TimePoint: Accelerated Time Series Alignment via Self-Supervised Keypoint and Descriptor Learning
Authors: Ron Shapira Weber
Shahar benishay
Andrey Lavrinenko
Shahaf E. Finder
Oren Freifeld
Venue: ICML 2025 poster (2025)
Keywords: Time series
alignment
dtw
dynamic time warping
Primary area: applications->time_series
TL;DR: We present TimePoint, a self-supervised keypoints detection and descriptor Learning of time series

Abstract: Fast and scalable alignment of time series is a fundamental challenge in many domains. The standard solution, Dynamic Time Warping (DTW), struggles with poor scalability and sensitivity to noise. We introduce TimePoint, a self-supervised method that dramatically accelerates DTW-based alignment while typically improving alignment accuracy by learning keypoints and descriptors from synthetic data. Inspired by 2D keypoint detection but carefully adapted to the unique challenges of 1D signals, TimePoint leverages efficient 1D diffeomorphisms, which effectively model nonlinear time warping,  to generate realistic training data. This adaptation, along with fully convolutional and wavelet convolutional architectures, enables the extraction of informative keypoints and descriptors. Applying DTW to these sparse representations yields major speedups and typically higher alignment accuracy than standard DTW applied to the full signals. Despite being trained solely on synthetic data, TimePoint generalizes well to real-world time series. Extensive experiments demonstrate that TimePoint consistently achieves faster and more accurate alignments than standard DTW, making it a scalable solution for time-series analysis. Our code is available at https://github.com/
BGU-CS-VIL/TimePoint.

TimePoint: Accelerated Time Series Alignment via Self-Supervised Keypoint and Descriptor Learning
Ron Shapira Weber 12 Shahar Ben Ishay 12 Andrey Lavrinenko 12
Shahaf E. Finder 12 Oren Freifeld 2
Abstract
Time series with Predicted Keypoints
Signal
Fast and scalable alignment of time series is fundamental challenge in many domains. The standard solution, Dynamic Time Warping (DTW), struggles with poor scalability and sensitivity to noise. We introduce TimePoint, a sule-supe method that dramatically accelerates DTW-bbased alignment while typically improving alignment accuracy by learning keypoints and descriptors from synthetic data. Inspired by 2D keypoint detection but carefully adapted to the unique chal- lenges of ID signals, TimePoint leverages ef- ficient ID diffeonmorphisms-wwiii which effectively model nonlinear time warping-to generate real- istic training data. This approach, along with fully convolutional and wavelet convolutional architec- tures, enables the extraction of informative key- points and descriptors. Applying DTW to these sparse representations yields major speedups and typically higher alignment accuracy than stan- dard DTW applied to the full signals. TimePoint demonstrates strong generalization to time series when trained solely on synthetic data, and further improves with fine-tuning real data. Extensive experiments demonstrate that Time- Point consistently achieves faster and more ac- curate alignments than standard DTW, making it a scalable solution for time-series analysis. Our code is available at https: : / /////ttttuu com/ BGU- CS- VIL/TimePoint.
Amplituue
Predicted Seypoints
Keypoint Probabilty
Koypont probabirty
Threshold Keypoint Probability
Descripters
peccreters PGA
Time
Figure 1. TimePoint (TP): Keypoint Detection and Deseriptors on real-world, unseen, ECG data of length 2500 (TP was trained on synthetic data of length 512). Each panel depicts ---ptto-ottom) the original signal and predicted keypoints. keypoint probability map, and PCA principal components for visualization purposes).
learned descriptors (D 256, using
ordered sequences of observations collected over time and are instrumental in revealing temporal patterns, trends, and anomalies. A key challenge arises when these sequences grow in length or vary in sampling rates: not only do com- putational costs escalate with sequence size, but subtle mis- alignments arise due variable speeds temporal distortions in the data. Consequently, developing robust and efficient approaches for comparing and aligning time series has become essential for tasks such as classification, clustering, and anomaly detection.
1. Introduction
Time series data are ubiquitous across finance, healthcare, environmental monitoring, and engineering. They consist of
Dynamic Time Warping (DTW) is one of the most widely used algorithms for time series alignment due its ability to accommodate elastic shifts in the temporal axis (Sakoe, 1971; Sakoe & Chiba, 1978). It has found broad application in speech recognition, gesture analysis, and signature veri- fication. Despite its flexibility, DTW suffers from several practical limitations. First, its computational cost is (LL)) with respect to the sequence lengths L, which becomes prohibitive for large-scale high-throughhur applications.
Equual contribution 'Dpaarment of Computer Science. Ben- Gurion University of the Negev (BGU). "Data Science Research Center, BGU, "School Brain sciences Cognition, BGU, Cor- respondence to: Ron Shapira Weber <ronsha@ post. bgu.a.gu..c..l..
Proceedings of the nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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TimePoint
Second, DTW is sensitive noise abrupt time distortions, potentially yielding suboptimal alignments under significant temporal variations amplitude noise.
In computer vision, keypoint (KP) detection and descrip- tion techniques, such as SIFT (Lowe, 1999) and Super- Point (DeTone et al., 2018), are foundational for tasks like image registration, object recognition, and 3D recon- struction. These methods identify KPs and compute corre- sponding descriptors that facilitate robust matching. Super- Point (SP), in particular, leverages self- self-supervised learning scheme to jointly detect KPs and generate discriminative descriptors. Due to a limited large-scale dataset with known KPs, SP is first pre-trained on a synthetic dataset and later fine-tuned on weal-world data.
(a) DTW alignment path on dense data
However, an equivalent methodology for one-dimensional (ID) time series data remains largely unexplored. This gap arises from several unique challenges: time series often ex- hibit more complex and variable transformations (more than, e.g., homographies in images), suitable synthetic data with labeled KPs are not readily available, and existing models must handle arbitrarily long sequences without exploding in parameter count or computational cost.
(b) DTW alignment on sparse TP features
Figure 2. Comparison of DTW alignment using the raw sequence (top) or TimePoint keypoints and descriptors (Bottom). The black and blue boxes highlight areas where sparse DTW using TP de- scriptors results in better matching.
We introduce TimePoint, self-supervised method for KP detection and description in time series. Inspired by Su- perPoint (DeTone et al., 2018), we adapt KP learning to ID signals by modeling nonlinear temporal misalignments using Continuous Piecewise Affine Based (CPAB) trans- formations (Freifeld et al., 2017). To enable training, we generate synthetic time series with known KPs and apply CPAB warps to produce paired examples with ground-truth correspondences. This framework also supports fine-tuning on real-world data without architectural changes. Once trained, TimePoint extracts sparse KPs and descriptors that allow DTW to operate on salient locations only, signif- icantly reducing runtime while improving robustness to noise. The model architecture fully convolutional, com- bining standard and wavelet-based convolutions (Finder et al., 2024), and scales efficiently to variable-length inputs, unlike transformer- based methods, which incur quadratic cost in sequence length. Figure shows TomePoint applied to a long ECG signal (L- = 2500) (Dau et al., 2019). Despite being trained solely on synthetic data of length T. 512, TimePoint accurately detects salient locations and computes meaningful descriptors. To visualize these 256-dimensional features, we reduce the dimension via PCA to 5, providing an interpretable representation of the learned embeddings.
design a synthetic time series dataset (SynthA) 1 in) with known KPs and apply CPAB warps to generate training pairs with ground-truth correspondences.
Efficient Multiscale Network Architectures: We adapt the recently proposed WTCCov architecture for ID signals, maintaining a constant parameter count re- gardless of the sequence length, enabling fast, scalable training and inference.
Fast and Sparse DTW Alignment: We demonstrate that DTW performed on learned keypoints and descrip- tors yields more accurate and efficient alignment at a lower computational cost.
2. Related Work
Dynamic Time Warping (DTW) was introduced by Sakoe (Sakoe, 1971) and later refined in the seminal work by Sakoe and Chiba (Sakoe & Chiba, 1978). It is a clas- sie method for aligning time series that differ in speed or phase by nonlinearly warping the temporal axis. Despite its widespread usage, DTW suffers from a quadratic time and memory complexity, making it challenging to apply scale. Moreover, DTW can be overly sensitive to noise, as it attempts to align every point in both sequences. These limitations underscore the need for methods that reduce com- putational complexity without sacrificing accuracy, such as the sparse Kasba alignment framework we propose in
Our contributions are as follows:
A 1D Keypoint Detection and Description Frame- work: We introduce TimePoint, a supe-supe KP detection and description method for time series data.
a Synthetic Dataset for Time Series Alignment: We
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TimePoint
this paper. Figure 2 illustrates the difference between dense DTW alignment and the sparse KP matching approach in- troduced by TimePoint.
man al., 2021; Martinez et al., 2022; Weber & Freifeld, 2023; 2025), the goal is to predict CPAB warps for time series averaging. TAP (Su & Wen, 2022) aims to predict the optimal alignment between time series pairs in super- vised manner but ignores the order-preserving quality of most alignment algorithms. Deep declarative DTW (Xu et al., 2023) predicts the alignment in an end-to-end man- ner but with increased complexity. Warpformer (Zhang et al., 2023) generates alignment paths between irregularly sampled time series. In the domain of few-shot action recognition, key works such as DeepCTW (Trigeorgis et al., 2016), OTAM (Cao et al., 2020), TTAN (Li et al., 2022), and TCCL (Dwibedi et al., 2019) focus on learning repre- sentations from videos using temporal alignment. However, these methods do not produce descriptors or detect KPs.
Several variants aim to mitigate DTW's limitations. Soft- DTW (Cuturi & Blondel, 2017) replaces the hard-minimum operator with a differentiable approximation, facilitat- ing gradient-based optimization. However, it retains the core quadratic complexity in both computational time and memory due to the large cost matrices and gradients. ShapeDTW (Zhao & Itti, 2018) 8) incorporates local shape descriptors into the aligmment process, yielding better match- ing of local patterns but introducing additional compu- tational overhead. DTW with Global Invariances (GI- DTW) (Vayer et al., 2020) handles global scaling and offset differences, yet remains bounded by quadratic cost. Fast- DTW (Salvador & Chan, 2007) approximates the standard DTW algorithm in linear time at the cost of some accuracy; however, it was shown that under some assumptions, the speed-up could be marginal (Wu & Keogh, 2020). While we do not explore this in our experiments, it is worth noting that the use of TimePoint's KPs and descriptors could poten- tially accelerate FasDDWW further by operating on sparse representation of the data, similarly how TimePoint ac- celerates DTW by focusing alignment sparse subset of KPs rather than the entire sequence.
Summary. In contrast to existing work, TimePoint ex- plicitly detects KPs and learns discriminative descriptors for alignment using only synthetic data (while results even further improve upon fine tuning on real data). Unlike traditional methods that are computationally expensive or deep learning approaches that rely on approximations or supervision, TimePoint restricts DTW a small set of KPs and meaningful descriptors, significantly reducing computa- tional overhead while often improving alignment accuracy.
In contrast to all these methods, TimePoint explicitly de- teets KPs and descriptors, enabling reduced dimensionality for the alignment problem while achieving accurate and scalable performance.
3. SynthAlign: Synthetic Data Generation for Keypoints Detection and Matching
In this section we present Synt hal hnnggn, synthetic time series and KP generator designed with the goal of facilitating self-supervised KPs detection and descriptors learning (see also Figure 3, left). This includes the data generation, KP annotation process, and augmentation strategies.
CPAB Transformations for modeling time warping. Mod- eling the nonlinear temporal distortions that time series of- ten exhibit is a non-trivial task. Unlike image pairs, which can often be modeled via homographies, there is no gold- standard transformation family for time series. While it is well understood that diffeomorphisms are a natural choice to model time warping (Mumford & Desolneux, 2010), the associated computational difficulties historically hindered this approach. Fortunately, Continuous Piecewise Affine Based (CPAB) transformations (Freifeld et al., 2015; 2017) provide a flexible and efficient way to parameterize diffeo- morphisms. In ID, CPAB transformations were used in deep learning for constructing activation functions (Chelly et al., 2024; Mantri et al., 2024), multi-task fine-tuning (Mantri et al., 2025), and, most relevant in our context, modeling time warping (Weber et al., 2019; Martinez et al., 2022; Weber & Freifeld, 2023). For example, the Diffeomorphic Temporal Alignment Network (DTAN) (Weber et al., 2019) used CPAB for weakly supervised time series averaging. However, DTAN does not provide descriptors or KP detec- tion for its inputs and requires class labels during training.
3.1. Challenges of Keypoint Detection in 1D Signals
While TimePoint draws inspiration from the 2D KP detector SuperPoint (DeToone et al., 2018), adapting its framework to 1D signals introduces unique challenges. First, in 2D images, KP detection and description often leverage well- defined local patches and low-dimensional transformation family (e.g.. homographies). Time series, however, fre- quently exhibit significant nonlinear distortions, including varying speeds and local stretching or compression, which cannot be well approximated by a low-dimensional trans- formation family. Second, amplitude variations, caused by noise or changes in sampling rates, can obscure salient events and complicate the task of identifying KPs. Taken to- gether, these challenges complicate the creation of synthetic data of ID signals, for training a model for KP detection and description (note this is a different task from generating ID synthetic data for, say, training forecasting models).
Deep Learning for Time Series Alignment. In the afore- mentioned DTAN and its variants (Weber et al., 2019; Kauf-
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TimePoint
Generation
Training: Synthetic Data
Inference: Real-world Data
With known correspondense
Contrastive Loss (using known comeesonndnne))
DTW matching using keypolnns descriptora
Inpure
Keypoints Loss
Keypoints Loss
CPAB
Augimentation
Time
Keypoint Decoder
Descriptor Decoder
Descriptor Decader
Keypoint Decoder
Input
TimePaint
TimePoint
Encoder
Encoder
SynthAlign
Synthetic
Data Generator
Input
Input B
Input
Input
Figure 3. Training and Inference overview, Left: signals and keypoints are synthetically generated and augmented using CPAB warps (Section 3). Middle: TimePoint predicts KP location and descriptors using the known correspondence (Section 4). Right: real-world. unseen data pairs are matched using DTW on TimePoint descriptors at keypoint locations.
3.2. Generation of Synthetic Data
We generate synthetic signals with known KPs and aug- ment them such that the correspondences between original and augmented signal pairs are known (see subsection 3.3). These synthetic data pairs provide a controlled environ- ment for the model to learn fundamental temporal features (descriptors) and KP detection. We generate large-scale synthetic dataset, named SynthAlign, by composing pat- terns and trends from a pre-defined bank of signal types. Unlike previous works in time series representation learn- ing that synthesize signals for data augmentation (Fu et al., 2024; Ansari et al.. 2024), our approach focuses specif- ically on generating patterns that are useful for time se- ries alignment and also includes explicit KP generation. SynthAlis in consists of the following pattern types:
UMMAN
Figure 4. Samples from the synthetic dataset SynthAlign.
Sine Wave Composition: A combination of sine waves with varying frequencies and amplitudes to sim- ulate oscillatory patterns.
augmentations allows the model to handle real-world varia- tions effectively. Samples from SynthA ig are shown in Figure 4 (see more details in Appendix B).
Block, Triangle, and Sawtooth Waves: Signals with square, triangular, or sawtooth waveforms represent abrupt changes linear ramps.
3.3. Generation of Correspondences Using CPAB Warps
Radial Basis Functions (RBF): Mixtures of Gaussian blobs to model localized smooth events.
As stated in (DeTone et al., 2018), "a homography is good model for what happens when the same 3D point is seen from different viewpoints". Adopting a similar approach time series, requires family of transforma- tions, in ID, that provide a good model for nonlinear time warping. With this in mind, tions (Freifeld et al., 2017) to simulate nonlinear temporal distortions and generate correspondences between original and transformed signals. CPAB transformations are para- metric, highly-expressee and computationally-efiin dif- feomorphisms (a diffeomorphism an invertible map with
KPs for each pattern are derived from salient features, in- cluding pattern start and end points, peaks, and derivative zero crossings. We denote X E L and Y = (y))... with E {0, 1). as the synthetic signal and its KPs respectively (where L = 512). To further diversify the data, we superim- pose linear trends to emulate non-stationary behavior, ran- domly flip sections or entire signals, and add Gaussian noise (jitter) < (00 0.1). This suite of pattern composition and
the CPAB transforma-

