybbinaay
'regression': ('task': 'regression',
'split': 'split', 'target': prrductivity..uumrrcaal1
'license': 'cc 4.0',
'source': 'https://archive ies. uci http://ttwt....aattattttaattttta arment-employees'
'title': 'Productivity Prediction of Garment Employees')
44

---

Data Handling and Plotting
Data can be accessed with standard xarray methods.
[In] import matplotlib, pyplot as plt
import numpy up
In] # the först time serses da[O]]
[Out]
<rarray. Crarray. DataArray 'data' (signal_id: iid 9, time_id: 59)> Size: 9kB <C00: shape=(9, 58), dtype=float64, nnz=392, iill_value=nam>
Coordinates:
day
(time_id) <09 2kB 'Thursday' 'Wednesday'
department
<U9 36B 'finishing'
productiivity_binary
int32 4B
productivity__clas
<04 16B 'high'
productivvtty_nmmrri
float32 4B 0.8126
quarter
(time_id) <08 2kB 'Quarteri' 'Quarter2' (signal_id) <U21 756B 'idle_men' 'wip' <U5 20B 'train'
signal_id
split
team
int32 4B (time_id) [ntetime64[ns] 472B 2015-01-01101:000000
time
ts_id
<U12 48B 'finishing_1'
Attributes:
_fixed_at:
_ia_fixed: True
author:
['NA']
configs:
{ ('default': {'task': 'claasification', 'split': 'aplit', 'tar...
license:
CC BY 4.0
source:
attps://archive ics uci. htw.aattwwss/////prppotttttttttt
title:
Productivity Prediction of Garment Employees
[n]] the först channel of the first time serses do, 0]
[Dut]
MaaraayDatakrray 'data' (time_id: 59)> Size: 784B <C00: shape-(59,), dtype=float64, nnz-49, fill_vals valle-ann-n
Coordinates:
day
(time_id) <09 2kB 'Thursday' WWennesday'
department productivvtty_binet
<U9 36B 'finishing'
int32 4B
productivity_class
<U4 16B 'high'
productivityynmmerle
float32 4B 0.8126
quarter si signal_id id
(time_id) <08 2kB 'Quarter''
'Quarter2'
<U21 84B 'idle_men'
split
<U5 20B 'train'
team
int32 4B
45

---

time_ id
(time_id) [atetime64[ns] 472B
ts_id
<V12 48B 'finishing_1'
Attributes:
fixed_at::
2024- 2022-12-44221::::444000000000120
_is_fixed:
True
author:
['NA']
configs:
{ ('default': ('task': 'classification' 'split': 'split', 'tar... CC BY 4.0
license:
source:
attps://archive ics uci
title:
Productivity Prediction Garment Employees
[In] : # to access the underlying sparse vector
da [0, 0] data
[Out]
<c00: a59,)=(59,), dtype=float64, nnz=49, fill_ =al_vaale=nnn>>>
[n] # to access the underlying dense vector da da[o, 4] data. todense()
[Out] array([ 8., 8., 8., 8., 8., 8., 8., 8., 8., 8., 2., 8., 8.,
8., nan, nan, nan, 8., 25., 8., 8., 10., 10., 10., 10., 15.,
19. 19., 10., 10., 12., 10., 10., 10., 12. 12. 12., 12. 8.,
nan, nan, nan, nan, 12., nan, nan, nan, 8., 8., 8., 8., 8., 8., 8., 8., B., 8., 8., 8.1)
[In] : # this vector contains lot of nans, which are the padding necessory to havey shared timestamps w.r.t. whole dataset
np (dal 4] data. todense()) sum
[Out]] 10
[In] : plt. (da 4] ["time_id"]] da [o, 4], marker="o")
[Out]
[<matplotlib 1ines Line2D at 0x172a35b50>]
5-
2015-01-01 2015-01-15 2015-02-01 2015-02-15 2015-03-11
46

