Towards Editing Time Series
Authors: Baoyu Jing
Shuqi Gu
Tianyu Chen
Zhiyu Yang
Dongsheng Li
Jingrui He
Kan Ren
Venue: NeurIPS 2024 poster (2024)
Keywords: Time Series
Editing
Diffusion Model
Primary area: diffusion_based_models

Abstract: Synthesizing time series data is pivotal in modern society, aiding effective decision making and ensuring privacy preservation in various scenarios. Time series are associated with various attributes, including trends, seasonality, and external information such as location. Recent research has predominantly focused on random unconditional synthesis or conditional synthesis. Nonetheless, these paradigms generate time series from scratch and are incapable of manipulating existing time series samples. This paper introduces a novel task, called Time Series Editing (TSE), to synthesize time series by manipulating existing time series. The objective is to modify the given time series according to the specified attributes while preserving other properties unchanged. This task is not trivial due to the inadequacy of data coverage and the intricate relationships between time series and their attributes. To address these issues, we introduce a novel diffusion model, called TEdit. The proposed TEdit is trained using a novel bootstrap learning algorithm that effectively enhances the coverage of the original data. It is also equipped with an innovative multi-resolution modeling and generation paradigm to capture the complex relationships between time series and their attributes. Experimental results demonstrate the efficacy of TEdit for editing specified attributes upon the existing time series data. The project page is at https://seqml.github.io/tse.

Towards Editing Time Series
Baoyu Jing" 2 Shuqi Gu" Tianyu Chen', Zhiyu Yang',
Dongsheng Li", Jingrui He?, Kan R1
ShanghaiTTec University, Universitty of Illinois Urbana-Chann Chaamapgn,,,,,, Microsoft Research
Abstract
Synthesizing time series data is pivotal in modern society, aiding effective decision- making and ensuring privacy preservation various scenarios. Time series are associated with various attributes, including trends, seasonality, and external infor- mation such as location. Recent research has predominantly focused on random unconditional synthesis conditional synthesis. Nonetheless, these paradigms generate time series from scratch and are incapable of manipulating existing time series samples. This paper introduces novel task, called Time Series Editing (TSE), to synthesize time series by manipulating existing time series. The objec- tive is to modify the given time series according to the specified attributes while preserving other propertics unchanged. This task is not trivial due to the inade- quacy of data coverage and the intricate relationships between time series and their attributes. To address these issues, we introduce a novel diffusion model, called TEdit. The proposed TEdit is trained using a novel bootstrap learning algorithm that effectively enhances the coverage of the original data. It is also equipped with an innovative multi-resolution modeling and generation paradigm capture the complex relationships between time series and their attributes. Experimental results demonstrate the efficacy of TEdit for editing specified attributes upon the existing time series data. The project page is at https://seqmll.iithe github.oo/tse
Introduction
Time series data analysis plays a crucial role in various modern business sectors, including climate monitoring [22] 13 14. healthcare 2 3 46,, urban management 43 16 15). and online ser- vice [35). Time series are derived from diverse sources with inherent characteristics, such as system configurations, alongside external influential factors like environmental status. These factors are considered attributes of the time series. many real-world scenarios, such as healthcare and urban monitoring, time series data tend to be sparse and srivacy-sensitive, particularly concerning specific attributes that are rarely observed real-world scenarios.
Synthesizing time series data has emerged as a prominent research area aimed at addressing these challenges. Existing methods for time series synthesis range from unconditional generation [47 3] to conditional generation 339 27 4]. Unconditional generation produces outputs based solely on the underlying distribution of the data. Without relying any input conditions, the generated results are highly uncontrollable. Conditional generation controls the outcomes of the generated data based on the input conditions. However, conditional generation tends to produce samples around the data mean [40], as demonstrated in our experiments, which could easily overlook detailed characteristics of the data, such as high- tigh-frequency patterns and local structures. Both unconditional and conditional generation paradigms synthesize time series from scratch, and they are incapable of manipulating existing samples. As a result, these paradigms are unable to answer "what if"" questions in time series synthesis: given time series, what would it become if some of its attributes are modified?
"aoyy Jing Shuqi Gu share the co- first authorship. "Correspondence to Kan Ren: renkan @ shanghaitech.cdu.cn
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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Attribute
City
Attribute
City
Valuee
Lonson
Spring
Value
London
Summer
"London summer
Model
Conditional Generation
Conditional Generation
Time Series Editing
"London
Model
Series
Editing
"London spring"
Two time series generation paradigms
Illustration of the time series generation paradigms. Conditional generation generates time series from scratch, which usually generates samples around the dataset mean. Time series editing allows for the manipulation of the attributes of an input time series sample, which aligns with the desired target attribute values while preserving other properties.
Figure 1:
In this paper, we introduce a novel task, called Time Series Editing (TSE), for sample-level time series manipulation. More specifically, our objective directly manipulate specific attributes of an input time series target values while maintaining consistency in other characteristics. For instance, consider observing air quality under season conditions in different cities as shown in Fig. T On the left side, two example air quality time series for the spring and summer of London are illustrated. The spring one exhibits a lower amplitude with more noise, whereas the summer one features higher amplitude with less noise. What would the resulting time series look like if we modify the season attribute of the spring one to summer while maintaining all other properties? As illustrated Fig. it could have larger amplitude with much noise.
The task of TSE is complex due several challenges. Firstly, the time series data distribution over the full composited attribute space is biased and may not be adequately covered, leaving gaps in our understanding, especially concerning unobservable example, in climate data analysis, attributes like temperature and humidity are observable and well-defined. However, attributes like atmospheric pressure variations or localized microclimates may be challenging to observe or define accurately. Secondly, different attributes influence time series at varying resolutions. For example, trends have global impact, while seasonality exerts more localized influence. Modeling these multi-scale attributes and time series associations, while effectively controlling them, presents significant difficulties.
poorly defined attributes. For
To address these challenges, we introduce a novel method called Time Series Editor (TEdit). which is based on predominant generative models, specifically diffusion models [10] 38 39 27 To address the data coverage issue, we propose a novel bootstrap learning algorithm, which leverages for subsequent model learning. This algorithm helps
the generated data as improve the coverage of the whole attribute space and enhance the generation performance. To capture the intricate multi-scale associations between time series and attributes, introduce multi-resolution modeling and generation paradigm. The proposed multi-resolution paradigm can manipulate the given time series and attributes both effectively and efficiently. Our experiments, conducted on both synthetic and real-world datasets, demonstrate the effectiveness of our proposed solution. Specifically, our approach excels in generating precise time series under specified attributes, while keeping consistency in other attributes, showcasing the practical utility of our method.
In summary, our main contributions are threefold. We introduce and formulate the novel task of Time Series Editing (TSE). To the best of our knowledge, this is the first work exploring editing time series according to the given attribute configuration upon the input data. (2) We propose novel multi-resolution diffusion model, called TEdit, with a bootstrap learning algorithm, which can flexibly capture the patterns diverse granularity and gradually improve the generation performance. (3) We conduct and release benchmark for TSE, including synthetic data and several carefully curated real- world datasets with comprehensive evaluation protocol, aiming to facilitate further research in the community.

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2 Background
2.1 Time Series Generation
A time series sample x E IR is a series of L chronologically ordered sample points. Each time series sample adheres to some attributes
N such as trend and the cycle number, and the k-th item a corresponds to N₂ categorical options where & E (1.... .... K}. In real-world scenarios, each time series may have several attributes, e.g.. the air quality time series associated with K attributes: location and season. Note that, not all the attributes can be observed due to practical limitations.
Before describing the main task of the paper, we first briefly review the background of Time Series Generation. Unconditional Time Seriey Generation (UTSG) samples the time series data x upon the modeled data distribution p(x) such that x p(x), of which the generation process is uncontrollable, Conditional Time Series Generation (CTSG) generates the time series sample based on the conditional distribution such that x p(x(a), where a is the input condition. CTSG tends to produce data samples near the dataset mean [40]. Both UTSG and CTSG paradigms generate time series from scratch and lack the ability to directly manipulate the existing samples.
2.2 Conditional Difffusion Models
Diffusion models 10 37 38
learn to estimate and remove the random noise, which was added in the forward process onto the real-world data, through sequence of sampling processes. Specifically, during training. random noise is gradually added to the original data sample Xo = x via a Gaussian Markov transition p(x/|x;-1) := (N[VI- -, 3,I), where [1,T] indicates the diffusion step, and { are the predetermined variance schedule. The expression of latent variable X can be simplified as x, = Vaixu + /1 ayc, c <~ ((,,), a II's=1( component of the diffusion models is a noise estimation network cØ (x,, t, a), where a denotes the additional condition such as attributes. EØ Ex(x,, a) is trained by estimating the noise added Xy:
3,). The learnable
min ((xo) == min (O.) ||c
co (x,,t,))1e
where No ((xo) is sampled from the real data distribution, x, a noisy version of Xo-
After training, given an attribute a, we can generate a sample & from random noise &y ~ ~N I) us- ing Eo (xex, f, a) with sampler, e.g., deterministic Denoising Diffusson Implicit Model (DDIM) 38
x = 1 f (K, a) + v1 16011 a,
(2)
fx f, a) = (x: vI 0460 (Xe, f, a)) (,))
Va.
(3)
where fo(X;: 1, a) can be regarded as a prediction of Xo at the diffusion step I.
Adapting diffusion methods from the computer vision domain to the time series domain is not trivial. Time series different from images, and poses unique challenges for modeling and generating time series data, such as the complex multi-scale entanglement between time series and attributes.
3 Time Series Editing
Given a time series and its associated attributes (x, a), natural question arises: "what would become if some of its attributes are modified?" In our work, we take novel perspective of directly editing the given time series with the specified attribute modifications.
In this section, we first formally formulate the problem of Time Series Editing (TSE) in Sec. 3.1 Next, we present our method Time Series Editor (TEdit) with the overall procedure of the diffusion basel-based TSE in Sec. 3.2 the model architecture in Sec. 3.3 and the learning algorithm in Sec. 3.4
3.1 Problem Formulation
Recall that, each time series sample E IR is associated with set of attributes NK Each attribute ag has N₂ options, & E (1, K}. For example, the trend type may contain N₁ = 4 values
Xo and interchangeable in this paper.
3