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TimePoint
CPA-based Transformation
Signal and Warped Signal
and two decoders: one specialized for KP detection and the other for descriptor computation. By leveraging a fully convolutional network with Wavelet Transform Convolution (WTConv) layers, TimePoint efficiently captures multiscale temporal features. This design maintains a fixed number of parameters, regardless of the input's length, L, ensuring scalability to long sequences.
Original signal
0.2
Warped signal
0.6
9.4
02
Ideatity
CTMB tranedorm
Centinuous Piecewise Velccity Bold
-3.0
4.1. Shared Encoder
-25
The shared encoder processes the input time series X e RCLL where C. is the number of channels and L is the signal length. In this work, we focus on the univariate case (C 1), with multivariate data left for future ex- ploration. We employ the recently proposed WTConv layer (Finder et al., 2024), which operates in the wavelet domain to capture patterns at multiple scales. Using 3- level wavelet decomposition with kernel size 3, the WT- Conv layers efficiently learn both low-frequency (global) and high-frequency (local) features. Each WTConv layer followed by batch normalization. A stride of is applied be- tween each of the 3 WTConv blocks, resulting in overall downsampling factor of 8.
0.0
300
7.5
16.0
12.
20
100
1.50
Figure 5. Generating signals and keypoints pairs with known corre- spondences using a CPAB transformation 7 which was obtained from CPA velocity field, v proposed in (Freifeld et al., 2017) and briefly explained in our Appendix.
differentiable inverse). Briefly, CPAB transformation is ob- tained by integrating Continuous Piecewise Affine (CPA) velocity field. The term "Piecewise Affine" is w.r.t. some partition of the domain (as shown in Figure 5). While the full details behind CPAB transformations. - which are available in (Freifeld et al., 2017) - are inessential for understanding our paper, Appendix C contains the key details of how a CPAB transformation is defined and built.
The encoder produces feature map F E x where Dene is the feature dimension, and L' represents the length after downsampling. This feature map forms the basis for subsequent KP detection and descriptor generation.
Given an original signal X, we generate a warped signal X' = X o T where 7 is a CPAB transformation param- eterized by 0. The known transformation provides ground truth correspondences between X and X', enabling super- vised learning for KP detection and descriptor matching.
4.2. Keypoint Decoder
The keypoint decoder processes the feature map E RR (Denc 256 in our experiments) produced by the shared en- coder to predict keypoint scores for each temporal location. It consists of a convolutional layer that refines the features for KP detection and maps RR X + 8 (analogous to the 'cell size' of size 8 in (DeTone et al., 2018)). The cells are then reshaped from 8 x back to L followed by sigmoid activation function. The output a score vector S (st) with 8, E [0, 1]. where each entry represents the probability of a KP time step f. Non-Maximum Sup- pression (NMS) is then applied with window size of 5 to suppress redundant detections. KPs are selected by ei- ther applying a pre-defined threshold or choosing the top- timesteps with the highest probability.
Not all diffeomorphic transformations yield realistic tempo- ral distortions. To ensure plausible time warping, sample 0 from (0, ECPA) the zero-mean Gaussian smoothness prior from (Freifeld et al., 2017). That prior, over CPA velocity fields, penalizes large or abrupt deformations. Its covariance matrix, ECPA, has two syper-parameteer Vvar and smoothh, which govern the variance and smoothness, respectively. of the CPA velocity field, hence also of the and Ovar 0.5 imposes mild constraints on the velocity fields, favoring nearly affine local segments without overly restrict- ing their overall variability. In our experiments, we partition the domain into 16 segments (so, due to eero-boundary con- ditions. d0 = 15; see (Freifeld et al., 2017)) to balance flexibility and simplicity in warping.
resulting CPAB transformation. Setting Osmoath
4.3. Descriptor Decoder
The descriptor decoder also operates on F' but is tasked with generating a descriptor vector for each time step. A convolutional layer first maps F into a descriptor space (Daesc 256), after which an upsampling operator restores the temporal dimension from L' back to L. We then apply lg normalization that each descriptor lies on the unit hy- persphere. The resulting output, F E R Dauce XL serves
4. TimePoint Architecture
We now describe TimePoint's overall architecture and its components, followed by the loss functions. The entire training and inference pipeline is illustrated in Figure 3. The architecture, detailed Figure 6, consists of shared encoder
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TimePoint
Keypoint Proh. Map
Descriplors
Descriptor Loss. Let N < L be the number of ground- truth KPs from the original and warped signals, Y and Y'. We define set of matched indices G S ......,NJ x {1...., . where (i.j) E G if and only if the i-th keypoint in Y corresponds to the j-th keypoint in Y' under the known transformation T Denoting the descriptors at these KPs (D.) i=1 and AV j-1 trastive loss only over these KP descriptors. Specifically, for each pair (i, we treat it positive (matching) pair if (i,5) E G, and negative pair otherwise. The loss is then
Sigmaid L).L)
CaNomalize (B, Dew: L)
Keypoint Decoder
Descriptor Decoder
Reshape (B, L)
Upsample (factor=8)
compute margin-based con-
ConvBlockID (B,8, L/8)
Con-BlockID (B, Dasse L/8)
Encoder Output (B, D L/8)
Casse DD)=))=
N N
Encoder
1c((1,))) max(0, mp cos(s Di, D))))
WTConvBBockII x3 (stride=2)
N
+ (1 15 ((i,)))) max(0, (D,, D2) - -mn) | (2)
ConvBlockID (stride=1)
DJ D2 is the cosine similarity and 1g)( is the indicator function: namely, (g((i,)) = if (i. E 9 and 0 otherwise. We set the positive margin my 1 to push matched pairs toward maximal similarity, and a negative margin m, = 0.1 to separate non-mater non-matching pairs. Since N < L. computing the descriptor loss solely at KP locations substantially reduces memory usage and focuses the training on salient regions of the signal.
where cos (Di, D2) Iلb
Inpul
(B, L)
Figure TimePoint model architecture.
as a dense descriptor matrix from which we extract a sparse set of descriptors at the KP locations identified by the de- coder above. This results in a compact representation for alignment tasks.
Overall Loss. The overall loss combines the detection and descriptor losses across the original and warped signals:
4.4. Loss Functions
C(S, Y,Y, D,D'
We train TimePoint self-supervised manner by sam- pling synthetic signals and KPs (X, YY) from SynthAlis i ign (as described in Section 3), where X is time series and Y its associated keypoint labels. To simulate realistic tem- poral distortions, we generate (X', Y") via a known CPAB transformation o (having sampled 0 from the prior), i.c. X' = X o T and Y' = Y o T This process ensures we have warped signals pairs with ground-truth correspon- dences, facilitating KP detection and descriptor learning.
(s
Lup(S", Y")
+ Cdese (D,D') (3)
kp detection in
kp detection in X"
descriptor matching
4.5. DTW Alignment Using TimePoint
Once TimePoint is trained, its learned KPs and descriptors can be used to perform alignment more efficiently. At test time, given two input signals X E R and X' E R,,, we apply TimePoini to extract their respective KPs and descrip- tor sequences, denoted D R
Lx Daese and D' R L/ xDaese where L < L, L' < L' are the numbers of selected KPs.
Keypoint Detection Loss. Keypoint detection formu- lated as a binary classification task at each time step. Con- sequently, we use a binary cross-entropy loss between the predicted scores S (st)/n, and the ground-truth labels Y (
Instead of aligning the raw signals X and X', we perform DTW directly on the descriptor sequences D and D'. To account for the vector nature of the descriptors, we replace the standard scalar-based Euclidean cost in DTW with a cosine-simillrity-baaee cost:
(S,Y)
1
yo log(s)) + y1) log(1 st)]
cost(Dit), D'((') = = cos Dit), D'(r'),
(4)
where f and E' index the descriptors corresponding to KPs in X and X', respectively.
(1)
Here, ye E {0, 1} indicates whether keypoint is present at time i, and 8t is the predicted probability.
Critically, by aligning only the sparse set of KP descriptors, the computational complexity of DTW is reduced from
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TimePoint
OLL L') to O(T D.). For instance, using 10% of the original signal length yields up x00x speedup, while often improving alignment accuracy due to the robustness of the learned features.
Euclidean 11.515
1.706 DTW(1.0)
ShapeDTWidew) 8,471
5.652 T2 DTW.0.2)
ShapeDTWWhog) 7.902
6.128 T2 + DTW(0.5)
SoRDTW((=0.11 7,015
6.245 DTW
SchDTW(y=10) 6.936
DTW-GI
SoftDWW(y=1) 6.853
6.549 TP DTW(0.3)
6.74 ShapeDTWiraw)
4.6. Fine-TTuning on Real-World Time Series
While SynthAlign enables TimePoint to learn KP de- tection and descriptors in fully synthetic setting. there may still be a distribution gap when applying the model to world signals. To further improve generalization, we fine-tune TimePoint directly on real data from the UCR archive (Dau al., 2019), using a similar sulf--upe protocol. We simulate temporal distortions by applying two independently sampled CPAB transformations to each signal,X, yielding two warped views Xi = X T and X2 = X T Generating two augmented versions also helps to mitigate overfitting (which not necessary when data is generated on-the-fly fly in SynthAlign) Since both originate from the same source X, we know the ground- truth correspondence between any point in X₃ and Xg.
Figure 7. Critical Difference Diagram. The scores represent the av- erage rank (I-NN Acc.) of each method across 102 UCR datasets. TimePoint (TP) was trained solely on synthetic data.
Conv layer from the official implementation (Finder et al., 2024) to ID inputs. SynthAl in synthetic data genera- tion occurs on-the-fly, such that no example is seen twice. Training is performed on single NVIDIA RTX6000 GPU with 48 GB of memory. The model converges within ap- proximately 100,000 iterations and 20 hours, with batch size of 512, and the Adam optimizer (Loshchilov, 2017) with a learning rate of 1 x 10- with cosine learning rate scheduler. The encoder consists of layers with a number of kernels = [128, 128, 256, 256].
As no ground-truth KPs are available in real datasets, we adopt the same heuristic strategy used in SynthAlign we mark as KPs locations of local extrema (minima and maxima), derivative zero-crossings, etc. While it is less effective on real-world data, we notice that the produced KPs are akin to training with 'noisy labels", contributing to TP's generalization and robustness.
To fine-tune tune TP, we first train our model as detailed above on SynthAl gn. Next, we train TP 100 UCR datasets for 2000 epochs. We resample each signal to L = 512 and follow the procedure described in subsection 4.6.
To facilitate fast evaluation of DTW k-Nearest Neighbors (kNN), we implement DTW using pytorch and perform batch-wise KNN. For SoftDTW (Cuturi & Blondel, 2017) we use the CUDA implementation' Since the CUDA version of SoftDTW is limited to maximum length of 1024, using fewer timesteps not only reduces computation time and RAM consumption but also allows for processing of much longer sequences by using only the selected KPs.
5. Limitations
Although our synthetic data generation facilitates KPs and descriptors learning that can be easily transferred to real- world data in many scenarios, TP's performance might be sub-optimal if signals deviate substantially from the syn- thetic distribution (however, this can be mitigated by fine- tuning TP). Moreover, if the underlying temporal distortions exceed the scope of the predefined CPAB prior, it might require further adjustments. Finally, our encoder downsam- ples each time series by a factor of 8 to enhance efficiency. While beneficial for long signals, this fixed rate might overly compress shorter sequences.
6.2. Classification on Real-Worke Data
TP was trained on synthetic data using the SynthAl 1 in dataset (i.c., without the fine-tuning step). We evaluate its generalization to real-world data using the UCR Time Series Archive (Dau et al., 2019). The archive has 128 datasets with inter-dataset variability the number of samples, length, application domain, and more. We use the original train-test splits provided by the archive. Thus, the results might differ from the ones reported by the original SoftDTW (Cuturi & Blondel, 2017) since they have shuffled the splits and pro- duced new ones. We use a subset of 102 datasets, omitting ones that did not produce results for all methods (c.g., when one or more of our competitors' runtime exceeded 12 hours or due to data handling issues).
6. Experiments and Results
We evaluate TimePoint across a range of experiments that assess the computational efficiency, robustness to noise, and classification accuracy on real-world data, An ablation study further analyzes the contribution of each component.
6.1. Implementation Details
Our model, implemented in PyTorch, has total of 200K trainable parameters. We have adopted the 2D WT-