---

[In] # using the custom ". trr" accessor, we can filter out the nans to the manimun
amount possuble due raggedness
np. (daa irr [0, 4] data. todense ()) sum ()
[Out]
[In]
plt plat irr [0, 4] ["time_id") da. irr[o, marker="o")
[Out]
[<matplotlib: lines. Line2D 0x172afec30>]
3015:01:01 2015-01-15 2015:02-01 2015:02-15 20158301
[In] # the fourth channel first 10 time series of the dataset, as a heatmap da. ta.irr[::a [:10, 4] p)
[Out]: : <eatplotlib. wtw.iii...olleecnnssc..o QuadMesh at 0x172aefe90>
trshiing7
frisKin_
frishing
frishing
70
frisking_3
frishing.s
frthhin_3z
10
frthhng 11
mosiing10
trstiing
47

---

[In]
# plotting some channels
da ca.irrlo, 2] plot coords siggnal_idd"] [2] .item
da.irr(0, 4] plot (label-da coords ["signal_id"" [4] iitem))) ())
da. da.ir 5] plot (labellld- coords [siinal_id"]] [5] iitmm legend))
[Out]: <eatplotlib.. Legend..egeen at 0x30fe61be0>
department finishing, productivity binary 1...
over over_time
10000
6005
0000
bmo_id
2015 Mar
Downstream Tasks
The xarray nice but not supported by basically any downstream library. Thus, we can convert it into numpy array.
[In]
Xitime
time series data, timestamps
x, T = da. árr (o_dense(
normalize seaiee_ttieerrreee normalize the time indea to E0, 1]
CPU times: 14.9 sys: 1.57 ma, total: 16.4
Wall time:
5.14 ms
[In] the shape is (n_ Lime_series n. channels, n. timestamps), timestamps arey rreturned as separate channel, for dounstream methods that are able to
shape, shape
[Dut]: ((24, 9, 59), (24, 1, 59))
48

---

[In]
# static variables
da coords. to co_dataset() [["split",
"productiiitty_ to_pandas
Z. head()
[Out]
split productivity__inayy department productivity_claase
ts_id
fiiishing_1 1
train
finishing
high
finishing- 10 train
finishing
low
finishing. 11
finishing
high
finishing_ 12 train
finishing
hi high
finishing_2 2 train
finishing
high gh
productiiitsy_mmeriia.
team
ts_id
finishing_
0.812625
finishing _0
0. 628333
10
finishing
0.874028
finishing_12
0.922840
12
finishing_2
0.819271
[In]
# target and split
y. split da.irr.get, task terrggtttasktttrree..ndd
Train-test split
[In]
X_train, X_test - x [split !- "test"), X[split -- "test"] y_train, y_test y [aplit "teat"), ylsplit "test"] L.train.shape y_train shape, X_test. y_test shape
[Out]: ((18, 9, 59), (18,), (6, 9, 59), (6,))
Classification
We have several ready-to-use classifiers in the pyrregular package. Be install the required dependencies.
[In]
from pyrrogular.moc
import rocket_pipeling
[In]
XXtime
model rocket.pippelne
(ieel. y_train)
model. (X_test, y_test)
[Out]
0. 6666666666666666
49

---

Regression (work progress)
Some datasets have regression tasks defined. The usage is the same as for classification.
[In] from sklearn metrics import mean wwwp:abboluueeeercnnteeee
meleicn from sklearn, pipellee import make.pipeline
[In]
# target and split y, split da. irr get_task_tateeeeeeeeeeeeeetstttt targetaaand__pppae. ("ogreeerrressioon)) X_train, X_test = [split 1= "test"], X[split == "test"]
y_train, y_test y [split "test"l, y ysplit "test"]
X_train shapa,..shape, y_train shape, X_test. shape, y_test. shape
[Out] ((18, 9, 59), (18,), (6, 9, 59), (6,))
[I]]
from sklearn dummy import DumnyRegresaor
[In]
from sktime. transformations wttp:/tttitootnneel..ee.meeceeee redcee import Tabularizer
make.pipeline(
Tabularizer(), DummyRegressor ()
model dummy_pipelize
model fit (xtai y_train)
www.aaaboluee__ercettee _erorr testest, model. predic((_ttest)
[Out]: 0. 0.97221919149160335
[In]
from aeon. www.eeormmtiinnn......omm collection Det.ea...e.....nii.iioaoa.aaeee. import BORF from aklearn. linear __odel import LassoCV
borf-pipeling make_pipelinee BORF(),
LassoCV(),
model borf_pipeline
model fit (iiitt_ttraaat (X_train, y_train)
test, model predict (X_test))
[Out] 077544878610867717
50