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StC
Lineer
Down
Linear
Cycles
Cydles
Forward DDIM
DDIM
x
Edit
Self-Score
Score
Forward
DDIM
Reverse DDIM
Attributes
Cycles
%
23
Cycles
Preserve Aguv
Edit Aeti
Figure 2: Illustration of the editing function 2g (upper) and the process self-scoring the generated X for bootstrap learning (lower). Upper: te (xp a al first encodes the source (xo a into the latent viable x7 via the forward DDIM Eq. 4), and then decodes x with the target attributes all: (xTT a into tet via the reverse DDIM in (5) See Sec. 3.c. for more details. Lower: during bootstrap learning, use ty to self-score the generated x by editing xi) back Xg and obtain the score MSE x()), see Sec. 3.4 for more details.
like linear, quadratic, exponential, and logistic. Let source attributes to be edited, and a be the desired target attributes. Comparing a and a there are Kedit edited attributes, e.g., trend types, and K pasv preserved attributes, e.g., cycle numbers, where Kedit + Kpesy K. Now, we can formally define task as below.
be pair of source time series and
Definition (Time Series Editing (TSE) The time series editing task is build a function Dy to generate a larget time series & "0(x" s a a by modifying the set of Kedi edited attributes Aedi and mainlaining the set of K prsv preserved attributes Apray well other information of xecc.
3.2 Editing with Source Modeling and Target Generation
Given a pair of source time series and attributes (x"re a generating the target time series * corresponding the target attributes a entails two requirements. On one hand, the edited attributes Aedit should satisfied after the generation. On the other hand, the preserved attributes Aprev as well as other characteristics, c.g., noise, need to be maintained. Conditional generation (27]. such as directly adopting the conditional diffusion model described in Sec. 2.2 would fail the editing task because does not take into consideration the detail characteristics of the source time series.
In this paper, we propose diffusion based two-stage procedure for TSE: ( a a which is illustrated in the upper part of Fig. 2 In the first stage, encode both the attribute semantics [6] and the detail characteristics [10] of the source time series x into the latent variable xt via the deterministic forward DDIM process [8]
fo aT + V 04-11 (T ".).
(4)
1+1
In the second stage, originating from the latent variable X target time series 1gt with the consideration of the target attribute as via the deterministic reverse DDIM process [38]
lgt
xx gradually generate the final
0r fo ( VT az a (x a
(5)
where fg is given in Eq. Till now, have detailed the procedure of the proposed TEdit. In the following subsections, we introduce the model architecture and the training algorithm.
3.3 Multi-Resolution Noise Estimator
The key component of our TEdit is the noise estimator eg (x a) in Eqs. 4 Though many diffusion model realizations have been proposed in other fields [18 37 and the time series
domain [39 27, found them inefficacious in modeling and generating time series data, especially

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Input noisy time series
Multi-Pathh Encoder
Mult-Patchh Decoder
Model forword
Estimated noise
1xl
1xt
Input attributes 1xX
Diffusion
1x1
MLP
Patchifly
3KL
DX
D
Processing Module
DXN
Resolution
Dxc1g
DXN
Noise Estimator
DX(N) + Mg NEO
DX(N) Ng++
Architecture of the proposed multi-resolution noise estimator Eg. We illustrate with patching schema, patch length 1, = 2"-1, {1,..., R) and the input length I. = 8. N2
Figure 3:
L-L,
the patch number. D is the embedding size, Please refer to Sec. 3.3 for details.
when considering attributes. Few of them consider that different attributes can influence the time series on varying scales. We argue that it is important to take into account these differences. For instance, the trend types have global impact on time series, while the seasonality affects the time series in local areas.
In this paper, propose multi-resolution noise estimator. It first slices the input time series x, into several patch sequences of different resolutions E {11, {1,... R}. where R is the total number of resolutions. Then it processes the patch sequences along with other information, c.g., attributes a and diffusion step f, to estimate the noise é; for resolution r. Finally, the estimated noise for different resolutions are mixed together to obtain the final estimated noise éG An illustration presented in Fig. 3
We elaborate on the details in the following conten
Patchifying. Following 29 we patchify the input time series x E R., into sequence of patch tokens P (PI, PN, where p; E Ty T.p the window size and Np = | ""=!" the patch number. After that, encode them into embeddings P (p:,..... where p. E ERD.
Multi-Resolute Modeling and Generation. To model multi-resolution patterns and better control the conditional generation at multiple scales, we propose a multi-patch design with various patch lengths. Specifically, as shown the left part of Fig. 3 following the above patchifying operation, we encode the input time series x into patch embedding sequences (P" j of R resolutions, where
P = [pi P J. P. E RD R. No - is the patch number, 4 is the window size. We
set the patch length as 4 = to produce exponential receptions, where E N4 is the base. Thereafter, the processing module operates self-attention the Transformer 42 to capture the input patterns for each resolution r, and it incorporates the attribute a and diffusion step information to produce the embeddings {Pr}._ The details of the processing module are presented in Appendix D
The processed output [Pr),R_ is still the form of multi-resolution sequences. The model subse- quently decodes the diffusion noise at different resolutions back into the original space and integrates the outcomes at different resolutions to produce the final noise estimation, as shown on the right side of Fig. Specifically, for each Pr, the decoder transforms to è" E RR_ in the original time series space. The final estimated noise is obtained via mixing upon the concatenation of estimated noise of different resolutions as é MLP((E) .... e e'I), where MLP(.) denotes Multi-Layer Perceptron.
Parallel Processing. Though more effective, the proposed multi-resolution modeling and generation brings efficiency issues since it derives multiple token sequences with various sequence lengths. Iteratively processing each sequence is of low efficiency. Herein we design a novel parallelization
We discuss the noise estimator in the diffusion step here, thus, we omit the notation of the diffusion step t without causing confusion.
5

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with an attention masking mechanism in the processing module in Fig. to harness the parallelism of the Graphies Processing Unit (GPU). Specifically, we first concatenate the imput patch embedding
sequences of different resolutions into a single vector P = [P] oone P E R +N....... When calculating pair-wise self-attention scores as in the Transformer
we use a mask matrix mask out the seter-ee attention operations across different sequences while preserving sequene attention. Please refer Appendix D for more details.
3.4 Bootstrap Learning Algorithm
Although any pretrained conditional diffusion model can be directly used to perform editing as shown in Sec. 3.2 the pretrained model is an essentially conditional generator, which has great ability in generating time series directly from attributes but it might be less effective in modifying the attributes of an existing time series. The simplest way to improve the model's ability is to finetune 69
via the ground-truth source (x3 a²m)) and target (x) (t a!*) pairs. However, the imaginary target xo which satisfies both a and the details of x might be very rare or even does not exist in the real tet world. Thus, key challenge for finetuning is how to effectively learn the information of X
Fortunately, the pretrained model is capable of generating some edited samples of certain quality via X (xx"" at" ). An example is shown in Fig. 2 In this paper, we propose bootstrap learning algorithm, which first pretrains Eg based on the noise estimation loss in Eq. ① and then finetunes eg based on X with top confidence scores. Here, we briefly present the key steps of the bootstrap learning, and the full algorithm is given in Appendix E Given batch (X0 A )²), where X E
R BxL, A E N B is the batch size, the finetuning works as follows:
Compose target attributes lst For each ,x,,, a E (X5e A ), we compose the imaginary target attributes a based a by randomly sampling values for edited attributes Aodit and keeping the values of the preserved attributes Apwv. Then we have the tuple (X are A
Generate the edited time series x via 4g. An illustration is shown in the upper part of Fig. 2
Self-score X via d and keep top s samples. An illustration of the self-scoring process is
shown in the lower part of Fig. 2 We first edit X back to source Xee t [ A tet A )c). Then we use the Mean Squared Error (MSE) between X and X to score ty The top e
Xig samples of X with the lowest MSE are selected as the bootstrap samples, denoted by 0,bs
Update Eg by minimizing Las- We finetune Eœ by minimizing the noise estimation loss L in 1gt Eq. for 0,bs Formally, minimize Las -(X
4 Experiments
In this section, we present experiments aimed at addressing the following research questions: RQ1: How does the proposed TEdit perform terms of editing and preserving attributes? RQ2: What's the impact of bootstrap training? RQ3: What's the impact of multi-resolution modeling?
4.1 Experimental Setup
Datasets. We collect three datasets for TSE, including one synthetic dataset and two real-world datasets. For the Synthetic data, each time series sample has a length of 128, and is associated with 3 attributes, where there are 4 trend types. 2 trend directions. and 4 season cycles. In addition to attributes, noise and bias added to simulate real-world conditions. The Air Quality [8] dataset contains PM2.5 time series of Beijing and London from 01/01/2017 to 31/03/2018. Each time series has a length of 168 and is affected by two attributes: 2 cities and 4 seasons. The Motor Imagery MI] dataset contains the DeectrrEneephaloGram (EEG) data of subjects, who were required to imagine the movements of either the tongue the left small finger. Each sample takes a length of 150 with attributes: 2 movements and 64 channel ids.
Fach of the three datasets is associated with pertaining dataset and several finetuning datasets.
The pretraining datasets only contain the source time series x and attributes a During pertaining, the noise estimator Eg is trained to denoise x5 with are via Eq. (I. Each finetuning

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dataset corresponds to specific split of Asdit and Apsv. For example, the Synthetic dataset has 3 attributes, and thus has 6 - = different splits, namely, finetuning datasets. Similarly, both Air Quality and Motor Imagery datasets have 2 finetuning datasets. Finetuning datasets contain the source time series xsc, source attributes a and target attributes a The process of generating the target attributes mirrors the target attribute composition step in bootstrap learning. Note that for Synthetic, the target time series x is also available. During finetuning, we use the proposed bootstrap learning algorithm to further improve the model. More details can be found in Appendix F
Evaluation Metrics. A good editor should be able to generate x that aligns with each a E als! The cornerstone of our evaluation is the Contrastive Time series Attribute Pretraing (CTAP) model, which extracts time series and attribute embeddings by learning their alignment. Our CTAP is similar to CLIP 33 For details of CTAP, please refer Appendix G Based on the CTAP model, we introduce two metrics: CTAP score and Log Ratio of Target-to-Soure (RaTS). (1) The CTAP score is similar to the CLIP-I score [3 33,, which measures the alignment between the generated time series and the real-world time series which are associated with the given attribute value d Specifically, we first use the CTAP model to extract embeddings fix, which is the embedding of x and hx dg which is the average embedding of the time series associated with ag in the training data. Then we calculate the cosine similarity of fix and h ba where the higher similarity indicates better aligmment.
1st
1st (2) The RaTS score measures whether & is closer to d than x Formally, the
RaTS score for tuple (x xxe, ag
ug is defined as:
p( 181 X &
RaTS( x .a log(
p(ag Xxar))
where p(ag)* x)) is calculated by applying a softmax over the similarity scores of all (hx. hag=)) pairs, where hy is the CTAP embedding of x, and haz=i, E {I,..., N.), is the CTAP embedding of the i-th possible value for the k-th attribute ag.
We mainly evaluate s from two perspectives: Aodi and preserved attributes Apsv. respectively. For the edited attributes Aedi. the higher RaTS and CTAP the better. For the preserved attributes Agrvv the higher CTAP the better. Since RaTS can take negative values, we use IRaTSI for Apr. which measures the semantic divergence of x!at from a w.r.t og- The lower IRaTSI means the better preservability. For more details, please refer to Appendix|i H In addition to the semantic level evaluations, for the synthetic data, since we have ground truth target time series, also use Mean Square Error (MSE), Mean Absolute Error (MAE) to perform the point level evaluation.
editability and preservability for edited attributes
Compared Methods. Since there is no existing method for TSE, we modify the popular time series diffusion model CSDI (39 and the recent conditional diffusion model Time Weaver [27] for TSE. As CSDI is unable to process attributes, we add extra attribute encoder to incorporate attribute information. For Time Weaver, we slightly modify its attribute encoder for our settings. After pretraining the modified CSDI and Time Weaver, we use them to edit time series via the editing procedure described in Sec. 3.2 Our proposed TEdit-CSDI and TEdit-TW are implemented by incorporating core processing modules of CSDI and Time Weaver into our proposed multi-resolution noise estimator Sec. 3.3 and trained via Sec. 3.4 Architecture details are presented in Appendix D
Implementation Details. For all experiments, we set the number of diffusion steps T = 50, embedding size for attributes and time series as 64, and use Adam optimizer (20 to train the model. For pretraining. set (batch size, learning rate) as (256, Ie-3); for finetuning, (64,1e-7) for Synethtic and (32,1e-7) for Air Quality and Motor Imagery. We conduct a grid search for the hyperparameters of the multi-resolution. Considering the balance between performance and efficiency, we chose a compromise ratio for bootstrap. Specifically, (R, T.p, 1/) = (3. 2, 0.5) for Synthetic, 23, 0.5) for Air Quality, (3,3, 0.5) for Motor Imagery. All our experiments were conducted on a single Nvidia-A100 GPU.
set them as
4.2 Main Results
In this section, we quantitatively evaluate the performance of comparison methods for editing and preserving attributes (RQ1) for all the datasets. For the edited attributes Aedi. we report the RaTS and CTAP scores to depict the models' ability to edit attributes. For the preserved attributes Apev.
7