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TimePoint
Table 1. Comparison of DTW and SoftDTW on 102 UCR datasets w/o TimePoint at various KP percentages. Top: I-NN classification accuracy. Bottom: total runtime in GPU hours.
Table 2. I-NN classification accuracy under various perturbations. Results averaged over 30 UCR datasets.
No
Blur
Blur
Jitter
Jitter
Method
Noise
o 0.1
1.0
a 0.1
a 0.5
Method
TimePoint I-NN Accuracy
DTW (raw)
0.844
0.843
0.838
0,801
0,744
Raseline
10%
50%
100%
(10%)
0.867
0.866
0.853
0.804 0.828
0.760
TP (20%)
0.881
0.873
0.873
0.791
DTW
0,706
0.707
0.721
0.710
0,732
+ fie-tuning
0.706
0.777
0.790
0.769
0.80
SoftyTWey = 0.1)
0,677
0.639
0.662
0.689
0.720
tnning
0.577
0.724
0.712
0.729
0.752
SonDTWWy
0.671
0.659
0.711
report the results for fine-tuning TP. The results show that both methods enjoy significant improvement in both met- rics. Comparing DTW with TP+DTW, using 20% of the KPs yields 2% accuracy gain with a x 65 speedup (192 vs. 3 hours respectively). For SoftDTW, using 10% or 20% of the KPs is not necessarily better, since SoytDTW(r) encour- ages the solution to be smooth across adjacent time steps. However, using 50% of the KPs yields better accuracy (1- 2%) less than half the running time, and the full length gives significant accuracy boost across various y values (4-5%) with almost identical runtimes. Finally, we provide
+ line taning
0.671
0.72
0.711
0.722
0.749
Son = 10)
0,670
0.655
0.688
0.680
0.703
Fine tuning
0.670
0.724
0.729
0.752
Method
GPU Runtime (bours)
Baseline
10%
20%
30%
100%
DTW
192
0.71
2.88
19.59
193
SoftETWWY 0.1)
2.10
0.65
0.30
1.00
2.17
SofttTW/y
1.98
0.52
0.86
2.16
So 10)
1.94
0.50
2.02
A common approach for time series classification and align- ment evaluation is KNN with DTW as the distance measure. As baselines, we include classical DTW, SoftDTW (Cuturi & Blondel, 2017) with 7 E {0.1,1, 10), DTW-GI (Vayer et al., 2020), and ShapeDTW (using 'raw', 'derivative" and 'hogld' descriptors) (Zhao & Itti, 2018), where we rely on the official DTW-GI implementation and sktime for STapeDTTW (Löning et al., 2019). For TP, we evaluate keypoint ratios w.r.t. the sequence length {0.1, 0.2, 0.5, 1}, observing that selecting fewer KPs accelerates DTW align- ment while often preserving improving classification accuracy. We select KPs by sorting the detection confidence and retaining the top K%. Each value corresponds to dif- ferent threshold. This adaptive strategy avoids using fixed threshold across all datasets, which may be sub-optimal.
GPU-RAM consumption analysis in subsection A.3.
To further improve performance, we fine-tune TP on real- world time series using the same self-supervised framework. Fine-tuning leads to a substantial boost in accuracy: com- pared to the synthetic-only model, we observe 7-8% im- provement in performance across KP ratios. Importantly, runtime remains unchanged, as the architecture and infer- ence pipeline are identical, which demonstrates that TP can be adapted to new domains while preserving its computa- tional advantages.
6.4. Runtime Analysis
We evaluate the runtime performance of DTW-KNN on both the raw signal and TimePoini descriptors with varying num- bers of KPs. We compute kNN between two synthetic datasets, each of size N 500, with sequence lengths L E (50, 100, ..., 10003. The results are shown in Fig- ure 8. When using the full signal (7. = 100%), the runtime for DTW and TP+DTW is almost identical. However, as the number of KPs in TP decreases, the runtime for TP+DTW scales significantly better with L, demonstrating near-linear behavior for long sequences. For instance, when L 1000, using 20% of the KPs is almost two orders of magnitude faster than performing DTW on the entire length. This in- dicates that TP's ability to focus on reduced set of the KPs leads to substantial efficiency gains, particularly for large-scale, long sequence datasets.
We summarize performance using a critical difference dia- gram (Demšar, 2006; Middlchurst al., 2024), which ranks each KNN classifter across multiple datasets (the full re- sults appear in Appendix E). Classifiers are grouped into "cliques," connected by a horizontal line, if their average ranks are not significantly different according to pairwise one-sided Wilcoxon signed-rank tests with Holm correc- tion (Middlehurst et al., 2024). As illustrated in Figure 7, TP+DTW achieves the highest average rank (with statis- tical significance) at both 100% and 20% keypoint usage, and remains highly ranked at 10% and 50%. Despite be- ing trained exclusively on synthetic data, TP demonstrates strong zero-shot generalization to real-world time series.
6.3. GPU-enabled DTW
6.5. Robustness to Noise
We compare TP+DTW and TP+SoftDTW to the correspond- ing methods using batch-wise KNN on the GPU. Table shows the average accuracy and total runtime across the datasets. The reported runtime includes TP's forward pass, sorting KPs by probability, NMS, and DTW. Here, we also
To assess TP's robustness under noisy and distorted condi- tions, we conducted a controlled evaluation using a subset of 30 datasets from the UCR archive. We introduced two common types of perturbations: additive Gaussian noise
8

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TimePoint
Runtime Analysis
OTW
Table 3. Ablation Study
10
DIW TP3 ETNW
I-NN
T711 0) 6TNY
DTW (baseline)
Euclidean 100%
0.797
Runtime (og(sec)
10"
TP + Dense Conv
Fuclidean
100%
0.82
TP W WTCCovv
Euclidean
100%
0.815
TP' + Dense Conv
Cosine Sim.
100%
0.835
10a
WTCCov
Cosine
0.869
TP + DTW (no descriptors)
Euclidean
20%
0.792
TP + Dense Conv
Cosine Simn.
20%
0.826
104
TP WTCCovv
Cosine Sim.
20%
0.841
TP + WTCCny
Cosine Sim.
20%
0.865
109
50
100
150
200
300
500
75.0
1000
7. Conclusion
Signal Length
We introduced TimePoint (TP), self-supervised framework for efficient time-series alignment. By leveraging synthetic data and employing CPAB transformations, TP learns to detect KPs and descriptors that enable sparse and accurate alignments. Our approach addresses the scalability limita- tion of traditional methods like DTW, achieving significant computational speedups and improved alignment accuracy. Extensive experiments demonstrate that TP generalizes well across diverse real-world datasets, underscoring its effec- tiveness as a practical solution for time-series analysis.
Figure 8. Runtime analysis. DTW KNN runtime between two synthetic datasets of N 500 and varying lengths (on GPU).
(jitter) and Gaussian blur, each applied at two intensity lev- els. For each condition, repeated the experiment three times to account for randomness in the perturbations and report the average I-NN classification accuracy using DTW on raw signals and TimePoint descriptors. We evaluate TP at two keypoint selection ratios: 10% and 20%. The results are presented at Table and indicate that TP is robust to varying noise types.
Acknowledgments
This work was supported by the Lynn and William Frankel Center at BGU CS, by the Israeli Council for Higher Edu- cation via the BGU Data Science Research Center, and by Israel Science Foundation Personal Grant #360/21. S.E.F.ss work was supported by the BGU's Hi-Tech Scholarship. S.E.F'ss and R.S.W.'s work was also supported by the Kreit- man School of Advanced Graduate Studies.
6.6. Ablation Study
Table 3 summarizes an ablation study over 27 datasets using a I-NN accuracy metric. With the full sequence, the stan- dard DTW (baseline) achieves an accuracy of 0.797. Per- forming DTW with TP features improves performance for both Dense Conv (~400K parameters, 0.82) and WTConv (~2000 parameters, 0.815) encoders when using Euclidean distance. Switching to cosine similarity further boosts per- formance to 0.835 for Dense Conv and 0.869 for WTCouv, highlighting the effectiveness of the wavelet-based encoder. Reducing the signal to just 20% of its length yields com- parable trends: first, using TP merely as subsampling (i.e., ignoring the descriptors and using DTW the raw signals restricted to TimePoint's s KPs) gives I-NN accuracy of 0.792, while switching to using TP's descriptors, with ei- ther the Dense Conv or WTCCnv encoders, reaches 0.826 or 0.865, respectively. This shows that TimePoin! differs from naive subsampling methods by not only learning an input-dependent KP detection scheme tailored alignment but also providing descriptors that, crucially, capture local context through a large receptive field. This leads to efficient DTW alignment without sacrificing accuracy. Note also TP+WTCC WTCony nearly matches in accuracy the full- length setting even though far fewer KPs are used. Lastly, dropping the NMS decreases TP+WTConv to 0.841.
Impact Statement
This paper presents work whose goal is to advance the field of Machine Learning. There are many potential societal consequences of our work, none which feel must be specifically highlighted here.
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TimePoint: Appendix
Table of Contents
Appendix A: Additional Results.
Additional figures depicting TimePoint keypoints and descriptors for signals of varying lengths, 1-NN classification comparisons against other DT'W-based methods, and a GPU RAM consumption analysis.
Appendix B: SynthAlign.
Details on synthetic dataset generation process, including waveform composition, keypoint extraction, and the impact of synthetic training on real-world performance.
Appendix C: CPAB Transformations.
Overview of the continuous piecewise-affine -eree-affiie--eeeee based (CPAB) transformations used to generate nonlinear time deforma- tions. Includes ID-specific formulation and illustrative examples.
Appendix D: TimePoint Architecture Details.
Description of the TimePoint encoder-decoder architecture, including layer specifications and parameter choices.
Appendix E: Full UCR Archive Results.
Additional information on the UCR datasets used in our experiments, including train/test splits, signal lengths, and data types. Complete accuracy tables for our method and competitor methods across the entire UCR dataset collection.
12