---

Notebook: Dataset Conversion
The "Long Format"
The basic format to convert any dataset representation is the long format. The long format is simply a tuple:
(time_series_id, channel_id, id, timestamp, value, static_var_t 1,, static_var. _2, ...).
If your dataset contains that in this format, you almost good to go. Else, there will be little bit preprocessing to do.
Case 1. (easy) Your dataset is already in the long format
Let's assume for now your dataset is already this form. Here a minimal working example.
[28]: Import pandas pd
import numpy as
[29] df pd. DataFrame
"time_series_id" up raadoon.chicce(Iaa. choice(["A", "B", "C"], size-100), "channel_id" id": mp. random. choice(["x", "Y", "z"], size-100), "timestamp": pd date_KKKKK caate_rageeeeeee ("2023-01-01", periods-100, (req="H") "value": np. random. randn
af ("labels") df tiime. seriiin_ssrrieeem series_id") map
("A"; 0, "B": "C": 1)
let 's say we have labels df head()
[29]
time_series, id channel, id
timestamm
value
labels
2023-01-01 00:00:00
105162
2023-01-01 01:00:00
-0.573337
2023-01-01 02:00:00 -1.973967
2023-01-01 03:00:00
0.6666
1
2003-01-00 -01
04:00:00
-0.500246
[30]
# Let 's save this dataframe to CSV file
df. to__cs("yoorr wow.goiinnnadaasseedeee.
index-False)
[31]:
csu file can converted our format using our interface
from pyrregular.io utuis import read_csv from pyrregular reader jitteffant import ReaderInterface from pyrregular accessor accessor import IrregularAccessor
class YourDataset
51

---

estaticmethod
def wwwgiiaal_ersiinnvveeeooooo
return read_csv(
"ilenames=""our_oriie dataaatttttss..c..
time_id- "im.dddmmmmmmpp.,
dignal_id="chaneell
value_id- "valuuee""""" dims-(
"ts_id";
"labels"
J. static variable depends on the time serses "signal_id" __d"; [)
"time_id"; 0
b,
indiinidex_ii wwweiieeeeddeeetttaaaaaaaa ses_daettmm-Falee. verbose-verbose,
[32]
YourDataset moaddooiginal_eerraeee (True)
da
Getting dataset metadata: Oit [00:00, ?it/s]
Reading dataset: 0%1
0/100 [00:00<?, ?it/s]
[32]
<xarray DataArray (ts_id: 3, signal_id: 3, time_id: 100)> Size: 3kB <C00: shape-(3, 3, 100), dtype-float64, mnz-100, nan__
Coordinates:
time_id
(time_id) <U19 '2023-01-01 00:00:00' '2023-01-05 03:00...
labels
(ta_id) int64 24B 0 1
ts_id
(ts_id) 12B 'A' 'B'
signal_id
(signal_id) <U1 12B 'X' 'Y' 'Z'
If you don't know if a variable is static, to which dimension it depends from, you check it.
[33]
from pyrregular data_utils import infer_static_con columns
Infer_static_columss (df, "time_series_ic"
[33]
['labels']
The dataset can be saved with our eustom accessor
[34] :
da.irr. hhwfasd"oor_dtttaaaaaaaa h5")
And then loaded directly with xarray
[35]
Import xarray as xr
52