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Synthetic
Air
Motor
Overall
Edised
Preserved
Edited
Preserved
Edited
Preserved
JMSE
1MAE
fRaTS
1CTAP
LIRaTSI
TCTAP
TRaTS
1CTAP
(JRATSI
[CTAP
[RaTS
1CTAP
UIRTTSi
1CTAP
CSDI
0,1789
0.3221
0.7540
0.5405
0.1439
0.7898
0.7452
0.1581
0.1705
0.6311
0.0939
0.4203
0.1597
0.6617
Weaver 0.1454
0.2898
0.9030
0.6943
0.1169
0.8292
0.8956
0.3266
0.1866
0.6299
0.0979
0.4168
0.1520
0.6691
TEdit-CSDI
0.1235
0.2606
0.9257
0.7109
0.1021
0.8553
0.8022
0.2179
0.1614
0.6529
0.1016
0.4186
0.1580
0.6654
TEEH-TW
0.1315 0.2722
1.0121
0.7957
0.0995
0.8622
0.9661
0.3930
0.1916
0.6274
0.1212
0.4348
0.1571
0.6621
Table 1: Averaged performance over all finetuning sets for Synthetic (left), Air (middle), and Motor (right). "Edited" and "Preserved" are the average results of all edited and preserved attributes.
Synthetic
Air
Motor
Overall
Edited
Preserved
Edited
Preserved
Edited (RaTS 1CTAP
Preserved
[MSF
[MAR
"RaTS
1CTAP
(URTTSI
1CTAP
|RRTS 1CTAP
[IRATSI 1CTAP
URRTSSI 1CTAP
TEdi: TW w GT
0.1233
0.2622
1.0165
0,7991
0.0984
0.8650
TEdit-WW
0.1315 0.2722
1.0121
0.7957
0.0995
0.8622
0.9661
0.3930
0.1916
0.6274
0.1212
0.4348
0.1571
0.6621
who BS
0.1376 0.2793
1.0127
0.7952
0.0962
0.8632
0.9524
0.3792
0.1839
0.6418
00.1113
0.4289
0.1571
0.6658
w/a BS & MR
0.1454 0.2898
0,9030
0.6943
0.1169
0.8292
0.8956
0.3266
0.1866
0.6299
0.09788 0.4168
0.1520
0.6691
Table 2: Ablation studies on the Synthetie, Air and Motor datasets. GT, BS and MR refer to
Ground Truth source and target pairs, BootStrap and Muutti-Resolution. Results are averaged over all finetuning sets. "Edited" and "Preserved" are the average results of all edited and preserved attributes.
we report the IRaTSI and CTAP scores to depict the models' ability to preserve attributes. For the Synthetic dataset, since we have the ground truth, also include MSE and MAE to evaluate the overall disparity between the ground truth x and the generated x
In Tab. 1 we present the averaged results over all the finetuning sets for each dataset. The detailed results for each finetuning sets are presented in Appendix [..]] Firstly, for the overall performance (MSE and MAF), the proposed T'ditt could significantly outperform baselines. Secondly, for the edited attributes Acdit: TEdit-CSDI and TE could respectively outperform CSDI and Time Weaver on RaTS and TAP, showing that x generated by TEdit could better fulfill the desired target Asdit- Thirdly, for the preserved attributes Aprv. TEdit could basically maintain IRaTSI and TAP scores, which indicates TEdit is capable of preserving Agnvv In summary, these observations show that TEdit, including the multi-resolltion and bootstrap learning, could improve the ability of conditional diffusion models to edit Aedi and preserve Aprsv
4.3 Ablation Study
In this subsection, we conduct ablation studies on the Synthetic and Motor datasets based on the TEdit- TW to investigate the impact of the proposed multi-resolution modeling (RQ2) and the bootstrap training algorithm (RQ3). The averaged results over all the finetuning sets are presented in Tab. 2
Firstly, we compare TEdit-TW trained with BS (BootStrap) with TEdit-TW trained with GT (Ground Truth) on the Synthetic d For GT, we finetune TEdit-TW by minimizing MSE between the generated & and the ground truth x!er. It can be observed that GT performs much better than BS on the overall metrics (MSE and MAE), and it is only slightly better than BS on the attribute level metrics for both edited and preserved attributes, demonstrating that BS has a strong ability to capture the attribute semantic information. Secondly, we remove BS from TEdit-TW and compare its performance with the full TEdit-TW model. For the Synthetic dataset, BS could improve the overall scores, and maintain the performance on other attribute level metrics. For the Air dataset, BS primarily improves the scores of the edited attributes, with trade-off in the scores of the preserved attributes. We hypothesize that there might exist some intrinsic connections between the two attributes: city and season. For the Motor dataset, BS can enhance the performance on the edited attribute while maintaining the performance on the preserved attributes. Thirdly, we investigate the contribution of MR (Multi-Resofutionn For the Synthetic and Air datasets, MR could improve the scores all metrics. For the Motor dataset, MR helps improve the scores for the edited attributes and mostly sustains the performance on the preserved attributes. These improvements highlight the effectiveness of the proposed MR.
Ground truth is only available in the Synthetic dataset.
8

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4.4 Extended Investigation
We further conduct an in- in-depth analysis of the effectiveness and efficiency of our TEdit, aiming to reveal how it works through quantitative comparison and visualization.
Editing vs. Conditional Generation. Although conditional generation x = (0 0, a can produce time series that satisfy the specified target attribute values, it often struggles retain sufficient details, particularly for difficult or unobservable attributes, since it generates samples from scratch. In contrast, editing ~ (x(x>> a a!*)) is designed to modify the attributes of existing time series, and thus is able to preserve much detail information. We first quantitatively compare the two modes on the Synthetic data. The averaged MSE and MAE over all the finetuning datasets presented in Tab. 3 which shows that editing could significantly outperform conditional generation with much better (lower) MSE and MAE scores.
Trend Type: linear quad & Season Cycles: -
Trend Type: log linear
Edi RED CP47
EliiMSE: è 0383
Synthetic
Conslpon
Method
Mode
MSEJ
MAEU
0:-
TEdit-SDD
Cond. Gen.
0.2581
0.4096
Edit
0.1235
0.2606
-03
-12
Condcond
Cond. Gen.
0.2875
0.4308
TEdit-TW
Edit
0.1315
0.2722
6-3
(a)
Figure 4: Case study for editing and conditional genera- tion, under two settings: (a) editing the trend type from logistic to linear; (b) editing the trend type from linear to quadratic and the season cycles from to 4.
Table 3: Quantitative comparison of edit- ing and conditional generation on the Synthetic dataset. The results aver- aged over all the finetuning sets.
We further showease how editing and conditional generation work at the sample level. Fig. plots the generated results of these two modes under. We can observe that the edited time series could better align with the target attributes, Fig. 4 (b)(b), and preserve the characteristics of the source time series, e.g., bias in Fig. (a). In comparison, conditional generation tends to produce smooth samples around the dataset mean. Additionally, we can also see in Fig. that the MSE scores editing are much lower than conditional generation. More visualization results can be found in Appendix I.4
Efficiency of Multi-Resolution Generation. Considering that multi-resolution modeling and generation brings additional efforts for process- ing different resolutions, as discussed in Sec. Sec.
Running time (ms) Serial processing
Parullel processing
Noise estimator
11.1
6.0
Processing module
8.8
3.7
Table Running time (ms) of the multi-resolution modeling and generation module, averaged 1000 samples.
We compare the running time of serial process- ing and parallel processing implementations of the multi-resolution paradigm with total resolu- tion number R = 3 and patch length L; r {O...., R 1}. The experiment is conducted on the Synthetic dataset with a batch size of 32. The serial processing iterative processes a single resolution at time, whereas the parallel processing concatenates multiple resolution sequences along the length dimension and processes them simultaneously. We record the time it takes for the full noise estimator and the processing module to complete the forward diffusion process. As shown in Tab. 4 our designed parallel mechanism can significantly improve the inference efficiency.
Bootstrap Improves the distributional coverage of the attribution space. Fig. 5 visualizes the dimension reduced distribution of (a) the raw data, (b) the generated data and (c) the mixed data, on the Synthetic dataset using t-SNE [41]. We can observe that the newly generated data fulfill the uncovered region of the original raw data therefore enhance the data coverage over the whole space, which explains how the bootstrap learning helps generative model training. More visualization analysis can be found in Appendix
4.5 Sensitivity Analysis
In this subsection, we conduct sensitivity analysis to study the impacts of hyper-parameters of the multi-resolution mechanism and bootstrap learning.