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TimePoint
A. Additional Results
A.I. TimePoint Keypoints and Descriptors for Time Series Different Lengths
with Predicted Koypoints
Time Predicted
Signal
Sigal
Peyd
Amplitude
Predicted
Keypeitt hobabiity
Beypont hobebilt
Thrsshoi
Movpeint protani
Theshold
Rospoit hubdbiky
Seyport Pabbbiiity
Desoriaton PCA
15c
200
100
250
(a) TimePoint keypoints and descriptors for the Coffee dataset (L 286)
Time with Predicted Keypoints
Time Predicted
Signal Keypoints
Signal Predicted
Ampikodee
Amplitud
Keypoitt hobabiit
Seypont Robebilty
Threshold Kayownt Publllly
Nowwin prolaalty
Threshold Kaypait Pobabikk
Descriatoo FCA
Dusitotors
(b) TimePoint keypoints and descriptors Beet LeFLy dataset 512)
Redirted
Peetined Exyoainn
Keyoaint
Cescriptors
Descc
1250
355
20
125
(c) TimePoint keypoints and descriptors for the Phoneme dataset (L 1024)
13

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TimePoint
A.2. I-NN Classification Comparisons
Sqaal perkeaacee
6.
Ours
Ours
04-
02
02
Blyi performanee
0.2
04
66
10
62
1.0
20
62
04
20
02
04
0.6
DTW
SoftDTW(0.1)
SoftDTW(1)
SoftDTW(10)
10
26
Ours
Ours
0.7
04-
DTWGI
ShapeDTW(raw)
ShapeDTW(hog)
ShapeDTWidev)
Figure A.2. TP with 100% of keypoints vs. several DTW-based methods. Synthetic data training only. Each represents a dataset.
Ours
26
0.6
02-
part TERCO
Equa perforrance
Equal
30
DTW
SoftDDW(0.1)
SoftDTW(11
SoftDTW(10)
len Eipal perkeroeee
66
Ours
Ours
Ours
0.6
04
Eaul
23
65
2/
04
10
66 ShapeDTW(hog)
16
DTWGI
ShapeDTW(raw)
ShapeDTW(dev)
Figure A.3. TP with 20% of the keypoints several DaWebased methods. Synthetic data training only, Each dot represents dataset.
14

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TimePoint
A.3. GPU RAM Consumption Analysis
Table A.J. Total GPU memory usage comparison. Summary of seven UCR datasets and the corresponding GPU memory usage (in GB, rounded to integers) for 1-NN DTW. The 7P DTW columns represent the memory foolprint at different keypoint ratios, while standard DTW uses the full signal length.
DTW
TP + DTW
Dataset
Train
Test
Length
10%
20 %
50 %
100 %
ChlorinnConnenntaation
3840
166
186
8
47
Crop
7200
16800
46
996
17
46
260
996
ECG5000
500
4500
140
167
2
7
42
167
TwoPatterns
1000
4000
128
248
3
11
63
248
U Library
896
3582
315
1194
49
302
1194
Wafer
1000
6164
538
22
538
Yoga
300
3000
426
611
7
25
154
611
Memory Consumption and Channel Independence. As shown in Table A.1, when both DTW and TP+DTW applied to the full sequence (keypoint ratio 100%), the GPU memory consumption remains the same despite the fact that TP features are 256-dimensional. The key reason lies in the memory structure of our DTW implementation.
Dynamic Programming (DP) Matrix. The largest tensor created during DTW is a 4D DP matrix D of shape [bat cbasch.izee.x bat ch.sizey, length.x + 1, length-y + 1]. Notably, this matrix does not include any channel dimension. As a result, increasing the feature dimensionality from, say, 256 does not change the DP matrix size.
Cost Computation Over Channels. At each time step (i,j), compute scalar cost by reducing across the channel dimension, whether using cosine similarity & distance. Consequently, the DP matrix stores only a single cost value for each pair (i,j), independent of the descriptor dimensionality.
Equal Memory Footprint at Full Length. When keypoint ratio = 100%, TP+DTW and plain DTW both operate on the entire time series. Although TimePoint descriptors have a higher channel count (e.g., 256), that extra dimensionality is collapsed into single scalar during the pairwise cost computation, leading to an identical memory footprint for the DP matrix. Hence, the total GPU RAM consumption scales only with the product of the sequence lengths and batch sizes, rather than the descriptor dimensionality number of channels.
15

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TimePoint
B. SynthAlign
Synthetic Dataset Training Loss Over Epochs
Unseen Datasets DTW-KNN Accuracy Over Epochs
0.7
Detecto" loss
1.00
Descriptor Loss
0.6
0.95
0.4
Lo55
0.3
0.80
0.2
0.75
CBF
0.1
0.70
ECGFiveDays
GuniPoint
0.0
0.65
TwoLeedECG
5000
10000
15000
20000
25000
5000
10000
15000
20000
25000
Epoch
Epoch
Figure B.I. Left: Training losses SynthAlign data. Right: DTW-KNI WNN accuracy unseen datasets. As the training losses decrease the accuracy increases, indicating the loss functions, along with the synthetic training framework (SynthAlign) generalize well to real-world data for the task of identifying keypoints and learning descriptors. Epochs trimmed 25K for visibility purposes.
Does synthetic training generalize to real-world data? A notable finding is that jointly minimizing the keypoint detection and descriptor losses on the synthelic dataset leads to improved k-NN accuracy on unseen real-world data, as evident from Figure B.I. This suggests that learning to detect salient points and produce consistent descriptors under controlled, yet varied, synthetic data and distortions allows for alignment capabilities that transfer beyond the training distribution.
Data generation To train and evaluate our method under diverse temporal patterns and keypoint structures, we introduce the SynthAlign dataset. Each sample comprises a univariate time series of length L and an associated keypoint (KP) mask that marks salient events (e.g.. peaks, start/end points). We generate data on-the-fly using composition of procedurally defined waveforms and optional augmentations.
SynthAlign randomly draws from four principal waveform generators with specified probabilities:
1. Sine Wave Composition: Superposes multiple sine waves (random frequency, amplitude, and phase), with derivative- based KPs for local maxima or minima.
2. Block Wave: Creates square-war wave-like segments with variable block sizes and amplitude, marking boundaries as KPs.
3. Sawtooth Wave: Forms a sawtooth signal of random frequency, designating signal resets as KPs.
4. Radial Basis Function (RBF): Summation of Gaussian "blobs," entered at random position; KPs appear blob peaks.
Each generated signal has length = 512 by default, though the code supports arbitrary lengths.
Composition and Augmentations. After selecting one or more waveform generators (drawn with probabilities [0.6, 0.15, 0.05, 0.2] from the waveforms mentioned in the list above), the resulting signals are summed to form a nal sample. We also introduce:
Linear Trends: Randomly superimposed slopes and intercepts to simulate mild non-stationarity.
Flips: Inverts random subsection of the signal.
Noise: Adds Gaussian noise sampled from from.((O. (0, 0.1) to further diversify training samples.
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TimePoint
Keypoint Extraction. Keypoints originate from local maxima/minima (sine and sawtooth), block boundaries, Gaussian centers (RBF), and explicit markings for flips and linear boundaries. We ensure start/end points are included only when warranted by the signal design. This strategy provides rich variety of salient events, allowing models to learn robust keypoint detection across diverse waveform shapes. Overall, SynthAligg ign delivers flexible pipeline for generating synthetic time series with automatically labeled keypoints, serving as a valuable testbed for alignment, detection, and descriptor-learing techniques. Figure B.2 shows additional sample drawn from SynthAlign
Mmn
Mw MMM
WwM
Figure B.2, More samples from SynthAlign.
17

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TimePoint
C. CPAB Transformations
Let T be a diffeomorphism parameterized by The main reason we chose CPAB transformations (Freifeld et al., 2017) as the parametric diffeomorphism family to used within our method that they are both expressive and efficient. Our presentation of CPAB transformations below closely follows (Freifeld et al., 2017), though we restrict the discussion the ID case (for the more general case, (Freifeld al., 2017)).
Let f2 [a, b] C I be finite interval and let V be space of continuous functions, from to IR that are also piecewise- affine w.r.t. some fixed partition of S2 into sub-intervals. Note that is miiieedimensional linear space. Let d = dim((), let E RR and let e E denote the generic element V, parameterized by O. The space of CPAB transformations obtained via the integration of elements of V, defined as
(x; s.t. (x;;)) solves the integral equation @6 (x;tt) = x S (0(00(;;))) dr where e E V T _ 10 x - +
{
Every 7 E T is order-preserving transformation (i.e., it is monotonically increasing) and diffeomorphism (Freifeld et al., 2017). Note that while 6 E V is CPA, the CPAB 7 E T is not (e.g., 7 is differentiable, unlike any non-trivial CPA function). Equation also implies that the elements of V are viewed as velocity fields. Particularly useful for us are the following facts: 1) The finer the partition of f2 is, the more expressive the CPAB family becomes (which also means that increases). 2) CPAB transformations lend themselves to a fast and accurate computation in closed form of r T (x:) (Freifeld al., 2017). Together, these facts mean that CPAB transformations provide us with convenient functions. Figure C.I show random CPAB
and an efficient way to parameterize nonlinear monotonically-increass transformation applied to synthetic data sampled from synthAligm. while Figure C.2 shows the effect of increasing the standard deviation varr)) when sampling 0 from the CPA smoothness prior.
CPA-based Transformation
Signal and Warped Signal
CPA-based Transformation
Signal and Warped Signal
Originel Warped squaa
Original aggnal Warped
trutuffrr
tratsform
Contimumuu Precenicn Veloctt Pield
Continusue Pleceriie Affine Weinciy Field
0.20
0.25
CPA-based Transformation
Signal and Warped Signal
CPA-based Transformation
Sigmal and Warped Signal
Mdentity
1.8
Original dignal
Identity
Original dignal
3.8
CPAB transform
Warped sinaal
CPAB traasform
2.0
Warpod signal
1.0
1.8
3.0 2.0
5.4
0.5
0.0
2.2
0.5
1.0
Custinanaa Pacceita Afline Valucity Fwild...
Contamanax Haccesia Affine Velecity Pall
-2.5
64
0.2
0.0
3.5
3.5
75
15.0
125
60
63
50
1.0
120
0.3
25
5.0
75
163
122
60
80
100
120
0
Q
Figure C.I. Additional examples of the CPAB transformation.
18

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TimePoint
CPAB warps Owr
CPAB warps dea
2.0
16-
Amplttud
CPAB warps at = 0.2
CPAB warps at Oua 0.2
Oriinal Time Series
ong not Time Serles
Augmertee ted Time Serre
Aug mevier Time Serto
Ampltude
CPAB warps Ovu 0.5
warps at Our 0.5
Original Series
Ong nal Tim Series
3.5
Amplitude
Figure C.2. SynthAll gn synthetic data augmented with CPAB warps increasing magnitude (top-to-bottom). Each panel shows the original signal in blue and 3 random augmentations in gray.
19

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TimePoint
D. TimePoint Architecture Details
Table D.I. SuperPointID model architecture. Conv(Cin -> Cout, k) denotes ID convolution from Cin to Cout channels with kernll size k. followed by BatchNorm and ReLU. WTConv denotes the wavelet-based convolution with group splitting and scale modules.
Stage
Layer
Details
Encoder (WTConwEncoder1D)
layer!
IonvBlockID
Conv(1 + 128, &o = 3, stride=1, padding-same)
BatchNormm1188) RcLU
layer2
WTConvBlockID
WTConvid(128 128, kemel=3, groups=128) scale modules
Conv(128 + 128, i: = 1). BatchNorm(L28), ReLU
layer3
WTConvBlockID
WTConv1d(125) 128, kemel=3, groups=128) scale modules
Conv(128 + 256, i: 1). BatchNorm((56),, ReLU
layer4
WTConvBlockID
WTConv1d(255) 256, kemel=33, groups=256) scale modules
Conv(256 256, k 1), BatchNorm(26), ReL.U
Detector Head (DetectorHcadII)
Conv(256 - 8, l 1) Sigmoid
Outputs detector logits for keypoint heatmap.
Normalize the logits to probabilities
Descriptor Head (Deseriptorflead
Conv(256 + 256, k = 1)
Descriptor feature map. Upsample with scale factor 8, mode=linear.
Upsample
20