---

[36] :
da2 xr lell__ddtaaeeeeeeeeeeeeeeeeeeeee (aapaeettt"yrruutttaaatee h5", engine "pyrreellyy"")"r""
da2
[36]
<xarray Dataset> Size: 11kB
Dimensions:
(ts_id: signal_id: 3, time_id: 100)
Coordinates:
labels
(ts_id) int32
signal_id id (signal_id) <U1 12B 'X' 'y' '2'
time_id
(time_id) <U19 8kB 12023-01-01 00:00:00 '2023-01-05 03:00...
te_id
(ts_id) <U1 12B 'A' 'B' 'C'
Data variables:
data
(ts_id, signal_io _d, time_id float64 3kB <C00: mnz-100,
fill_value-nan>
Case 2. Your dataset is not in the long format
Let's have 3d numpy array, containing the time series, and numpy array containing only the labels.
[37]
import numpy np
shape (10, 2, 100) time series, channels, 100 timestarn timestamps
data = full fallfulhppppppa (shape, nan)
mask np. random....oo rasap=sh)pe) 0.35
data [mask]
rwndoo. randn (mask. su)())
labels np. random. randint(O, 2, shape [0])
np. save ("your_ ("yourrmore________ee___ coour_more_coe caarre_comllex__teeeee ; data)
save ("your Saatyorrrrre_e_ more caarrreccopleeeeeeeeeeeeeeeee datteee_dttttttabbeeeeeee lapessssstl.blll.ppp........ labels)
data shape, labels shape
[37] ((10, 2, 100), (10,))
You need only function that takes the data and the labels, and returns a dataframe in the long format, yielding it by row.
[38] def read_your__ee cotrr_dataeetttff (filenmmee))))) data np labels np [
(" ["data"])
ts_ids, signal_ide timestamps np (ndices(shape))
ts_ida, signal_ids, timestampa ts_ids ravel signal_ide idda ravel () --timestapps.ree ravele
for ta_ id, signal id, timestamp in 2ip(ta_ide ida, signal,ide ida, timestamps) value data [ta_ id, signal_id, timestamp] if np ap.iinnnvvvalee continue
53

---

label labels[ts_kk
yield dict(
Lime_serie__iie ts__iddd
siinall_,d-ignna_.d,,
dimestamp-timestane
value-value,
labels-label,
[39]
from pyrregular so_utils import read_csv
from pyrregular getregular.raaer_iinne. reader_interfaee import ReaderInterface
from pyrregular accessor import IrregularAccessor
class YourDataset (BeaderInterfface)
@ataticmethod
def orgggnal_verratomarrroee--eee
return read_cav(
filenames=(
"data": yrour_more_complee__dtaaes .ny"", "labels"; },
ta iidd"iimessrrie_.." time time_id=""imee id="ttimmttmmpp,,
signal_id=" "chanel==leee_iid""
value_id iaabueddd=valuue"" dima={
"ts_id":
"labels"
J. # static variable that depends on the time series id "signal_id"" _id"; [], "time_id" 0,
reader_fuud fun-read_yeeee your, _oar_dataeet, time indeinidxiiii wateleex___dattttteeee datetime=False, verbose-verbose, attra={
"authors": "Bond, James Bond", # you can add any attribute you,
[40]
YourDataset. readaaastterreddeeeeee read_originall_eersioo.
da.
Getting dataset metadata: 0it [00:00, 7it/s]
Reading dataset: 0%1
0/720 [00:00<?, ?it/s]
54

---

[40] Exarray.Datakrze (ta_id: 10, signal_id: 2, time_ id: 100)> Size: 23kB
<C00: sh0pe=(10, 2, 100), dtype=float64, mnz=720, all_vaaue=nna>>
Coordinates:
time_id
stime_id) int64 800B 0 1 2 6 92 93 94 95 96 97 98 99
labels
(te_id) int64 80B 0
0
te_id
(ta_id) <U21 8408 '0' '1'
'2' '3' '4' '5' '6' '7' '8' 19'
signal_id
(signal_id) <U21 1688 '0' '1'
Attributes:
authors:
Bond, James Bond
55