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Orginal Quta
Bodisropatt Dam
Bootzraparng Dass
44
78
3.8
Ompinel Daln
14
3A
04
30
0.0
044
0.8
02
64
9.6
20
-22
6.2
02
2/4
0.5
2.8
20
1.2 -22
60
92
1. 6.
16
12
(e)
(b)
(c)
Figure 5: Visualization of data distribution before and after bootstrapping.
the proposed multi-resolution i.e., the patch length Lp and the number of resolutions R, on one Synthetic finetuning set, which aims edit trend directions while preserving other attributes. The results are presented in Fig. 6 More results on other Synthetic finetuning sets can be found in Appendix [.2
Multi-resolution hyper-parameters. We perform a sensitivity study for the
To analyze the impact of R. we fix Lp 2 and vary R. As shown in Fig. 6 (a), trend types prefer R 3, whereas seasonal cycles perform better with R = 4. To examine the impact of Lpp. we fix R 3 and vary Lp. According to Fig. (b). trend types show a preference for Lp
while seasonal cycles prefer Lp 3. Besides, we find that our proposed multi-resolution paradigm always performs better than the vanilla single resolution time-series modeling paradigms. i.c., Lp
R which corroborates the discussion in Sec. 3.3 that attributes may influence the time-series generation different scales and granularities.
Bootstrap learning hyper-paramen parameters. We conduct sensitivity study on the hyper-parameters of bootstrapping. specifically the bootstrap ratio 1 within the range (.31, 0.5, 0.7, 0.9} on Synthetic finetuning set, which aims to edit the trend direction while preserving others. As shown in Fig. 7 the model exhibits poor performance then the bootstrap ratio is low, e.g., 0.1. The model has a good performance with medium to high bootstrap ratios. We hypothesize that this is because tuning with only a few top samples might lead to certain mode collapse.
Editing trend directions
0.0455
MSE
0.004
00023
00823
00023
24
0.
0.
3.5
83
Bootstrap ratio
Figure 6: Sensitivity study for the papee-parameters of multi- resolution on the Synthetic finetuning set for editing trend directions and preserving others. The higher CTAP, the better.
Figure 7: Sensitivity study of boot- strap ratio on a Synthetic finetun- ing set aiming to edit trend direction.
5 Conclusion
In this paper, we introduced the task of Time Series Editing (TSE), which enables controllable time series synthesis by manipulating specific attributes of the input time series while maintaining consistency in other properties. There are two major challenges TSE. Firstly, the real world, the distribution of time series and attributes is usually biased, and the full space might not be adequately covered. Secondly, time series and attributes exhibit complex multi-scale entanglement. To address these two challenges, propose novel diffusion based approach, called TEdit, which incorporates bootstrap learning algorithm and a Comprehensive experiments both synthetic and real-world datasets demonstrate the effectiveness of our proposed TEdit in generating precise time series with specified attributes. In practice, our method still has certain limitations. For example, different attributes have varying difficulty degrees to edit; attributes may have complex inter-dependencies, and some attributes may be difficult to edit without affecting others. Despite this limitation, believe our work lays the groundwork for controllable time series synthesis, potentially benefiting applications in fields such as climate monitoring, healthcare, and urban management.
modeling and generation paradigm.
6 Acknowledgment
This work supported by HPC Platform of ShanghaiTech University, Shanghai Frontiers Science Center of Human-centered Artificial Intelligence, and MoE Key Lab of Intelligent Perception and Collaboration. We would like to express our sincere gratitude for their valuable support and resources that contributed significantly to the success of this research.
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A Related Work
Time Series Generation. Time series generation has become research hotspot in recent years From the view of modeling, early attempts for time series generation utilize Generative Adversarial Networks (GANs) 47 311, which trains the generator adversarial to the discriminator of correct classification on real or synthetic data. The latest research moves the focus to Diffusion models 39 which have illustrated much higher fidelity and generation performance than GANs 45 From the perspective of the generation paradigm, there are two branches. The first
(DMs) 127
36
one is unconditional generation [47 31 34 48). which employs a generative model to generate data samples without relying any conditions for regulation. The second paradigm is conditional making use of additional conditional information like category labels, textual descriptions, features, and other kinds of metadata guide the generation process and enabling high-quality generation that is tightly correlated with the conditions. Among them, CSDI 39 enhances time-series imputation by treating observed time series conditions in the diffusion model. TIME WEAVER [27] directly encode the metadata into the diffusion model to achieve controlled generation. Though they achieved better quantitative results, these methods suffer from unexpected outcomes that inadvertently emphasize trivial attributes while overlooking critical ones, especially with low availability of time-series data over various attributes.
generation 39 27
36 44
Diffusion Model. Diffusion model 10 37 has become increasingly prominent for data generation across various fields [26] 2
21), which learns to gradually regress and remove the noise that was added to the clean data in the forward process, allowing data generation aligned with real distribution. Our work highly related to conditional generation, which can be divided into two main streams of techniques. Classifier-guided generation [6] 19 24 adds the control signal from the gradient of the classifier to bias the generation towards the specific class in the denoising process of the diffusion model, which requires training an additional classification model. Another stream of works [I] 30) stem from classifier free guidance in data generation, without training any additional classifier.
Data Editing. Data editing aims to manipulate the content of the given input to meet the require- ments of the target data attributes, such as image style [7], semantics [25] and structure [7]. with the utilization of multimodal methods such as text-to-image synthesis 9
28 49] and audio based image editing (50 51. In text-based scenarios, some works also generate texts under the instruction of specific attributes like sentiment (5) and structural information 32.. However, directly adopting the existing solution from other fields to time series editing is not feasible because of the significantly different data formats and poor data coverage of real-world time-series data w.r.t. different attributes.
B Broader Impacts
Our method offers several potential positive societal impacts in many real-world scenarios. For example, in climate monitoring, enhances climate models and forecasts, aiding in climate change mitigation. For urban management, it supports better decision-making in areas such as traffic management, pollution control, and resource allocation. Additionally, in research, it facilitates robust experiments and innovative solutions by providing high-quality, customizable datasets.
However, there are potential negative societal impacts to consider. There are data privacy risks associated with the manipulation of sensitive information, necessitating strong data protection measures. In financial markets, our method could be exploited for fraudulent activities, undermining market integrity. An overreliance synthetic data may lead to biased or inaccurate models, resulting in flawed decision-making. Furthermore, there are ethical concerns regarding the potential misuse of method in creating deceptive artificial intelligence models, which raises issues of transparency and accountability. While our method offers significant benefits, is crucial to carefully manage these risks to maximize positive impacts and minimize negative consequences.
C Diffusion Process
In this section, we give review of the diffusion process. Diffusion models are class of two-stage generation models, consisting of forward process and a reverse(denoising) process. The forward process is a Markov chain which gradually adds random noise on the origin data sample xo and
15

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Difiusion embudding oft
Time embedding
(128,11))
(128, 1,4)
(F,1,A)
(F,C,))
conneccoeetted and SILU
Expand and Concatoratte
series
(C.F.6)
(128.
Attribute
embodding
(c,1,8)
Convial
(1281.11
L.FFFX
Convixt and expand
Temporal Transformer Layer
Convlad
Skip connections
[C,F,K ()
Discard attribue prompts
(C,F,4)
Convict and RelU
(C.F.4)
(2C,
(C.F..)
Comderd
(C.F.4)
Goted activation uni (.)
output
(C.F.4)
(C,F,4)
byerr
Figure 8: Model architecture of our modified CSDI.
intermediate latent variables through Gaussian transition ( xx-1) := - 8,I). where 1,... 7 indicates the diffusion step, and { are predetermined variance schedule. The latent variable can be described as:
x axxx + /1 a,c, E ~~ (OO,)), a, := II B1) B.).
(7)
In the reverse process, a denoiser (x t, a), where a N the given attributes, needed to approximate the noise in each diffusion step to help to sample backward from Xy through another Gaussian transition pa (x(xx_1|x,) := (x (x,, t), og (x,, t)I). To obtain t), the denoiser should be trained by optimizing the loss:
min L = min ex)
||e (xt, a)
(8)
Here q(xx)) denotes the data distribution of xn (or x), t U(1 indicates that the diffusion step t follows the uniform distribution between and T.
After training, given condition using the trained e 5x(x, a with a diffusion sampler, such as the deterministic denoising diffusion implicit model (DDIM) 38
can generate the sample Xy from random noise Xy NO, I)
x /ao- t, + /I 1 t, (x,, a. (R; vi 0rEg (X,, 1, a))/ Va,
(9)
(10)
where (R 1, a) can be regarded as prediction of the diffusion step f.
D Model Architecture
In this section, we will show the architecture utilized in our modified backbone models CSDI and TIME-WEATE WEAVER especially how the attributes are incorporated into the models. It should be mentioned that all the attributes in our experiment setting are categorical.
Consider batch of time series samples of size (Nbathh F, L). where Nuuthh represents the number of samples per batch, represents the number channels in the time series, and represents the horizon. The corresponding attributes a is of shape (Nbatch L, K), where K represents the number
16

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Diffusion
Time embedding
(12811.1)
(128,
Time series
ConwID
(8,C.))
Permute
Fully-someeted
Concatenate
(C,4,1)
(128,1))
Acribure
(C, F.L)
(C, P.L))
Convixt
Temporal Transformer Layer
Convint
(C.F.4)
Comtec
Convixt ReLu
(C,F,4)
(2C)F,4)
(C.F,
Gatee actvvatio und
output
(.F..))
Input to O the next byyer
Convix1
Com1xx1
Residual layer Residual Iayer
Figure 9: Model architecture of Time Weaver.
of the attributes. While performing the time-series inputs, each attribute option will be encoded to corresponding embedding vector.
As for CSDI, our modified part is highlighted red. First, the attribute embedding will be linearly projected to shape (Nbatch: d, F, K). then concatenated with the time series embedding to form the prompt with shape (Nbatch, d, F, K L). Next, the prompt is added with the expanded diffusion step embedding and the sum will be inputted into the transformer layer. After that, the attribute embedding will be discarded, and the shape gets back (Neaich, d, F, L).
For TIME- WEAVER, We only make some slight changes to its original structure: 1. setting doesn't 't include continuous type attributes, mentioned. 2. We replace the self-attention modules with multi-layer perceptrons (MLPs).
Our experiment
As mentioned in Sec 3.3 we introduce novel parallelization with an attention masking mechanism to accelerate the training and inference. The attention mask is of the following form, denoted as
xN,++...NN, where M E 0%, the residual value is negative infinity. Through adding this attention mask into the original attention calculation [42 and softmax operation softmax (z); Σ ef receiving RN,, such attention mask would mask out inter-sequence attention while preserving intra-sequence attention.
M IR IN
M
M
R--1
E Bootstrap Learning Algorithm
As mentioned in Sec. 3.4 learn the information of the imaginary target. Given the pretained model eg. the detailed algorithm for a batch (X BxL A sre E NDxK), where B is the batch size, the length, and K the number of attributes, is presented below.
we propose bootstrap learning algorithm that enables the model to
17