---

TimePoint
E. Full UCR Archive Results
E.1. UCR Archive Details
ID
Type
Name
Train
Test
Class
Length
Image
Adiac
390
391
37
176
Iragee
ArrowHead
36
175
251
Spectro
Boef
30
470
Image
Beetleblly
20
20
512
Image
BirdChicker
Sensor
Car
60
60
577
Simulated
CBF
Sensor
CulorinCCoacentrattt
467
3840
166
Sensor
CinCECGTorso
40
1380
1639
10
Spectro
Coffee
28
286
11
Device
Computers
250
250
720
Motion
CricketX
13
Motion
CricketY
390
390
12
300
14
Motion
CricketZ
390
390
300
15
Image
DistetSizeRedaattonn
16
306
345
16
Image
400
139
80
Image
DisulPMallanSOutianc
18
Image
DistuPPuulaxTWW
400
139
80
Sensor
Farthquakes
20
ECG
ECG200
100
100
96
21
ECG
ECG5000
500
4500
1440
22
ECG
ECGFFiveDays
23
861
136
23
Device
EleetrieDevices
8926
7711
96
Imuge
FaceAll
25
Image
FaceFoorr
24
88
350
26
Image
FacesUCR
200
27
Image
Fordy
450
455
50
270
28
Image
Fish
175
175
463
29
Sensor
FordA
3601
1320
500
30
Sensor
FordB
36336
810
500
Motion
GunPoint
32
Spectro
Ham
109
10S
431
33
Image
HandOutlines
1000
370
2709
34
Motion
Haptics
155
308
1092
35
Image
Herring
64
64
512
36
Motion
InlineSkate
100
550
1882
37
Sensor
InserrWingheatoonnd
220
1980
256
Ital PowerDDeand
39
Device
BungeKitchenAppiianee
375
375
720
40
Scnsor
Lightning2
60
61
41
Sensor
Lightnnng?
70
73
319
42
Sinnalated
Mallat
55
2345
1024
43
Spectro
Meat
60
60
448
44
Image
Medicallmages
381
760
99
Image
46
Image
600
291
80
47
Image
MiddlcPhalaxxTV
399
154
80
48
Sensor
MoteStrain
20
1252
84
49
ECG
NombovaiiveeeallECGGTT
1800
1965
750
50
ECG
1800
1965
750
51
Spectro
OlivoOil
30
30
570
52
Tmage
OSULeea
53
Image
Phalangs@OrtinsCooreett
1800
858
80
54
Sensor
Phoneme
214
1896
1024
55
Sensor
Plane
105
105
144
56
Image
ProxinalPhalannCCiiineen geroupp
400
205
80
57
Image
PossimalPhalancCuuttisCCCCCC
600
291
80
58
Image
ProximalPhalaaxI
400
205
80
59
Device
Defii
375
60
Device
ScreenType
375
375
720
61
Sinulltted
ShupeletSim
20
180
500
62
Image
ShapesAll
600
600
512
63
Device
AmallKiichen Apliances
375
375
720
64
Scnsor
Surrfcee
20
601
70
65
Sensor
Sony
ABOORoootSurface
27
953
65
66
Sensor
StarLightturve
1000
8236
1024
67
Spectro
Strawberry
613
370
235
68
Image
SwedishLeent
500
625
128
69
Image
Symbols
25
995
398
70
Simalated
SyntheticControl
300
300
60
Motion
IseSogmentatiiol
228
72
Motion
BseSegmenntaiin22
36
130
341
73
Sensor
Trace
100
100
275
74
ECG
TwoLeadECG
23
1139
82
21

---

TimePoint
ID
Type
Name
Train
Test
Class
Longth
75
Simulated
TwoPatterms
76
Motion
UWaveGestune All
896
3582
945
77
Motion
UWaveGesturel Abbray
896
3582
315
78
Motion
UWaveGesture Jibrary
896
3582
315
79
Motion
U UWavGGestueLiiraay2
896
3582
315
Sensor
Wafer
81
Spectro
Wine
57
54
234
82
Image
WordSynonyms
83
Motion
Worms
181
77
900
84
Motion
WornsTwoClnss
181
77
900
85
Image
Yoga
300
3000
86
Device
ACSF1
100
100
10
1460
WinnoteX
Vary
88
Sensor
AllGesture Wiimote
300
700
10
Vary
Sensor
AllGestureWimoteZ
Vary
90
Simulated
BME
30
150
128
91
Traficc
Chinatown
20
343
92
Image
Crop
7200
16800
93
Sensor
DodgerLoopDay
78
80
283
95
Sensor
Dodgerf Weckendd
20
138
288
96
EOG
EOGHorizontalSignal
362
362
1250
97
EOG
EOG VerticalSignal
362
362
1250
98
Spectro
EthunolLevel
504
500
1751
99
Sensor
FroezetRogular
100
Sensor
FreezerSmal/Tran
28
2850
301
Fungi
102
Trajectory
GestureiidAirD
208
130
26
Vary
Trajoctory
GestucMidAArD2
208
Vary
104
Trajectory
MistureMilAiiDD
208
130
26
Vary
Sensor
GesturePebbleZ1
132
172
Vary
GestuePebblee22
Vary
107
Motion
GumPointAgeSpun
135
316
150
Motion
GunPoiniMaleeene VernnMMaleeessaaemmaall
109
Motion
GunPointOldVersus Young
136
315
150
110
Device
HouseTwenty
40
119
2000
111
EPG
62
249
601
112
EPG
InseetPGSmmalTriin
17
249
601
Trafiic
MelboumeePeesstiiin
114
Image
MixedShupes RegularTrai
500
2425
1024
Image
MixedShupesSmall7nii
1024
116
Scnsor
PickupGesture Wimoee
50
50
Vary
117
Hemodynamiis mamodvnammiss
HggAirwayPressur Pressure
104
208
2000
118
lemmndynamics
PigAnPrressure
104
208
52
2000
119
Hemodynamics
PigCVP
104
208
2000
Device
PLAID
Vary
121
Power
PowerCons
180
180
144
Spectrum
Rock
20
2844
123
Spectrum
SomgHtanGGenderh22
300
600
1500
124
Specirum
Semgl
SemgilaadMeeeemenCCC
450
450
1500
125
Spectrum
HamgHHandSubjecthha
450
450
1500
126
Sensor
ShakeGesture WiimoteZ
50
50
Vary
Sinulated
SmonohSabspace
128
Simulated
UMD
36
144
150
E.2. UCR Archive Details
22

---

TimePoint
Table E.2: Accuracy Comparison Between TimePoint Configurations (SynthAAign training only.)
Datass/Method
TP4DTW
TP-SonDTW (7
(0.1)
(0.2)
(0.5)
(1)
(0.1)
(0.2)
(0.5)
(1)
Adioc
0.645
0.678
0.734
0.742
0,519
0.445
0.595
0.737
AllGestreWiimnee
0.55
0.554
0.552
0.542
0.461
0.421
0.454
0.437
ArrowHead
0.809
0.851
0.874
0.669
0.657
0.506
0.840
BMB
0.960
0.940
0.893
0953
0.953
0.893
0.860
0.947
Beef
0.733
0.767
0.700
0.800
0.600
0.500
0.700
0.811
BeetleFit
0,950
0.850
0.900
0,900
0.850
0.950
0.850
0.950
BiniChicken
0,900
0.750
0.750
0,950
0.750
CRF
0.940
0.900
0.400
0.933
0.9%0
0.970
0.988
0.9%6
Car
0.850
0.850
0.767
0.677
0.633
0.735
0.783
0.817
Chinatown
0.831
0.924
0.915
0.945
0.828
0.901
0.974
0.942
ChiviineCanceenntt
0.574
0.634
0.538
0.574
0.635
Coffee
1,000
1.000
1.000
1,000
0.750
0.714
0.893
1,000
Computers
0.668
0.676
0.664
0.676
0.628
0.664
0.630
0.552
CricketX
0.715
0.703
0.551
0.754
0.610
0.585
0.685
0.685
CricketY
0.664
0.628
0.705
Cricket7
0.726
0.723
5,590
0.766
0.654
0.592
0.713
0.708
Crop
0.665
0.893
0.682
0.705
0.658
0.670
0.693
0.705
DialomSizeReduution
0.967
0.958
0.967
0.958
0,830
0.794
0.905
0.958
0.583
0.619
0,583
0.604
0.612
0.547
DistalPfhalamm Cutrene
0.652
0.692
0.699
0,717
0.616
0.685
0.678
0.714
DistalPtullann TW
0,511
0.590
0.583
0.590
0.554
0.532
0.540
0.576
FCC1200
0.850
0.830
0.820
0,820
0.820
0.830
0.900
0,800
DCG5000
0.924
0.921
0.922
0.916
0.912
0.812
0.897
0.42
0.97
0.659
0.683
0.676
0.895
Fatthquiks
0.669
0.662
0.601
0.691
0.676
0.662
0.619
0.676
DlectricDeviies
0.593
0.607
0,666
0.613
0.588
0.592
0.602
0.615
AlueATl
0.676
0.717
0.720
0.667
0.691
0.730
FaceFour
0.807
0.922
0.886
0.932
0.693
0.727
0.912
0.943
CCR
0.735
0,540
0.802
0.859
0.682
0.762
0.831
0.852
Fings
0.745
0.760
0.736
0.802
0.684
0.668
0,677
0.754
Fish
0.874
0,903
0.880
0.209
0.834
0.891
FomIA
0.780
0.775
0.768
0,791
0.717
0.729
0.754
0.782
FonlB
0.641
0.640
0.647
0.662
0.586
0.635
0.640
0.623
TresseRegula
0,913
0.927
0.876
0.891
0,876
0.981
0,920
TreoorSmallTaan
0.780
0.790
0.760
0.798
0,756
0.763
0.962
0,771
Fungi
0,957
0.995
0.25
0.95
0.839
0.751
0.957
0.984
GestreMidAinDL
0,038
0.008
0.038
0.038
0.038
0,038
0.008
0.008
M
0,038
0.038
0.038
0.038
0,038
0.038
0.038
0.008
GesureMidAitD3
0,038
0.008
0.038
0.038
0.038
0.038
0.008
0.008
GestreP&bble211
0.163
0.163
0.163
0.663
0,163
0.163
0.163
0.163
GesumPPbblee2
0,152
0.152
0.152
0.152
0.152
0,152
0.152
0.152
GunPaint
0,972
0.993
0.933
0.993
0,913
0,860
0,980
0.993
GunPuintA Sppan
0
0.975
0.922
0.953
0.924
0.972
0,965
GunPointMaie VersusFeemale
0.994
1.000
0.997
1.000
0.978
0.994
1.000
1.000
GunPuintOldNensu Young
0
0.901
0.959
0.81
0.895
0.940
0.901
0,911
Ham
0.514
0.533
0.457
0.552
0,562
0.524
0.600
0.533
Herring
0.489
0.547
0.531
0531
0.594
0.625
0.453
0.516
0.954
0.922
0.766
0928
0,55
0,827
0.807
0.819
AllGessureWiime
0.558
0.561
0.788
0.558
0.192
0.47
0.47
0.457
AllGessure WiimoteZ
0.282
0.237
0.287
0.227
0.137
0.172
0.815
0.795
0,703
0.03
0.755
0.711
0.747
InseetWingbea
0.465
0.534
0.00
0.534
0.487
0.554
0.563
Itay enDannddd
0.341
0.534
0.557
0945
0.955
0.939
0.944
0.549
Large LangeKitchenA Kpcleennpnppsss
0.757
0.744
0.709
0.755
0,640
0.613
0.589
0.560
Lighning?
0.836
0.809
0352
0820
0.738
0.805
0.830
0.522
Lighning?
0
0.740
0.888
0767
0,016
0.658
0.038
0.740
Mallaa
0.912
0.565
0.807
0.97
0.664
0.630
0.782
0,900
Meat
0.500
0.000
03883
0.783
0.800
0,800
0
Medicallmages
0.687
0.714
0.729
0.28
0,674
0.672
0.722
0.714
0.422
0.390
0.461
0.396
0.364
0.429
0.396
0.442
M SosteertiaassstttneeeCrraa
0.663
0.729
0.094
0722
0.625
0.500
01002
0.722
MiddlePhalansIWW
0.442
0.494
0.526
0.578
0.884
0.912
0.880
0914
0.874
0.831
0.536
MoteStrain
0.826
0.561
0352
0862
0.863
0.865
0.866
Nontavasve horax!
0,814
0.798
0.769
ON25
03583
0.393
0.751
0.814
0.847
0.867
0.677
0.825
0.860
USULeaa
0.789
0.793
0.699
0810
0.086
0.661
0.715
OlivoOil
0.567
0.700
0.900
0933
0,633
0.500
0.600
0.633
0,600
0.727
07748
0740
0.671
0.699
0.681
0.754
Phoneme
0.267
0.208
02361
0284
02112
0.206
0.166
0.165
PickupGettre Wiimett
0.100
0.100
1.000
0100
0.100
0.100
0.100
Plane
1,000
0.500
10000
1000
1000
0.981
1.000
0.990
PowerConn
0.856
0.506
0388
0390
0311
0.967
0.911
0.861
0.700
0.505
0810
0.750
0.800
0,500
0,818
0.821
0.825
0812
0852
0.821
0.818
0.545
ProkimalPhalaneTW
0.673
0.693
0.712
0.277
0.685
0.721
0.751
0.712
Refiigentionneiess
0.525
0.491
0.504
0.483
0.461
0.451
0.429
0.483
Scrceillype
0.413
0.419
0.376
0.395
01387
0.384
ShukoGestureWmmmee
0.100
0.300
0.100
0.100
0,100
0.100
0.100
0.100
ShupeletSim
0.600
0.644
0.67
0.11
0.553
0.578
01550
0.644
ShapesAll
(1800
0.873
0.822
0.878
0.728
0.782
0.777
0.803
0.555
0.570
0.600
0.576
0.507
SmmorrSShhegeee
0.500
0.793
0.807
0813
0.793
0.807
0.907
0.793
Sony A AIBURccbttSrrfeee
0.839
0.764
0.760
0.745
0.814
0.800
0.785
0L777
Sony AbBORebolSurfeee
0.850
0.586
0.800
0.866
0.848
0.871
0.855
0.860
Straebeny
0.932
0.511
0.889
0.927
0.951
23