Reviews:
[{"review_id": "hjoPChd4Zd", "reviewer": "Reviewer_rR2X", "summary": "This paper studied the fragmentation in irregular time series research by proposing a unified framework and standardized dataset repository for irregular time series classification. Irregular time series, characterized by uneven sampling, partial observation, and raggedness, are common in many applications", "questions": "Can authors explicitly define how it distinguishes natural irregularity from artificial irregularity ?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper focuses on long-standing pain points in irregular time series research such as fragmented tools, lack of standardized benchmarks, and reliance on artificially induced irregularity.  The benchmark design is rigorous and comprehensive. Authors have evaluates a total of twelve methods over 34 datasets.", "weaknesses": "The paper studies exclusively on classification tasks and excludes other important time series tasks  such as forecasting, anomaly detection from benchmarking. While the paper mentions potential extensions, authors did not provide any technical details or preliminary results for these tasks.  Despite emphasizing practicality, authors provide incomplete details on computational ciost while it reports training/inference delay for classifiers, this paper did not analyze how the framework scales with increasing dataset.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "iU2W6SbtWp", "reviewer": "Reviewer_CxTm", "summary": "This paper introduces pyrregular, a unified framework designed to standardize the representation, analysis, and benchmarking of irregular time series (ITS) data. It proposes a clear taxonomy of irregularity types, implements an interoperable array structure that combines the flexibility of xarray with the memory efficiency of sparse COO tensors, and compiles a large repository of 34 naturally irregular datasets. The authors conduct a comprehensive evaluation of 12 classifiers spanning diverse modeling paradigms (including statistical, neural, and differential-equation-based approaches), offering the first systematic and reproducible benchmark for ITS classification. The work fills an important gap by centralizing disparate research efforts on irregular time series and providing a standardized, extensible foundation for future studies.", "questions": "see above", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper clearly identifies a major gap in the field: the lack of interoperable tools and standardized benchmarks for irregular time series classification, which has long hindered cross-domain reproducibility and comparison.\n\n2. The proposed array format elegantly combines xarray and sparse COO representations to achieve both flexibility and memory efficiency, while supporting multiple types of irregularity (uneven sampling, partial observation, raggedness).\n\n3. The authors assemble a substantial suite of 34 real-world ITS datasets and evaluate 12 state-of-the-art models across multiple irregularity types, dataset scales, and sequence characteristics. The empirical analysis is thorough and yields valuable insights into the comparative strengths of classical versus deep models for ITS classification.\n\n4. The framework, benchmark design, and dataset curation will likely become an important community resource for reproducible research and future extensions beyond classification.", "weaknesses": "1. While the benchmark covers a diverse set of classical and neural classifiers, recent developments in foundation or LLM-based time-series models (e.g., Time-LLM, CALF, or multimodal pretraining frameworks) are not discussed. Including such models, even conceptually, could contextualize where pyrregular fits within the broader trend toward generalist temporal modeling.\n\n2. The paper briefly mentions runtime comparisons, but a deeper discussion of computational efficiency, scalability with data size, and memory footprint across model classes would strengthen the benchmarking narrative—especially given that one of pyrregular’s core motivations is interoperability and resource efficiency.\n\n3. The paper focuses exclusively on classification, leaving forecasting, anomaly detection, and imputation for future work. A brief demonstration or pilot benchmark on another task could have further illustrated the generality of the proposed framework.\n\n4.  For complex datasets such as MIMIC-III or PhysioNet 2019, a more in-depth analysis of model behavior and domain-specific challenges (e.g., handling clinical missingness patterns or label imbalance) would make the study more informative for practitioners in healthcare and other applied domains.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "OAWJfN5CYe", "reviewer": "Reviewer_r4xR", "summary": "This paper introduces a unified preprocessing framework for irregular time series (ITS). The framework standardizes common steps and ships with curated datasets and baseline implementations. According to the paper, the pipeline supports multiple ITS tasks and provides a consistent and computationally efficient interface that lowers the barrier to reproducing baselines and evaluating new models across diverse benchmarks.", "questions": "+ As the MIMIC-III is available only to credentialed users, does the framework preprocessing code support the whole dataset?\n+ What determined the models chosen for inclusion? Were there any barriers to adding existing models from other families?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "+ It handles a real problem in ITS learning research, despite not being a traditional ICLR paper\n+ The framework covers multiple ITS settings, improving comparability across papers\n+ The proposed pipeline to Raw ITS into model-ready tensors can accelerate experimentation and deployment", "weaknesses": "+ As the main contribution is the framework itself, the paper could be more focused on how it was implemented instead of evaluating the included models.\n+ As it was proposed for unified ITS, some relevant families seem absent from the main implementation/benchmark (e.g., latent ODE[1]/RNN variants beyond NCDE, state-space models/SSMs, and foundation models as TabPFN[2]) despite being used for ITS with relevant results.\n+ TIMESNET is reported as a transformer-based model, but it is not based on the attention mechanism.\n\n\n[1] Rubanova, Yulia, Ricky TQ Chen, and David K. Duvenaud. \"Latent ordinary differential equations for irregularly-sampled time series.\" Advances in neural information processing systems 32 (2019).\n\n[2] Hollmann, Noah, et al. \"Tabpfn: A transformer that solves small tabular classification problems in a second.\" arXiv preprint arXiv:2207.01848 (2022).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)