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Algorithm Bootstrap Learning for a Batch
Input: (Xo E R B Art E [ where B. L, K are batch size, length, and attribute number. Attributes be edited Aedit and preserved Apssv.
The ratio v of the top bootstrapped samples to include for tuning.
1: # 1. Compose tuple (X A )
2:
for === 1.... do
3:
if l E Aodit then
4:
A A k] U(,,...,, N&
B Random sample a new value for the k-th attribute.
5:
else
6:
A ut
[, kt)
= A (
7: Generate the target t
8:
X <- (x
A sc 1gt
9: # 3. Self-Score the target l and keep the top j samples.
10: gge < +xx A A
11: + M X
X + Sort( Xo s)
Sort X based s. Keep the top to samples.
13: T + x00 vb]
0,bs
14: # 4. Calculate the loss.
15: LBs L( 0,bs)
f. the noise estimation loss of diffusion models defined in Eq.
Attribute
Value
Trend Types
[Linear, Quadratic, Exponential, Logistic]
Trend Directions
[Up, Down]
Season Cycles
4]
Table 5: Summary of attribute options
Datasets
F1 Synthetic Dataset
We generate the synthetic time series according the following formula:
X Xirnnd
+ X.cason
+ Xpoise
Xbiux
(11)
Table shows the summary of the attributes for the synthetic dataset.
For example, the attribute (Trnn Type linear, Trend Direction up. Season Cycles = 2) is one of the total 32 (4 Trend Types x Trend Directions x Season Cycles) attribute combinations. We split the 32 combinations into train, validation, and test sets by 24:4:4.
In the pretraining dataset, for each attribute combination, sample 300 time series according the random variances described in the following subsections. For the finetuning dataset, we treat each attribute combination in the 24:4:4 split as the target attribute. For each target attribute, we compose its source attribute based on the desired attributes to be edited Addi and preserved Apsvv. For example, {trend direction, season cycles). Suppose the target attribute ae [linear, up, 2). then its source could be are (linear, up, 2). a {quadratic, up, 2}. age (exponential, up. 2) and, a flogistic, up. Then for a source and target attribute pair (a a²R),, we generate the source and target time series (x x!at) based on the process described in the following subsections. Note that for each (a"e a! attribute pair, we first generate the deterministic part, e.g., trend type, for are and a denoted by sre
let Aetit = (trend type]. Apro
lgt deter and deter Then generate
10 randomnesses Xrands including noise, bias etc, and then we add these randomnesses to the
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deterministic part to ensure the source and target time series have the same randomness Xyand: x!8
l2t deter Xrand, x Xeter + Xrand-
The finetuning dataset contains 6 subsets for editing different attribute combinations. For modifying trend type, there are 760 train samples, 140 validation samples, and 160 test samples. For modifying trend direction, there are 420 train samples, 70 validation samples, and 80 test samples. For modifying season cycles, there are 760 train samples, 140 validation samples, and 160 test samples. For modifying trend type and trend direction, there are 1440 train samples, 280 validation samples, and 320 test samples. For modifying trend type and season cycles, there are 2880 train samples, 560 validation samples, and 640 test samples. For modifying trend direction and season cycles, there are 1440 train samples, 280 validation samples, and 320 test samples. In addition, the same random variance and bias pair is used both on the source time series and target time series in a source-target pair respectively.
In the following formulas, 4 indicates the i-th sampling point in the original synthetic time series(t) and 2; indicates the i-th sampling point in a contain form of x. For instance, the total length time series L = 128, then i .l..... 128).
F.1.1 Trend
Trend Types. As described in table there are trend types: linear. quadratic, exponential, and logistic. As described above, t is used to obtain x. For the linear trend: Xarend = t, in this case f; E 1]. 2, E [0, 1]. For the quadratic trend: Xrend t², in this case t2 E [0, 1], 2; E [0, 1]. For the
exponential trend: Xaend 1024
2 in this case 4 E 10, 10), 22 E [0, 1). Xtend is needed to range
from to 1, so I3 is in [-10,10]. For logistic trend: Xrend = 1+exp(-t)> where 4; E [-0, 10), E [0, 1]. Similar to the exponential trend, we repeat scaling process logistic trend. To train the model more easily, Xwend = (x 0.5) x 2 is used to normalize Xrend to 1].
Trend Directions. There are totally 2 directions: up and down. For instance, in the Cartesian coordinate system, a linear line from coordinates (0,0) to (1, represents an "up" trend, while another line from coordinates (0, (1, -1) represents "down" trend. All 4 trend types introduce above are originally "up" trends. Therefore, for an "up" trend, we set Xtrend trend, set Xrend
Xwrnnd and for a "down"
Xtrend-
Random Variance. We randomly perturb the scale of the trend by: Xucod = S-- -uuend where s the random scale obtained by the following process 4(0.8, 1.0) If m 1, then
+ (1 m) m (05n(0.5), [0.8, 1.0]; else [1.0, 1.25).
F.1.2 Season
The season attribute represented by the number of cycles in a time series. [0,1,2,4] sinusoidal wave cycles are randomly added.
Xeasoon a sin(2xt +
(12)
where t E [0, Deyetels Mayycle E 20, 21, 23]. The Random Variances these two variables follow uniform distributions: (1044, 0.6), (((0,
F.1.3
Noise
We simulate the real-world noise by combination of Gaussian noise and figh-frequenc noise.
Xauise
Xg + Xhr
(13)
The Gaussian noise is sampled through X₂ = =N(0, ~ ((0.004,000)) The high-frequency noise
is portrayed by a sinusoidal wave: Xhr = a sin + Φ), E [0, /lyylle] Reyole E [24, 25, 26), a ~ (0.008, 0.10), U(0 2T).
F.1.4 Bias
The last step is to add bias to the previous synthesized sample, which is randomly sampled from uniform distribution Xbias ~ 20-0((0.. 0.5).
19

---

Dataset
L
Categorical and Editable Attributes
4 Seasons [Spring, Summer, Autumn, Winter]
Air Quality
g
168
2 Cities [Beijing, London]
2 Movements [Figure, Tongue] 64 Channels[1, ..., 64]
Motor Imagery
150
Table 6: Dataset description in our experiments. Some key information is listed above. The real-world datasets include Air Quality, Motor Imagery. All datasets have been carefully processed. L indicates the time series length. The second column indicates all editable attributes.
F.2 Real World Datasets
F.2.1 Air Quality Dataset
This dataset used in the KDD Cup 2018 forecasting competition B. contains lots of hourly sampled time series representing the air quality levels measured from different stations cities: Beijing (35 stations) and London (24 stations) from 01/01/2017 11/03/2018. This air quality level is represented in multiple measurements such as PM2.5, PMIO, NO2, CO, O3, and SO2.
We processed the data and split it into train, validation, and test sets. We only selected the data of PM2.5, which is common in all stations. Season attributes are generated according to month. We slice the series by week to length of 168 (24hour x 7day). For pretraining dataset, there totally 3650 time series samples, in which we randomly pick 2825 time series samples as train, 353 time series samples as validation, and 472 time series samples the test. For finetuning dataset, it's composed of multi-subsets where each subset corresponds to an editing setting, e.g. modifying the "season". In any subset, a sample is composed of a source time series, source attributes, and target attributes where the source time series are sampled from the corresponding splits of pretraining data. For each source time series, we randomly change the source attributes 2 3 times to get the target attributes and pair the source time series and target attributes together. Finally, we get (train: 2000, valid: 600, test: 600) samples for modifying "city" subset and (train: 3000, validation: 900, test: 900) samples for modifying "season" subset.
F.2.2 Motor Imagery Dataset
The Motor Imagery dataset [I] contains some Ellettrenncephalogran (EEG) records. During the Brain-Computer Interface experiments, the subjects have to perform imagined movements of either the tongue or the left small finger. All recordings are collected at sampling rate of 1000 Hz with 64 channels. Every record contains 3000 time stamps (3 seconds measurement). For pertaining data, there 19353 samples in the train set, 2419 in the validation set, 2419 in the test set. For fine-tuning data, the processing operation is similar to the Air Quality. In the subset modifying movement, there are (train:2000, valid: 1000, test: 1000: samples. In the subset of modifying channel id, there (train:3000, valid:1500 1500, test: test:15000 1500) samples.
G CTAP Model Details
Our Contrastive Time-Series Attribute Pretraining (CTAP) model similar to the popular CLIP model [33]. The purpose of this model is to train the time series encoder and the attribute encoder by learning the alignment between the time series x E R& and its associated attributes a E NK Note that we use separate attribute encoders for different attributes An illustration of CTAP for batch of X E B and their associated k--th attribute A N is presented in Fig. TO, Note that the attribute index lo is dropped for clarity. The time series encoder and attribute encoder extract the embeddings H2 ) and H,, = (h., ) Following [33], we calculate the pair-wise similarities between the hx; and ha and the encoders are trained by distinguishing whether hy, and ha, are from the same data pair. The pseudocode of the CTAP model is shown in Algorithm 2
a(k]. h E f1,... K}.
1
20

---

Attributes
Attribute Encoder
Q1
Time Series
hai
Dag
Bag
Bag
h
Baj Ro.
By Ray
Py Ray
b. Aug
1
Dgg Ru
by
A
Aug
Encoder
Bgg
Rog
Bay Aeg
Bey
Rep
Deg
Dey
May
Beg Dec
Figure 10: Illustration of the CTAP model for the given pairs of time series X = (x: and attributes A = {a.)/=1 where B is the batch size. Here a = a[k], k E {1, K) } is the k-th attribute of the full attribute vector a E NK We use K separate attribute encoders for K attributes. In the illustration, obtaining embeddings [hx,he and [ha, Hen we calculate the the pair-wise similarities between the hx, and ha, The encoders are trained by distinguishing the positive pairs (green blocks) and negative pairs (white blocks).
only show one attribute and thus drop the attribute index k for clarity. After
Algorithm Pseudocode for CTAP model
Input: A batch of paired time series and attributes (X RBXL A R
1: # 1. Extract embeddings of time series and attributes.
2: Hy < Time Series Encoder(X)
H. Attribute Encoder(A) A)
4: Calculate pairwise similarities.
5: S <- Sim(H,, H.)
S E BxB is the similarity score matrix.
6: # 3. Calculate loss.
7: Lx +- Cross Entropy I, axis 1)
DIE [0,1] is the identity matrix.
8: C. <- Cross Entropy(S,,, (S, I, axis 0)
9: L +- (Lx + /2
Synthetic Trend directions
Air
Motor
Trend types
Season cycles
City
Season
Channel id
Imagined movement
Topl Acc
0.7877
1.0000
0.9558
0.9725
0.9068
0.8979
0.9537
Top2 Acc
0.9408
1,0000
0.9983
1.0000
0,9492
0.9364
1.0000
Table 7: The performance of CTAP models on different datasets. We report the top-1 1 and top-2 classification accuracy for each attribute on the test sets of the pertaining dataset.
21