---

TimePoint
DetasesMethod
TP-DTW
TP-SunDTW (7
(0.1)
(0.2)
(0.5)
(1)
(0.1)
(0.2)
(0.5)
(3)
SwedishLeaf
0.875
0.504
0.888
0.899
0.790
0.811
0.866
0.504
Symbols
0.949
0.955
0.939
0.965
0.897
0.912
0.860
0.930
SysthelieCanted
0.957
0.977
0:967
0.970
0.980
TeeSemmenttiion
0.846
0.838
0.555
0882
0.789
0.785
0.789
0.829
TesSegmenttiion
0.823
0.585
0321
0.000
0.777
0.800
0.862
0.869
Trace
0.950
0.900
0.990
0.990
1,000
0.950
0.930
0.990
0.830
0.816
0.695
0.785
TwoPiilerns
0.779
0.771
0.87
0.78
0.681
0.676
0.602
0.756
UMD
0.910
0.539
0.840
0.889
0.854
0.819
0.812
0.854
UWaaveeettre All
0.917
0.956
0.927
0.68
0.784
0.913
0.910
0.905
Cesuurrr X
0.758
0.791
0.753
0.740
0.703
0.730
0.716
0.743
0.643
0.682
0.560
0.711
0.689
0.728
0.701
0.738
0.652
0.693
0.688
0.714
Wafer
0.994
0.993
0.989
0.994
0.990
0.990
0.995
0.994
0.611
0.722
0.611
0.574
WordSynoryms
0.688
0.721
0.694
0.737
0.594
0.605
0.589
0.694
Worms
0.662
0.584
0.600
0.584
0.649
0.519
0.558
0.597
WormeTwoClaes
0.727
0.636
0.649
0.675
0.688
0.549
0.623
0.688
Yogn
0,858
0,760
0.832
0.856
Table E.3: Accuracy Comparison Between Competitors.
Datsee/Mthhd
DIW
DTW-GI
Eac.
ShapeDTW
SoNDTW
(dev)
(bog)
0..1
(y
(> 10)
Adiac
0,588
0.604
0.066
0.652
0.251
0.637
1.000
0,513
0,750
AllCestteWimmie
0.135
0.611
0.101
0.327
0.377
0.613
0.663
0,833
0,576
Amowllead
0.680
0.229
0.674
0.712
0.550
0.761
BMB
0.900
0,900
0.493
0.760
0.707
0.783
0.800
0.696
Reaf
0.567
0.635
0.267
0.700
0.733
0.667
0.935
0.714
0.341
BeeileFlly
0,700
0,700
0.450
0.650
0.750
0.750
0,850
0.781
0,769
BiniChicken
0,750
0.800
0,700
0.550
0.797
0.880
CRF
1,000
0.997
0.658
0.360
0434
0.066
0.676
0.783
0,800
Car
0,750
0.735
0.600
0.717
0.717
0.817
0.600
0,046
0.925
Chinatown
0.965
0.956
0.805
0.953
0.948
0.962
0.764
0.907
0,120
0.627
0.709
0.511
0.750
0.600
Coffee
0,961
1,000
0.536
0.961
1,000
1,000
0.752
0,913
0,808
Computers
0.668
0.696
0.672
0.528
0.624
0.556
0.933
0.948
0.046
CrickeXX
0.772
0.754
0.097
0.282
0.400
0.669
0.747
0.857
0,577
CricketY
0.749
0.074
0.226
0,411
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CrickeiZ
0.787
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0.567
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Crop
0,876
0.664
0.052
0.719
0.524
0.716
0.995
0.953
0.899
DislomSizeReduciion
0.961
0.967
0.431
0.922
0.958
0.889
0.676
0.551
0.663
0.748
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0.521
0.650
0.725
0.717
0.478
0.757
0.721
0.659
0.809
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0,650
DisulPthallnsTV TW
0.640
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0.468
0,590
0.612
0.511
0.611
0.809
0,521
BCG200
0.800
0,770
0.350
0,880
0.340
0.152
0,946
0,859
BCG5000
0.930
0.137
0.921
0.633
0.665
0.631
BCGF
0.775
0.768
0.551
0.747
0.920
0.830
0.665
0.631
0.717
Barthquakes
0.669
0.719
0.695
0.259
0.724
0.662
0.046
0.797
0.880
Deviiris
0.653
0.592
0.232
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0,916
FaueAll
0.772
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0.762
0.717
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FuceFour
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0,830
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FCResUCR
0.934
0,905
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Wordy
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Fish
0
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ForlA
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FordB
0.606
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0.935
Rrguler
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0.899
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0.892
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0.879
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FresseSSmalTrain
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0.703
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Fungi
0.909
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0,973
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1,000
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GesirreMdAAiDL
0
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DestueMdAAiit2
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GesturePebbee21
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0.615
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0.581
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0.808
0.907
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GestwePhhle22
0.152
0,563
0.152
0.201
0.563
0.722
01341
01805
0.907
GunPeint
0.880
0.515
0,960
09600
0.519
0.606
0.915
0.690
0.956
0,953
0.84
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0.519
0.605
GunPointMaae Versuskemale
0:957
0.997
0.867
0.997
0.891
1000
01539
0.760
0,519
GunPeintDldNeenn Young
0.841
0.838
0,314
0.991
01844
1.000
0.766
0,539
0.700
Harn
0.562
0.4%
0.543
0.333
0.360
0.575
0.580
Herring
0.547
0.551
0.406
0.531
0.594
0.91
0.45
0.046
0.797
InsocdtPGGReggaaataaa Train
0.867
0.871
0.703
0,530
0.743
1000
0.902
0.610
0.066
AllGistteeWimee Wilooee
0.154
0.558
0.1
Nan
Nun
Nan
0.423
[.66]
01833
AllGiesture Winmonel
0.084
0.4%3
0.661
0.852
AnsecEEPSSmalliin
0.719
0.735
0.691
0.546
0.679
0.046
0.962
0,610
InteerWinghettSoee
0.431
0.355
0.091
0.523
0.552
0.567
0.785
0.712
0.550
ItalyPower enteemand
D.9466
0.950
0.532
0.954
0.881
0.965
0.399
0.764
0.907
0.837
0.795
0.355
0.480
0.120
0,933
0,948
Lighning?
0.803
0.869
0.541
0.475
0.574
0.303
0.948
0.8446
0.962
Lightning?
0.767
0.726
0.151
0.356
0.200
0.589
0.603
0.8015
0.880
Mallar
0.914
0.934
0.244
0.857
0.580
0.114
0.100
0.975
0.109
Meat
0:933
0.333
0.753
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Medicallmages
0.754
0.757
0.478
0.604
0.336
0.166
0.300
0.696
0.152
0.506
0.500
0.208
0.552
0.448
0.513
0.600
0.611
0.809
24

---

TimePoint
Mathosd
DTW
DTW-GI
Eac.
ShapdDTW
SOADTW
(dev)
(bog)
(rm)
0.1)
(ty
(y 10)
0.704
0.698
0,554
0.706
0.643
0.766
017712
0,600
0.611
MiddlePPullbsTTV TW
0.494
0.506
0.448
0.487
0.494
0.487
0.823
0.712
0,600
MistShupsSmmllTaan
0.540
MoteStrain
0.89]
0.835
0.570
0.761
0.892
0.946
0.859
0.174
0.772
0,790
0.024
0.550
0.833
0.512
0.835
0.101
0.913
0.851
0.864
0,052
0.787
0.891
0.689
0,899
0.835
0,101
OSULeaf
OlivoOi!
0.833
0.133
0.833
0.667
0.46
0.925
PhalongesDuttinsCCooom
0.719
0.728
0.505
0.790
0.760
0.669
0193
0,823
0.712
Phomeme
0:272
0.228
0.011
0,082
0.047
0.117
0.952
0.849
0,100
8.130
0.831
Plane
1,000
1,000
0.095
0.971
0.952
0.91
0.618
0,633
0,665
PowerCons
0.872
0.878
0.506
0.728
0.806
0.972
0.606
0,618
0.633
ProcimalPlulansCiii Catralaluaanniileepeferrro
0.776
0.805
0,820
0.829
0.780
0.780
0,580
0.933
0.823
PreximulPhalamm OutlineCumee
0.849
ProximulPhnlamTTV TW
0.75]
0.756
0.737
0.737
0.668
0.702
0.174
0.611
0,880
Refigeminnoovves
0.450
0.461
18384
0.341
0.885
0.424
01914
0.120
0.033
ScreenTyyp
0.416
0.395
0,600
0.333
0.320
0.913
0.914
0.120
SlukeGestre
0.650
StupeletSim
0.756
0.650
0.506
0.994
0.650
0.784
0.769
ShapesAll
0,773
0.768
0.058
0.615
0.720
0.778
0.925
0,455
0.046
Small KiccheAppnineee
0.707
0.645
0.336
0.357
0.400
0.405
0.101
0.913
0.914
SmoochSubspuch Subepace
0.740
09007
0.120
Sony SonyAIDo A
0.712
0.725
0.696
0.704
0.622
0.600
0.955
0,830
Sony funt
0.843
0.831
0.643
0.837
0.728
0.885
0.955
0,830
0.905
Strawberry
0.943
0.941
0.643
0.959
0.935
0.911
0.550
0.761
0,550
SwedishLenf
Symbolls
0.953
0.900
0.370
0.822
0,907
0.305
0.650
0,853
CymmretisConrrol
0.987
0.867
0.203
0,440
0.350
0.907
0.830
0.995
0,953
Seggemmmnttionn
0.798
0,772
0.649
0.645
0.610
0.737
0.913
0.808
0,516
IneSegmematioon2
0.846
0.838
Trace
0,990
1,000
0.190
0.880
0.570
0,900
0.808
0.516
0.819
TwaLowEEG
0.931
0,904
0,500
0.969
0.651
0.848
0.839
0.174
0.611
TwoPutterns
1,000
0.255
0.667
0.766
Ueaurr ibrary
0.916
0.891
0.138
0.529
0.948
0.845
0.974
0.780
0,928
UWwreGesuurLiirau X
0.731
0.671
0.162
0.625
0.749
0.574
0.679
0,587
0.879
0.645
0.605
0.149
0.439
0.671
0.523
0,530
0.679
0.587
UWwaeGesturLLiirae ibrayyy
0.609
0.129
0.566
0,805
0,880
Wafer
0.984
0,980
0.834
0.996
0.996
0.999
0.750
0,680
0.766
Wine
0.593
0,574
0,500
0.519
0.611
0.593
0.761
0,550
1,000
WordSynceyms
0.676
0.045
01316
0.519
0.416
0.101
MormaTToollss
0.636
0.623
0.429
0.571
0.597
0.610
0.109
0.101
0.899
Yoga
0.839
0.836
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0,780
0.797
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0.789
0,395
0,050
25


Reviews:
[{"review_id": "ZvoWDrxjPl", "reviewer": "Reviewer_QUAf", "summary": "This paper proposes a self-supervised method that dramatically accelerates DTW-based alignment while typically improving alignment accuracy by learning keypoints and descriptors from synthetic data.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their kind words and comprehensive response and suggestions. \n\n# Q1 - Non-stationary time series  (NSTS)\nThis is an excellent point. A single CPAB warp implies a stationary velocity field, while the CPAB prior restricts the warps to avoid unrealistic distortions. Three factors affect the alignment quality:\n1. KP detection is unaffected by NSTS as it is performed independently for each TS. \n2. Descriptors are also computed independently. Since each descriptor's receptive field is quite large, it might be affected by NSTS. \n3. The matching is computed using DTW on TP’s features at KPs locations. DTW is not restricted to a specific type of misalignment between TS, and in TP’s case only relies on the features' similarity. \n\nEmpirically, the UCR archive consists of more than a 100 of real-world datasets, including several NSTS datasets. E.g., “ECGFiveDays” dataset holds ECGs that were recorded five days apart. TP reaches 94.2% test accuracy while the best competitor achieves 92% (see Tables E2 and E3 at our appendix). \n\nAs an aside, one can compose CPAB warps, and, partially because CPAB warps are not closed under composition, this has the effect of integrating a non-stationary velocity field. However, in 1D, this effect can be well approximated by taking a finer tessellation. Either way, we haven't noticed an empirical need to do so.\n# Q2 - Fine-tuning on real-world data \nTrue, the performance could potentially be enhanced by fine-tuning the model on real-world data. The reasons we chose to focus the training on synthetic data (as opposed to the test, that was done on real-world data) are: \n1. We are unaware of real-world TS dataset with enough annotated KPs. \n2. Using synthetic data bypasses any license-related issues that might arise. \n\nThat said, and in accordance with the reviewers' suggestions, here in the rebuttal we include additional results, obtained after fine-tuning TP on real-world data (see below). \n\n# Fine-Tune on Real-World Data\nWe thank all reviewers for their suggestions and inquiries regarding fine-tuning TP on real-world data. \nThe experiment details are as follows: \n* We used the same method to compute KP as we did for SynthAlign (local minima/maxima, etc.).\n* We fine-tuned TP after it was first trained on SynthAlign. \n* The training data contained virtually the entire UCR archive (a few datasets were omitted due to technical reasons). \n* We created pairs with known correspondences by applying two CPAB warps to each input signal (i.e., each signal is augmented twice, and those augmented views are the input for TP and loss functions).\n* Training for 2K epochs \n\nBelow we report results for 10%/20%/50% of the length (the experiment \nwith 100\\% didn't finish in time for the rebuttal, but we'll include it in the revised manuscripts). \n\n**Results**\n| Method                   | Baseline (DTW) | 10%   | 20%   | 50%   |\n|--------------------------|----------------|-------|-------|-------|\n| TP (Synth)               | 0.706          | 0.707 | 0.721 | 0.710 |\n| TP (Synth + Fine Tune)   | 0.706          | **0.777** | **0.790** | **0.769** |\n\nThe results show that fine-tuning TP on real-world datasets yields a significant improvement in performance across all KPs percentages. The table follows Table 1 from the paper, where the results in the first row are taken from the paper and the second row from this fine-tune experiment. The runtime remains the same as in Table 1. \n\nWe thank the reviewers for suggesting this experiment. Fine-tuning TP on real-world data, using our proposed training scheme, is a useful addition to the overall method.  We will incorporate this experiment in the revised manuscript. \n\n# Q3 - Robustness with missing KPs\nRe missing data, we agree such analysis can be benficial. That said, we are unsure exactly what the reviewer meant: “[...] where certain points are removed from the pairs and evaluating on incomplete data“. Since during inference KPs are computed automatically by TP, the scenario where points are removed never occurs in our current setting. \n\nWe have conducted a **“Robustness to Noise”** experiment using Blurring and Jitter. (please see our answer to **zrdP**). \n\n# Q4 - Sensitivity to the threshold. \nThe choice of threshold is critical for TP’s performance. To that end, Table 1 shows the performance for 10\\%, 20\\%, 50\\%, 100\\% of KPs. Effectively, each value corresponds to a different threshold (the reported runtime includes sorting KPs by their probability, so the overhead is minimal). Since the evaluation was conducted on 100+ datasets, choosing a fixed threshold for all dataset will be sub-optimal. Thus, we believe this adaptive strategy is a good heuristic when working with a large number of datasets. When working on a specific dataset, cross-validation may also be used for choosing the value. \nWe will elaborate on the model’s sensitivity to the choice of threshold in the revised manuscript.", "claims_and_evidence": "The claims are clear and convincing except one concern. \nWhile CPA is an effective method for modeling nonlinear temporal distortions and generating correspondences between time series signals, I am concerned that CPAB may not be sufficiently comprehensive to model all types of time series distortions, particularly those exhibiting high complexity and variability. For instance, in the case of non-stationary time series, the parameter $\\theta$ in the CPAB transformation might evolve over time. I recommend that the authors conduct further studies to enhance their proposed method and ensure it can fully address all challenges posed by real-world problems.", "methods_and_evaluation_criteria": "The paper introduces a synthetic time series dataset with known KPs and applies CPAB warps to generate training pairs with ground-truth correspondences. It further proposes a self-supervised framework, TimePoint, for detecting and describing keypoints in time series data. However, the model's performance could potentially be enhanced by fine-tuning it on real-world datasets. As noted by the authors in Section 5, TimePoint's performance may be suboptimal if the signals deviate significantly from the synthetic distribution. Therefore, I strongly recommend incorporating a fine-tuning step on real-world data to ensure robust performance and adaptability to practical scenarios.", "theoretical_claims": "The proofs for theoretical claims are correct.", "experimental_designs_or_analyses": "I recommend that the authors conduct a robustness analysis of their proposed methods. The paper utilizes CPAB warps to generate training pairs with ground-truth correspondences. However, in real-world scenarios, some pairs may be incomplete. Introducing a study where certain points are removed from the pairs and evaluating the model's performance on incomplete data could enhance its robustness to various practical scenarios.", "relation_to_broader_scientific_literature": "The efficient and scalable alignment of time series is a critical research topic within the time series community. This study introduces a self-supervised method that significantly accelerates DTW-based alignment, offering key advancements and valuable insights to the field.", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "While CPA is an effective method for modeling nonlinear temporal distortions and generating correspondences between time series signals, I am concerned that CPAB may not be sufficiently comprehensive to model all types of time series distortions, particularly those exhibiting high complexity and variability. For instance, in the case of non-stationary time series, the parameter $\\theta$ in the CPAB transformation might evolve over time. I recommend that the authors conduct further studies to enhance their proposed method and ensure it can fully address all challenges posed by real-world problems.", "other_comments_or_suggestions": "In Section 4.2, KPs are selected by either applying a pre-defined threshold or choosing the top-K timesteps with the highest probability.\n -> In the experiments, please elaborate how sensitive the threshold to the model's performance.", "questions_for_authors": "While CPA is an effective method for modeling nonlinear temporal distortions and generating correspondences between time series signals, I am concerned that CPAB may not be sufficiently comprehensive to model all types of time series distortions, particularly those exhibiting high complexity and variability. For instance, in the case of non-stationary time series, the parameter $\\theta$ in the CPAB transformation might evolve over time. I recommend that the authors conduct further studies to enhance their proposed method and ensure it can fully address all challenges posed by real-world problems.\n\nThe paper introduces a synthetic time series dataset with known KPs and applies CPAB warps to generate training pairs with ground-truth correspondences. It further proposes a self-supervised framework, TimePoint, for detecting and describing keypoints in time series data. However, the model's performance could potentially be enhanced by fine-tuning it on real-world datasets. As noted by the authors in Section 5, TimePoint's performance may be suboptimal if the signals deviate significantly from the synthetic distribution. Therefore, I strongly recommend incorporating a fine-tuning step on real-world data to ensure robust performance and adaptability to practical scenarios.\n\n I recommend that the authors conduct a robustness analysis of their proposed methods. The paper utilizes CPAB warps to generate training pairs with ground-truth correspondences. However, in real-world scenarios, some pairs may be incomplete. Introducing a study where certain points are removed from the pairs and evaluating the model's performance on incomplete data could enhance its robustness to various practical scenarios.\n\nn Section 4.2, KPs are selected by either applying a pre-defined threshold or choosing the top-K timesteps with the highest probability.\n -> In the experiments, please elaborate how sensitive the threshold to the model's performance.", "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": "y0cZMkB7io", "reviewer": "Reviewer_qAHo", "summary": "This paper introduces TimePoint, a self-supervised framework for accelerating DTW-based time series alignment by learning keypoints and descriptors. TimePoint leverages 1D diffeomorphisms to model nonlinear temporal distortions, combined with fully convolutional and wavelet-based architectures to extract multi-scale features. Experiments demonstrate that TimePoint consistently achieves faster and more accurate alignments than standard DTW, making it a scalable solution for time-series analysis.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their kind words and suggestions. \n# Q1 - It is recommended to conduct experiments under the real-world dataset like PTB [1]\nAgreed, but this was already done in the paper. Please note that while TP was trained on synthetic data, all of the reported experiments were conducted on **real world datasets** of the UCR time series classification archive (i.e., section 6.2  **Classification on Real-World Data** and the results from Figures 7-8, and Tables 1-2).\n\nWe evaluated TP on more than 100 datasets of various data types, such as ECG, sensors, motions (action recognition for Wii remote, smart devices), Human activity recognition (HAR) and more. The datasets were contributed by different authors. \nSpecifically, the original dataset for \"ECG5000\", which is one those 100+ UCR datasets, is a 20-hour long ECG downloaded **from Physionet** (under the name “BIDMC Congestive Heart Failure Database”, see: https://www.timeseriesclassification.com/description.php?Dataset=ECG5000 ); i.e., the very dataset the reviewer mentioned. \n\nThe UCR archive is considered a gold standard for time series alignment and is widely used for evaluating alignment algorithms such as DTW.\n\nWe will make sure this is clearer in the revised manuscript. \n\nSince PhysioNet contains dozens of datasets, not all are applicable for TS alignment, we were unable to evaluate TP on further datasets (besides ECG5000) from this benchmark.\n\n### Please also see the **additional experiments** on real world data:\n1. **Robustness to Noise** experiment - Reviewer **zrdP**\n2. **Fine Tune on Real World Data** - Reviewer **QUAf**\n\n# Q2 - Runtime (Figure 8)\nYou are correct. The runtime for DTW and TP+DTW using 100% of KPS (the entire length of the time series) are almost identical. This is due to the low-overhead of TPs forward pass, which means that the runtime is almost entirely dominated by the time it takes to compute DTW. We will make this point clearer in the revised manuscript. Thank you for pointing it out.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "Yes", "experimental_designs_or_analyses": "Yes", "relation_to_broader_scientific_literature": "Not mentioned.", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "Strengths:\n\n1. The paper introduces an efficient time-series alignment framework which addresses the scalability limitation of traditional methods like DTW.\n\n2. Experimentally, the proposed TP is a robust and efficient method.\n\nWeakness:\n\n1. Since SynthAlign relies on predefined waveforms (e.g., sine, RBF), its performance on real-world data with complex patterns (e.g., high-frequency noise, non-stationary trends) is unclear. It is recommended to conduct experiments under the real-world dataset like PTB [1].\n\n[1] Goldberger, Ary L., et al. \"PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.\" circulation 101.23 (2000): e215-e220.\n\n2. In Figure 8, when using the full signal (L = 100%), the runtimes for DTW and TP+DTW are almost identical. This observation warrants further analysis and discussion of the results.", "other_comments_or_suggestions": "See Weakness.", "questions_for_authors": "See Weakness.", "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": "VQ9Y0gNufI", "reviewer": "Reviewer_zrdP", "summary": "The paper presents TimePoint, a self-supervised method for accelerating Dynamic Time Warping (DTW) in time series alignment by leveraging keypoint detection and descriptor learning from synthetic data. The main findings demonstrate that TimePoint significantly outperforms traditional DTW in terms of speed and accuracy, effectively addressing computational challenges and noise sensitivity in time series analysis.