---

H
Evaluation Metrics
H.1 MSE and MAE
For the synthetic dataset, MSE and MAE are two of the metrics used our experiments. For the reason that have targets in our synthetic datasets, MSE and MAE can be applied to evaluate the generated time series. The two formulas are listed below:
D
MSE
Xx
8:)²
(14)
[D]
MAE
x
Xd
(15)
[D]
Here [D] is the size of the test set, x; is the i-th target time series, and &; is the corresponding generated time series.
H.2 CTAP
The CTAP score obtained through the Contrastive Time series Attribute Pretraining (CTAP) model, which is similar to the popular CLIP model [33). The details of the CTAP model given utl in Appendix G Suppose dg is one of the target attributes, the CTAP score is similar to the CLIP-I score (23], which uses the pretrained CTAP model to measure the alignment, e.g., cosine similarity, between the generated e and the real-world time series also associated with the attribute Aet Specifically, we first use the CTAP model to extract embeddings hx which is the embedding of x! lgt
lgt which is the average embedding for the time series associated with dg in the training and hx
data. Then calculate the cosine similarity of hi and hyla where the higher similarity indicates better alignment between the generated and real-world time series.
H.3 RaTS
We introduce Log Ratio of Target-to-Souree probability (RaTS) score to measure whether the ig! generated time series 1 is closer to the target attribute d than the source time series x Formally, the RaTS score for tuple (X!st, sec ag
lgt
defined
1oT p(ag (xtrt)
RaTS( STE
.,k
181 log(
plat [xsce)
1st
(16)
where p(ag|x) xx) is calculated by applying a softmax over the similarity scores of all (hx, ha,=)) pairs, where hy the CTAP embedding of and h (.....,N,], is the CTAP embedding of the i-th possible value for the k-th attribute dg.
RaTS > 0 represents the model doesn't retain characteristics of the source time series * certain utt degree. RaTS O means that the attribute dg is reinforced compared to the source xaee For the edited attributes Astii the higher the RaTS score, the closer the edited Eet is to the target attributes. For the preserved attributes Apeee, the lower the IRaTSI score, the better the preservation of the attributes.
22

---

I More Experimental Results
I.1 Results
Overall
Trend Type
Trend Direction
Scason Cycles&
MMS
1MAE
TRaTS
1CTAP
LIRATSI
1CTAP
JIRTTSI
1CTAP
CSDI
0.1049=00.0852 0.2334++0.0088
0.5661 1651 0.3868+0.1777 0.0014=000001
0,9877=+000044
00004100.0049 0.8337=0.0307
Time Weaver 0.0519:20.0257 0.1763-20.0422 0.8460 1394 0.6870:20:1997
0.0016:0000002
0.9871=2000004
0.001200.0219
0.8219:20.0261
TEdit-CSDI
0.0377=0.005 0.1478++0.0221
0.8442=0.0800
0,6775=000701 0.0017=000004
0.9869+00000
000013=000135
0.8562=0.0128
TEdi-TW
0.0522=-000141 0.1817+:000722 0.9006=0.0707 0.7205::0.0422 0.0015=000000
0.9874::000000
0.1042=0.0141
0.8382++0000811
Overall
Trend Typea
Trend Direction &
Season Cycles&
4MSE
JMAR
LIRATSI
1CTAP
TRaTS
1CTAP
LIRATSI
1CTAP
CSDI
0,2128:10,1502 0.3726:10.138
0.2227 0.2655
0.0000 0.9748.10.0077
0.0542:00.0158
0.8641:10.002
Time Weaver
0.1345.10.0262 0.2989-10.0320
0.156410.0016
0.6309100.1071
1.97111000009 0.88331:0.0030
0.0918
0,7877:10.0329
TEEdiLCCSDI
0.097910.00185 0.2009-10.0286
0.347610.1117
0.6673110.1024
0.0717
0.962441000019
0.0660
0.8418:1000663
TEdit-TW
0.00222 0.2432.10.0269
0.0500 0.7277-10.0293
0000077
0.9849.10.00228
0.0153
0.8182.10.0123
Overall
Trend Typea
Trend Directions.
Seuson Cycles
LMSE
LMAE
LIRATSI
1CTAP
JIRATSI
1CTAP
1RTTS
CCAPP
CSDI
0.1980:10.0105 0.3508
0.0454:0.0451 0.6962:10.0744
0.0012 0.9675:10000000
0.3758-0.0064
0.0944:1.00662
Weaver 0.216710.0186 0.3612110.0007
0.176710.0038 0.7221110.0632
0.0019-10.0005
0.9647:10.0023
0.5976 0.0903
0.323310000004
TEdi-CSSI
0.205411000149 0.3538:10.0229
0.1390::..0433 0.7795::0.0668 0.00033:000000 0.9877:1:000114 0.6564
0,3670::0.2543
TEdi--TW
0.2122+0.0228 0.3623+0.0208 0.1652=00.305 0.8021+0.0150 0.0013=000001 0.9881=00.0005
0.891100.0688 0.6645=000068
Overall
Trend Type
Trend Direction
Scason Cycles&
LMSE
LMAE
1RaTS
1CTAP
tRaTS
1TAP
LIRATSI
1CTAP
CSDI
0.1119:000031 0.2532+10.0777
0.4925 1530 0,4653:10. 1492
1.2200=0000005 0.9835-10.0003
0.0520=0.0071
0,8546
Time Weaver 0.0712::0.0100 0.2109=00.024
0.7017=:000986 0.6846:000883
1.1216=000000 0,9872::00000
0.0798=0.0183
0.8277=000252
TEdit-CSDI
0.0051 0.1638-100013. 38 0.7478:1000754
0,7215-10.0693
1. 0.000
0.9864-1000014 0.0544=0.0028
0.8609-10.0080
TEdit-TW
0.0676=00.0254 0.19974:00035 0.8014=0.0629 0.7706+0.0007
1.1222=000002 0.9870+:0.0002
0.0670=0.0092
0.8489=0.0087
Overall
Trend Type
Trend Direction
Senson Cycles
LMSE
JMAE
1RaTS
1CTAP
LIRATSI
1CTAP
1RaTS
1CTAP
CSDI
0.2065 0.3446 0607
0,44900100
0.5078.10. 1775 0.0022-10.000
0,9840-1-0.000
0.9425
0.29211-000921
Time Weaver
0.1843=00.0441 0.3286+00.032
0.6915:0.1057 1057 0.7177=000968
0.0030=0.0009 0,9817=000059
0.5502=0.1000
0,4780=00.0500
TEdit-CSDI
0.15571:0.0155 0.30091000066
0.73291:0.0654 0,73921:0.0399
0.0022-10.004 0.9855-10.0020
0.5233_0..2255
0.50081:0.2151
TEdi
0.1647:10.0202 0.3054:10.0187 0.7548.0.0000 0.765811000333 0.001000.0000
0.9869
0.7559 0.0291
0.6813:100007
Overall
Trend Typea.
Trend Direction
Scason Cycles
LMSE
7MAE
JIRTTS
1CTAP
tRaTS
1CTAP
fRaTS
1CTAP
CSDI
0,2396:20.0781 0.3760+00.042
0,3819 1058 0.5349:0.1373 1.1210=00.0069 0.9788:0001002
0.0091=000893 0.1987::00911
Weaver 0.2138:10.0191 0.358910.0146 0.239710.0787 0.7190::00972 1.1247=000002 0.985411.0007
0.5227=0.1156 0.4026:10.100
TEdit-CSDI
0.1991=-000122 000061
0.2255=00.0510 0.7317+0.0704 1.1240==000006 0.9850++0.002 0.258 0,4355+0.0881
TEER-TW
0.01911000111 0.3011-0.0125
0.2208 0.762610.0217
0.0000
0.9859:100009
0.7859=0.04665 0.66101:0.491
Table 8: Performance on all subsets of attributes combination of Synthetic,
A denote
cdited/preserved attributes.
Air
Motor
City
Scason&
Channel Id
Imagined Movement.
TRaTS
1CTAP
LIRATSI
1CTAP
1R
1CTAP
J/RATSI
1CTAP
CSDI
1.2878:10.0590 0.328744000799
0.2349-:0.0131 0.443300.226
0.0095 0.0395+0..025
0.2963+40.0045
0.4402:1-00045
Time Weaver
1.4429:10.0709 0.5227-10.8661
0.2143110.0074 0.4736=0.0440
0.1002-000085 0,83161:0.061
0.2099110.0018
0.0440110.010
TEdi-CCSD
1.3036:20.0565 0.3541=+0.0664
0.2016=1.00880 0.480700.0212 0.1025=000018
0.8418:0000055
0.2927=10.0040
0.4467+00.007
TEda-TW
1.600210.0556 0.5357-10.0657
0.2226:1:00000
0.4839
0.0035
0.8443-10.0000
0.2885:10.0094
0.4394110.0113
Citya
Scason
Channel Id.
Imagined Movement
jIRTTSI
1CTAP
TRATS
1CTAP
LIRATSI
1CTAP
TRaTS
1CTAP
CSDI
0.1062:000217 08149:20.019
0.2022::000124 -001124:0.0163
0.0231=0.0014
0.8832+0.0029
4..001820.0124
0.0010:10.0059
Weaver
0.1588:10:069
0.7863+000107 0.3483-0000200 0.1204=0.0206
0.024110.0022 0.8840120.0007
0.0153::0.0127
0.0021110.004
TEdit CSDI
0.1112.0.0058 0.8250 10.0099 0.3045 0.0017
0.0233 0.0008
0.8841 1000011
0.0106.10.0022 0.0044....0003
TEdit-WW
0.16061000014 0.7709-10:0588
0.0720
0.2523-0.0221
0.0010=000010
0.88471000005
0.0479::000069
0.0252:10.0067
Table 9: Performance on all subsets of attributes combination of Motor and Air,
/A denote
edited/preserved attributes.
23

---

I.2 Sensitivity Study for multi-resolution
In this section, we show the impact different Multi-resolution parameters on different attributes in Fig. [1] All experiments are performed on 6 subsets of Synthetic, including editing [trend type, trend direction, season cycles, trend type trend direction, trend type season eycles, trend direction & season cycles]. To analyze the influence of R. we fix T = 2 and vary R. To examine the impact of Tp. we fix multi-resolution parameters across different subsets. This observation supports the motivation for proposing a multi-resolution approach: distinct attributes exert different scales of influence on time series data.
and vary Tp. We found that different attributes exhibit varying preferences for
Editing trand type
trend trend_direction
trana_direction
sranon_cyc cyclle
seaeon_rycles
CTAP
Multi-esolltion number
Editing trend direction
trend type
trand type trand direction
trend direction
CTMP
Muttiresolltion number
Patch length 4g (bl
Editing season cycles
trend_hype
trene_type frend
trend trend_ pirection
weason cycles
seavon cycles
CTAP
length 4
Editing trend type and trend direction
trend type
trand type direction season, sycles
trend direction
cycles
CTAP
65-
Mutti-resolition number
Patch length (b)
(n]
type cycles
trend_hype
trend_type type
trend tend__ineitio direction
trend trend_direcion direction
CTMP
Hulti resolution number
Patch length (b)
Editing trend direction and cycles
trend
trond_directton
season cycles
season cycles
number
longth Ly
Figure 11: The impact multi-resolution on 6 Synthetic subsets.