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their kind words and comprehensive response and suggestions. \n\n# Q1 - TP vs. SOTA time series alignment: robustness to noise and temporal distortions.\n\nTP was evaluated on 100+ datasets of the UCR archive. These datasets significantly vary in terms of noise and temporal distortion. In Section A. in the appendix, page 12, we show the model’s KPs and descriptors on three datasets that vary in length, frequency, domain, and distortion. Additionally, we believe the comparisons in Figure 7 and Table 1 provide a strong indication of TP robustness compared to state-of-the-art DTW-based method. While we test TP+DTW and TP+SoftDTW (Table 1), other alignment algorithms could also benefit from TP’s descriptors and KPs. Finally, we have performed a \"Robustness to noise\" experiment on real-world data, detailed below.\n\n# Robustness to Noise (Additional Experiment)\n\nWe conducted a robustness analysis to Gaussian Blur where $\\sigma\\in\\[0.1, 0.2\\]$ and Additive Gaussian Noise (a.k.a, Jitter) where $\\sigma\\in\\[0.1, 0.2\\]$. \nDue to the random sampling of noise, we repeat the experiment 3 times. Due to the large number of experiments per dataset (noise types X noise rates X method = 2 X 4 X 3) we use a subset of 30 UCR datasets. The results are presented below:\n\n\n|                  | no_noise | Blur ($\\sigma=0.1$)  |  Blur ($\\sigma=0.2$) | Jitter ($\\sigma=0.1$) | jitter ($\\sigma=0.2$) |\n|------------------|----------|--------------|--------------|---------|---------|\n| DTW              | 0.844    | 0.843        | 0.838        | 0.801   | 0.744   |\n| TimePoint(kp=0.1)| 0.867    | 0.866        | 0.853        | 0.804   | 0.760   |\n| TimePoint(kp=0.2)| **0.881**    | **0.873**        | **0.873**        | **0.828**   | **0.791**   |\n\n\n# Q2 - What specific metrics or criteria were used to evaluate generalization?\n\nWe have used the Nearest Neighbor classifier with DTW as distance measure (DTW-NN) to evaluate TP on real-world data. It is the customary benchmark for evaluating alignment algorithms (e.g., see the SoftDTW paper [Cuturi & Blondel, ICML ‘17]). \nIn more detail, DTW is performed between each test and train sample. The test data is labeled according to its closest neighbor label in the train set. Higher classification accuracy indicates that the similarity measure (DTW, TP-DTW, SoftDTW, etc.) is better. \nGiven that the similarity measure is fixed (DTW) compared with competing methods, the quality of the KPs and descriptors is the main variable measured for evaluation. \n\nWe will clarify this point in the revised manuscript. \n\n# Q3 - Limitations using synthetic data when generalizing to real-world data\n\nYes. As we mentioned in the limitations section (Sec. 5, Line 344), a key limitation is when the real-world data significantly differ from the synthetic data for SynthAlign. An example for such data is speech recordings. While the UCR holds some data that relates to speech recognition (see Appendix. A.1, Line 652 -Phoneme dataset), speech alignment and/or recognition is left for further research, as it requires special care (perhaps a combination of SynthAlign and text-to-speech generative model). \n\nTo further answer this question, we have conducted an experiment where we fine-tune TP on real world data. \nPlease see our answer to  Reviewer QUAf: \n**Fine Tune on Real World Data**.", "claims_and_evidence": "The claims made in the submission regarding TimePoint's capability to enhance the speed and accuracy of Dynamic Time Warping (DTW) are well supported by extensive experimental results demonstrating significant improvements over traditional DTW methods. However, while the authors provide convincing evidence of TimePoint's performance on synthetic data, further clarification on its generalization to a wider range of real-world datasets with varying conditions may strengthen the submission's robustness.", "methods_and_evaluation_criteria": "The proposed method, TimePoint, effectively addresses the challenges of time series alignment by utilizing a self-supervised learning approach that incorporates keypoint detection and descriptor generation, making it suitable for handling large and varied datasets. Additionally, the evaluation criteria, including extensive testing on benchmark datasets like the UCR Time Series Archive, demonstrate its robust performance and contextual relevance to real-world applications in time series analysis.", "theoretical_claims": "I reviewed the theoretical claims presented in the paper, particularly focusing on the proofs related to the efficiency of the TimePoint framework and its alignment accuracy.", "experimental_designs_or_analyses": "The experimental designs and analyses appear to be sound, particularly the ablation study which effectively demonstrates the performance improvements of the TimePoint framework across various datasets. However, further clarification on the selection criteria for datasets and the impact of synthetic data generation on real-world performance would enhance the validity of the findings.", "relation_to_broader_scientific_literature": "The key contributions of the paper, particularly the introduction of TimePoint for efficient time-series alignment, relate closely to existing literature in several ways:\n1) Dynamic Time Warping (DTW): The paper builds on the foundational work of DTW, which has been widely used for time-series alignment due to its ability to handle elastic shifts in the temporal axis. However, it addresses DTW's limitations, such as its quadratic time complexity and sensitivity to noise, which have been noted in prior studies.\n2) Keypoint Detection and Description: TimePoint adapts concepts from 2D keypoint detection methods, like SuperPoint, to the 1D time-series domain. This adaptation is significant as it addresses the unique challenges of time-series data, such as nonlinear distortions and amplitude variations, which have not been thoroughly explored in previous works.\n3) Synthetic Data Generation: The introduction of SynthAlign, a synthetic dataset designed for training keypoint detection and descriptor learning, is a novel contribution. Previous research has often focused on real-world datasets, while this paper emphasizes the importance of synthetic data for self-supervised learning, a concept that has been underutilized in time-series analysis.\n4) Continuous Piecewise Affine Based (CPAB) Transformations: The use of CPAB transformations to model nonlinear temporal warping is a significant advancement. While prior works have explored diffeomorphic transformations, they often lacked practical implementations for keypoint detection and descriptor extraction, which TimePoint successfully integrates.\n5) Efficiency and Scalability: The paper demonstrates that applying DTW to a sparse set of keypoints and descriptors leads to substantial computational speedups and improved alignment accuracy. This finding aligns with the ongoing research in machine learning and time-series analysis that seeks to enhance efficiency without sacrificing performance, as seen in methods like FastDTW and SoftDTW.\n\nOverall, the contributions of this paper not only advance the state of the art in time-series alignment but also provide a framework that can inspire further research in both theoretical and applied contexts within the broader scientific literature.", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "The paper introduces TimePoint, a novel self-supervised framework for keypoint detection and descriptor learning specifically tailored for time series data. This adaptation from 2D keypoint detection methods to 1D signals is a significant innovation, addressing a gap in existing literature.", "other_comments_or_suggestions": "None", "questions_for_authors": "1. How does TimePoint's performance compare to other state-of-the-art time series alignment methods in terms of robustness against noise and temporal distortions in real-world datasets?\n2. What specific metrics or criteria were used to evaluate the generalization capability of TimePoint when applied to diverse real-world time series data, beyond the synthetic datasets used for training?\n3. Can you elaborate on the potential limitations or challenges of using synthetic data for training TimePoint, particularly in terms of its applicability to highly variable real-world time series signals?", "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": "6CTGcg32Z7", "reviewer": "Reviewer_wuFf", "summary": "This paper proposes a keypoint and descriptor detection algorithm, called TimePoint, for time-series alignment. The authors use a deep learning approach to train a model on synthetically-generated data for detecting keypoints and descriptors. The correspondences learned by their model is passed into a DTW algorithm to perform the time-series alignment.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their kind words and suggestions. \n\n# Q1 - The scope of the proposed method\n\nWe appreciate the reviewer's concern regarding the scope of the proposed method. Time Series Alignment (TSA) has been a key research topic of time series analysis and machine learning in general. Machine Learning conferences such as ICML and NeurIPS publish several TSA papers at each conference. The original DTW paper has approx. 10K citations. Some key examples for papers on this topic include:\n* SoftDTW (Cuturi & Blondel, ICML 2017)\n* Drop-DTW (Neurips 2017)\n* Autowarp (NeurIPS 2018)\n* Diffeomorphic Temporal Alignments Nets (Neurips 2019)\n* Closed-form Diffeomorphic Transformations for Time Series Alignment (ICML 2022)\n* Regularization-free Diffeomorphic Temporal Alignment Nets (ICML 2023)\n\nWe believe the UCR archive, that was used for evaluation and includes more than 100 real-world datasets, is diverse enough in terms of modalities (ECG, motion sensor, shape outlines, and more). Thus, the concern that the training on synthetic data won't suffice for obtaining good results on real-world datasets is unwarranted: as we showed in the paper, the approach yielded SOTA results on real-world datasets. \n\nRE: other tasks - TP could potentially be extended to other tasks such as time series classification, clustering, anomaly detection, and more. However, properly addressing these tasks is beyond the scope of the current paper. These are indeed interesting research directions that are left for future works. \n\n# Q2 - Synthetic data training vs real-world data\n\nAn excellent point. We discuss this issue in lines: 065-073. \n\nWe agree with the notion that training and/or fine-tuning TP on real-world data could further improve the proposed method. The reasons we chose to focus solely on synthetic data are: \n1. We are not aware of real-world TS datasets with annotated KPs. \n2. Using only synthetic data mitigates license-related issues that might arise. \n\nWe will make this point clearer in the revised manuscript. \n\nThat being said, we have conducted more experiments on real-world datasets. Specifically, as per your inquiry and other reviewers' requests, we perform a fine tuning experiment where TP is pre-trained on synthetic data and then fine-tuned on real-world data. \nDue to space limitations, they appear in our answers to other reviewers: \n\nPlease see the **additional experiments**:\n1. **Robustness to Noise** experiment - Reviewer **zrdP**\n2. **Fine Tune on Real World Data** - Reviewer **QUAf**", "claims_and_evidence": "The paper claims that their keypoint/descriptor model trained on synthetic data yields more accurate and efficient alignment at a lower computational cost. This is because their model enables a sparser correspondence. In the experiments, they show that their method has better classification performance and runtime in terms of a kNN analysis with various baselines and the proposed method as the distance measure.", "methods_and_evaluation_criteria": "Yes, I believe the methods and evaluation criteria make sense for the problem domain.", "theoretical_claims": "The paper doesn't have extensive proofs, but the equations in page 6 make sense to me.", "experimental_designs_or_analyses": "Yes", "relation_to_broader_scientific_literature": "This paper builds on DTW and its extensions (e.g. softDTW, shapeDTW), which propose either different optimization strategies or handcrafted descriptors for correspondence matching. This paper is different in that it uses a deep learning strategy to learn descriptors and keypoints, where the deep learning model is trained on synthetically-generated data.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "Strengths:\n- The paper is well-written\n- The paper solves a clear problem and motivates their method well.\n- I think the results indicate that learning data-driven, sparse correspondences leads to improved performance, and this makes sense to me.\n\nWeaknesses:\n- My main weakness is the rather limited scope of this work. It is focused on the problem of time-series alignment which is one task in a specific modality of 1-d time series, and may not be sufficiently broad enough for an ICML audience. I wonder if the authors might consider applying this sparse correspondence approach to other tasks or modalities?", "other_comments_or_suggestions": "I'm curious why the authors approach the problem from a synthetic-generation standpoint. I'm not sure if it was mentioned (at least I could not find it) for why this approach was used, as opposed to, e.g. training on real data with augmentations. I feel like this approach would be more aligned with real data and learn correspondences that might be more transferable to real-world applications. Could the authors please elaborate on this design choice, and perhaps motivate this more in their paper?", "questions_for_authors": "N/A", "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:

Meta-review: This paper proposes TimePoint, a self-supervised method for accelerating DTW-based time series alignment by learning keypoints and descriptors from synthetic data. The approach is well-motivated, technically sound, and addresses the high computational cost of traditional DTW.

Reviewers highlight strong performance across over 100 real-world datasets, solid theoretical grounding, and a clear problem setup. Concerns about generalization, robustness to non-stationary signals, and reliance on synthetic data are valid but addressed effectively in the rebuttal. The authors provide new fine-tuning experiments, noise robustness results, and clarify the diversity of evaluation datasets.

While the scope is focused, the contribution is significant and addresses a core challenge in time-series analysis. The method is likely to be useful in a range of downstream tasks.

The paper presents a practical, validated contribution to scalable time series alignment.
Decision: Accept (poster)