---

1.3 Case Study
In this section, we demonstrate the superiority of our approach by visualizing the results of our method with a baseline on editing tasks. As shown in Fig. 12 we compare our method and baselines on Synthetic dataset. The left column compares the TEdit-CSDI and CSDI while the right column compares the TEdit-TW and Time Weaver. Each row represents different attribute settings, containing: modifying trend type, modifying trend direction, modifying season cycles, modifying trend type and trend direction, modifying trend type and season cycles, modifying trend direction and season cycles. It's obvious that the editing results of our method are much closer to the target time series with lower MSE and MAE, proving the superiority of our method.
CSDI Base
Time Wesver Base
X
AM (*(C500
33-
0.
TEdit MSE:00 0126
TEdit MSE:
102
120
300
Trend Type: Ing linear
Trend Type: log Inner
1.5
2.5
1.0
1.0
0.5
3.5
0.0
3.0
-02
-10
Fedit 6:0117
-1.
MSE 0.0550
-15
CSDI MSE 0.0764
-15
TW MSE: D.1685
TEdt MSE o 881
TEdit MSE: 8,4575
-1.
-1.
-1.
-1.
Season Cyclesc
Seasion Cyclec
TEA
8.9953
0.5
CSDI MSE 0.1026
3.5
MSE
-1.
-15
2.0
200
120
200
Trand Type: lgg exe Trend Direction down up
Trend Type log exp Trand Direction: down up
1.5
0.0680 0.1048
Tedit MSE: 9-0486
CS0
0.0
3.0
-1.0
-1.
23
10
20
105
120
100
120
Type
suad
Cydees
Type:
Scason Cycles
1.5
1.5
1.0
-1.
-1.0
13
CSDI MSE
0.2366
MSE 0.5021
Trand Direction
Trend Direction:
Figure 12: More cases different attribute subsets Synthetic dataset
25

---

1,4 Comparing Editing and Conditional Generation
Here we provide more comparison results of conditional generation and editing in Fig. 13 The editing results are closer to the target time series with lower MSE and MAE. Although the time series generated by condition generation also meets the requirement the attributes, it still lost the detailed information of input while the editing process better retains the characteristics of the time series to preserve and manipulate only the target attribute to change. Therefore, the editing can achieve more precise control compared with conditional generation.
Cord gen, Mat: 9.213
EdR MED 9-2788
0.0
-15
-1.0
-1.
45
60
60
100
120
20
60
110
120
Trand Typec wap quadd
Directions down up
Cond gen: MSE 0.0870
Cond.gen 8.2057
MSEE
Cyclenc
Type log
Edk RaE 2.0821
10
-35
-0.5
-15
1.0
Cond
NEB
8.2005
Trend Typec Inear qued Swassn Syclecc
Trand Descciin:
down Swaste
Figure 13: Comparison between editing and conditional generation
1.5 Bootstrap influence on different attributes
To further explore the impact of bootstrapping on data coverage, we visualize the effect of bootstrap- ping data on different attributes to complement the original data. Fig. 14 present the results on trend types, and season cycles, trend directions, respectively. According to the classification accuracy can infer that trend directions are relatively simple pattern for
of the TAP model and case study, modeling while the trend types and season cycles are more complex. The data coverage improvement brought by bootstrapping is more obvious when the attribute is complex.
26

---

Orgnalova
Dipa 1914
Opgan bata
60
Dana Pols
Orcha Dets
Figure 14: The influence of bootstrapping different trend types
Digaa Cula
Ogga Duta
Orgha Deta
Orhba lels
Figure 15: The influence of bootstrapping on different season cycles
Drbba Euta
Figure 16: The influence of bootstrapping on different trend directions
27

---

NeurIPS Paper Checklist
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is fine to include aspirational goals as motivation as long as is clear that these goals are not attained by the paper.
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Answer: [Yes]
Justification: This paper has discussed the limitations in Conclusion part.
Guidelines:
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The authors are encouraged to create a separate "Limitations" section their paper.
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Reviews:
[{"review_id": "UfPQ8H0waj", "reviewer": "Reviewer_tfFr", "summary": "This work proposes a new task as time series editing (TSE), that edits existing time series samples based on given sets of attributes. The goal is to perform edits that are only relevant to the changed attributes while preserving other existing attributes. The authors propose a multi-resolution modeling and generation model, and they perform bootstrap tuning on self-generated instances to perform the experiments. The authors show the proposed method can be used for modifying specified time series on synthetic dataset and real-world datasets.", "questions": "NA", "limitations": "The authors discussed limitations.", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 2, "strengths": "- The idea is interesting. The TS editing could be an important task for time series.\n- The proposed framework makes sense. The Multi-Resolution backbone clearly addresses issues of existing backbones; and the bootstrapped training method seems legit given the challenge of existing data/information.\n- Experiments on synthetic datasets showed clear advantage of the proposed method.", "weaknesses": "While I personally like the idea a lot, there are a few non-neglectable issues (rooms for improvement):\n\n1. The work could use better applications.\nThere are a couple of tasks/applications that could clearly benefit from TS editing. For example, see the works below:\n\n[1] Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., & Azemi, E. (2020). Subject-aware contrastive learning for biosignals. arXiv preprint arXiv:2007.04871.\n\n[2] Liu, R., Azabou, M., Dabagia, M., Lin, C. H., Gheshlaghi Azar, M., Hengen, K., ... & Dyer, E. (2021). Drop, swap, and generate: A self-supervised approach for generating neural activity. Advances in neural information processing systems, 34, 10587-10599.\n\n[3] Yi, K., Wang, Y., Ren, K., & Li, D. (2024). Learning topology-agnostic eeg representations with geometry-aware modeling. Advances in Neural Information Processing Systems, 36.\n\nThe above works include datasets with very specific attributes, e.g. subject information; task information; geo/channel information. If the proposed method could be applied to the above tasks, and demonstrate robustness, the paper could be much stronger.\n\n2. Experimental results are overall not convincing enough. The datasets are too small, and there is no enough information about the synthetic dataset. There are not a lot of baselines.\n\n3. Some parts of the method section can use better presentation:\n- Section 3.1 How does the Time Series Editing task differ from the normal editing task in vision? Why do the authors want to define a new definition for it?\n- Section 3.2 The authors should argue why is diffusion necessary for performing the task. What advantages would it bring?\n- Section 3.4 The authors should compare the proposed method with traditional SSL methods like BYOL & Simclr and discuss about their differences.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your appreciation and comments!\n\n>**W1. Suggested new datasets.**\n\nThanks for your suggestion. The datasets you mentioned are indeed related and useful, revealing a wider application space for our work.\n\nWe have looked into these datasets, many of which are EEG time series with various attributes, such as subject and task information. For example, the SEED dataset used in [1] contains attributes such as EEG channels and subjects, which is similar to the Motor dataset (Sec. 4.1 and Appendix E) used in our paper. Potential applications of these datasets include completing time series of missing channels by editing time series of other channels. Recall that, time series editing is a relatively new task, and there is still much to explore.  We plan to further investigate the evaluation benchmark of Time Series Editing (TSE) task and will use a broader range of datasets in future research. \n\n>**W2. Dataset size, synthetic data details and baselines.**\n\nThanks for your comments. \n\n1. For the dataset size, we believe the size should not be considered \"too small\" since the datasets we used and many datasets used in recent studies, e.g., time series generation [2] and LLM based time series model [3], have approximately the same scale. In total, our Synthetic-1/Synthetic-2/Motor/Air have 10,660/13,680/12,800/6,850 samples. Many datasets used in [2] and [3] range between 10,000~30,000. Collecting larger datasets will be an important future work for our paper and the proposed TSE task, and we will keep gathering more datasets with larger scales.\n\n2. For the synthetic dataset, due to space limitation in the main paper, its details are presented in Appendix E.1. \nBriefly speaking, the synthetic samples we constructed consist of four parts: *trend*, *season*, *noise* and *bias*. \n- The *trend* has two attributes: trend types (linear, quadratic, exponential, logistic) and trend directions (up, down). \n- The attribute of *season* is the number of cycles, with possible values of 0, 1, 2, 4. \n- The *noise* is a combination of Gaussian noise and high-frequency noise. \n- The *bias* of each sample is randomly sampled from a uniform distribution. \n \n3. Regarding the baselines, we are exploring a brand new field, and to the best of our knowledge, this is the first work focusing on developing methods and evaluation benchmarks for TSE. There are only few related works, and adapting the models from other tasks to this one involves significant efforts. The baselines we selected are Time Weaver [2], the latest work of conditional time series generation, and CSDI [4], a classic time series imputation framework. \nPlease note that the latest work [2] only compared with 2 traditional GAN-based methods due to the lack of proper baselines in conditional time series generation. We believe [2][4] effectively reflect the current state-of-the-art in related fields. \nHowever, we will consider adding more comparative analyses in the future. Thank you for the suggestion.\n\n> **W3. Presentation suggestions.**\n\nThank you for your suggestions and we will update the revised version accordingly for better readability.\n\n1. About time series editing (TSE) and image editing.\n\n1.1. Comparison of TSE and image editing.\n\nBroadly speaking, TSE shares the same target with image editing: edit the given data according to the specified conditions.\nHowever, time series data is quite different from image data, and there are unique challenges for TSE. \n- Time series has attributes such as *recoding location of city*, which are more difficult for model to interpret than the attributes such as *color* in image.\n- Time series data has its own unique properties, e.g., variant seasonality and low signal-to-noise ratio. \n- Moreover, the data of the time series corresponding to some specific conditions might be rare, making generation and editing quite challenging. \n\nNote that, we propose to use multi-resolution modeling and generation with bootstrapping mechanism trying to resolve the problem of aforementioned problems.\n\n1.2 Definition presented in Section 3.1\n\nThough sharing similar definition of image editing task, in Section 3.1, we formally formulate TSE task mainly because editing is a relatively new concept for the time series research community. A formal problem formulation could help readers to easily understand the task and follow the paper.\n\n2. The necessity of diffusion.\n\nTSE is a kind of generation task, which requires powerful generative models. There are two considerations of using diffusion models. (1) Diffusion is the latest generation technique and some recent works [2] have demonstrated that diffusion models can significantly outperform traditional methods like Generative Adversarial Network (GAN). (2) To keep fair comparison to the latest conditional time series generation work [2], we leverage the backbone model and base generation algorithm as the same as the baseline works [2][4].\n\n3. BYOL and SimCLR\n\nThe work BYOL you mentioned bootstraps the output of a neural network to serve as targets for a learning network to approximate. It only bootstraps on the learning targets and it is different from our method that utilizes the learned generative model to generate the samples of both input and targets for bootstrapping the learning of the target generative model. SimCLR adopts different data augmentation to build the target representation for contrastive learning, which does not leverage bootstrapping. \nWe will add more discussions to other bootstrapping works to enhance the presentation of our work.\n\n[1] Yi et al. Learning topology-agnostic eeg representations with geometry-aware modeling. NeurIPS'2023\n\n[2] Narasimhan et al. Time weaver: A conditional time series generation model. ICML'2024\n\n[2] Jin et al, Time-LLM: Time Series Forecasting by Reprogramming Large Language Models. ICLR'2024\n\n[4] Tashiro et al. CSDI: Conditional score-based diffusion models for probabilistic time series imputation. NeurIPS'2021", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "NN2Go8koxi", "reviewer": "Reviewer_ZaGr", "summary": "This paper introduces time series editing (TSE), a novel approach for modifying existing time series to match specified attributes while preserving other attributes. Unlike traditional methods that generate time series from scratch, TSE employs a multi-resolution modeling and bootstrap tuning framework, enhancing data coverage and generation precision. The paper presents a multi-resolution diffusion model and a comprehensive benchmark dataset, demonstrating the method’s effectiveness on both synthetic and real-world data.", "questions": "I have no specific questions.", "limitations": "The authors adequately addressed the limitations.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "- The paper introduces the novel concept of Time Series Editing (TSE), a significant innovation in the field of time series data synthesis.\n- This work offers a practical solution for generating high-quality time series data with specified attributes, addressing data sparsity and enhancing the utility of time series synthesis. The novel approach and its demonstrated effectiveness on diverse datasets highlight its broad applicability and potential to drive future research and applications in the field.\n- The overall quality of the paper is exemplary, demonstrated by thorough experimentation and rigorous evaluation. The authors present a well-designed DDIM model with the proposed multi-resolution noise estimator and bootstrapped training algorithm.\n- The paper is clearly written and well-structured, making it accessible to readers with varying levels of expertise in time series analysis.", "weaknesses": "- The paper focuses on editing specific attributes of time series while preserving others but does not extensively discuss the handling of diverse attribute types and their interactions.\n- The reproducibility is uncertain without code and detailed implementation information in Appendix I.\n- There are minor writing issues, such as “levela” in line 291, page 8, that need correction.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your appreciation and feedbacks! Below are the responses.\n\n>**W1. Handling of diverse attribute types and their interactions.**\n\nThanks for your comments. \n\nThe following tables 1~6 show the complete experimental results of editing different combination of attributes (trend type, trend direction and the number of season cycles), hoping to provide a better understanding of our method.\nDue to time limitation, in the following tables, we present the results of TEdit-TW and Time Weaver [1] on editing different sets of attributes on Synthetic dataset.\nFrom the tables, our TEdit-TW outperforms Time Weaver [1] on all attribute combinations. \nWe also found that the difficulty of editing different attributes varies greatly. By comparing MSE, (1) editing season_cycle is the most difficult, editing trend_type is the easiest, and (2) editing multiple attributes is usually more difficult than a single one.\nFor more detailed experimental results, please refer to the PDF file in the General Response.\n\n- ### Table 1. Synthetic dataset: edit \"trend type\".\n|Method|MSE$\\downarrow$|MAE$\\downarrow$|RaTS:trend_type$\\uparrow$|TAP:trend_type$\\uparrow$|RaTS_abs:trend_direction$\\downarrow$|TAP:trend_direction$\\uparrow$|RaTS_abs:season_cycle$\\downarrow$|TAP:season_cycle$\\uparrow$|\n|-|-|-|-|-|-|-|-|-|\n|Time Weaver|0.0565/0.0115|0.1875/0.0229|0.7532/0.0656|0.5940/0.0686|0.0017/0.0002|0.9866/0.0012|0.0913/0.0245|0.8424/0.0132|\n|TEdit-TW|0.0431/0.0008|0.1629/0.0015|0.8139/0.0093|0.6621/0.0052|0.0016/0.0001|0.9879/0.0003|0.0818/0.0010|0.8582/0.0010|\n\n- ### Table 2. Synthetic dataset: edit \"trend direction\".\n|Method|MSE$\\downarrow$|MAE$\\downarrow$|RaTS_abs_trend_type$\\downarrow$|TAP_trend_type$\\uparrow$|RaTS_trend_direction$\\uparrow$|TAP_trend_direction$\\uparrow$|RaTS_abs_season_cycle$\\downarrow$|TAP_season_cycle$\\uparrow$|\n|-|-|-|-|-|-|-|-|-|\n|Time Weaver|0.1178/0.0181|0.2844/0.0214|0.4211/0.07058|0.5835/0.0739|1.9720/0.0010|0.9831/0.0044|0.0924/0.0249|0.8083/0.0394|\n|TEdit-TW|0.0965/0.0010|0.2636/0.0013|0.3426/0.0174|0.6656/0.0101|1.9716/0.0005|0.9826/0.0003|0.0747/0.0098|0.8282/0.0056|\n\n- ### Table 3. Synthetic dataset: edit \"season cycle\".\n|Method|MSE$\\downarrow$|MAE$\\downarrow$|RaTS_abs_trend_type$\\downarrow$|TAP_trend_type$\\uparrow$|RaTS_abs_trend_direction$\\downarrow$|TAP_trend_direction$\\uparrow$|RaTS_season_cycle$\\uparrow$|TAP_season_cycle$\\uparrow$|\n|-|-|-|-|-|-|-|-|-|\n|Time Weaver|0.2294/0.0130|0.3699/0.0090|0.1467/0.0299|0.6988/0.0092|0.0014/0.0001|0.9868/0.0007|0.4991/0.1420|0.2321/0.1344|\n|TEdit-TW|0.2004/0.0025|0.3608/0.0025|0.1471/0.0027|0.7768/0.0023|0.0012/0.0001|0.9887/0.0002|0.8086/0.0088|0.5230/0.01258|\n\n- ### Table 4. Synthetic dataset: edit \"trend type\" and \"trend direction\".\n|Method|MSE$\\downarrow$|MAE$\\downarrow$|RaTS_trend_type$\\uparrow$|TAP_trend_type$\\uparrow$|RaTS_trend_direction$\\uparrow$|TAP_trend_direction$\\uparrow$|RaTS_abs_season_cycle$\\downarrow$|TAP_season_cycle$\\uparrow$|\n|-|-|-|-|-|-|-|-|-|\n|Time  Weaver|0.0793/0.0091|0.2216/0.0122|0.6438/0.0539|0.6351/0.0567|1.1216/0.0008|0.9865/0.0011|0.0731/0.0114|0.8401/0.0197|\n|TEdit-TW|0.0504/0.0007|0.1743/0.0009|0.7194/0.0033|0.7070/0.0019|1.1217/0.0008|0.9871/0.0001|0.0575/0.0020|0.8641/0.0014|\n\n- ### Table 5. Synthetic dataset: edit \"trend type\" and \"season cycle\".\n|Method|MSE$\\downarrow$|MAE$\\downarrow$|RaTS_trend_type$\\uparrow$|TAP_trend_type$\\uparrow$|RaTS_abs_trend_direction$\\downarrow$|TAP_trend_direction$\\uparrow$|RaTS_season_cycle$\\uparrow$|TAP_season_cycle$\\uparrow$|\n|-|-|-|-|-|-|-|-|-|\n|Time Weaver|0.1873/0.0031|0.3310/0.0018|0.6564/0.0381|0.6721/0.0421|0.0022/0.0002|0.9844/0.0004|0.4523/0.1320|0.3880/0.1244|\n|TEdit-TW|0.1453/0.0001|0.2939/0.0005|0.6959/0.0078|0.7283/0.0045|0.0018/0.0001|0.9883/0.0001|0.6924/0.0105|0.6233/0.0080|\n\n- ### Table 6. Synthetic dataset edit: \"trend direction\" and \"season cycle\".\n|Method|MSE$\\downarrow$|MAE$\\downarrow$|RaTS_abs_trend_type$\\downarrow$|TAP_trend_type$\\uparrow$|RaTS_trend_direction$\\uparrow$|TAP_trend_direction$\\uparrow$|RaTS_season_cycle$\\uparrow$|TAP_season_cycle$\\uparrow$|\n|-|-|-|-|-|-|-|-|-|\n|Time Weaver|0.2307/0.0122|0.3776/0.0108|0.2556/0.0409|0.6810/0.0536|1.1252/0.0005|0.9862/0.0009|0.4601/0.1571|0.3448/0.1526|\n|TEdit-TW|0.1747/0.0011|0.3289/0.0013|0.2534/0.0080|0.7352/0.0075|1.1253/0.0003|0.9872/0.0002|0.6906/0.0059|0.5762/0.0044|\n\n>**W2. Reproducibility.**\n\nThanks for your comments. \n\nWe will release our codes upon the acceptance of this paper, and we will figure out a better way to present the implementation. In the current version of our paper, we have listed the implementation details and training details in the appendix. Specifically, Appendix I provides the training configurations. Appendix H shows the architectures of our model. Appendix E and F present evaluation model and metrics. Moreover, Appendix C and D illustrate the optimization details and training algorithm. Appendix E discusses the data processing details. Appendix F explains the evaluations. We will refine the discussion of the above implementation details for better reproducibility.\n\n>**W3. Writing suggestions.**\n\nSorry for the inconvenience. We will make more rounds of proofreading and revise the details.\n\n[1] Narasimhan et al. Time weaver: A conditional time series generation model. ICML'2024", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "1uonEQ8x3Q", "reviewer": "Reviewer_EFAk", "summary": "The paper discusses the challenges of synthesizing time series data influenced by intrinsic and extrinsic factors. It highlights the limitations of existing benchmarks and methods and suggests a new task, Time Series Editing (TSE), which modifies given time series based on specified properties while preserving other attributes. In addition, the paper suggests a new method based on generative bootstrapping and the incorporation of multi-resolution modeling to enhance data coverage, demonstrating its efficacy in experiments.", "questions": "- Can the author present all the possible preserved-edited groups in the main results? Currently, in my opinion, the main results are not extensive enough. \n\n- Why did the author not mention \"On the Constrained Time-Series Generation Problem\"?", "limitations": "detailed on the weakness", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The task of editing specific attributes is novel and very interesting. \n- Providing end-to-end benchmarks that include datasets,  metrics, and baselines\n- Suggesting  a new baseline method based on bootstrapping and multiresolution", "weaknesses": "- The work \"On the Constrained Time-Series Generation Problem\" [1] introduces time series generation and editing. Although there are certain differences between the current work and the above work, they can be seen as similar. However, the paper does not reference or discuss the similarities and differences. I think that incorporating such a comparison is crucial. In addition, this somehow negatively affects the first novelty that the offers suggested. Nevertheless, I still think the task is novel enough.\n\n- The main experiment results in Tables 1 and 2 are on specific attributes; to show robustness, it is necessary to show or average all the possible preserved-changed attribute groups. \n\nMulti-resolution models for time series tasks are already in use [2, 3], which limits the novelty. I want to caveat this weakness by stating that [2] is not in the context of generation, and [3] is almost concurrent with this work.\n\n\n[1] - On the constrained time-series generation problem. Coletta, Andrea and Gopalakrishnan, Sriram and Borrajo, Daniel and Vyetrenko, Svitlana.\n[2] - Multi-resolution Time-Series Transformer for Long-term Forecasting. Yitian Zhang, Liheng Ma, Soumyasundar Pal, Yingxue Zhang, Mark Coates\n[3] - Multi-Resolution Diffusion Models for Time Series Forecasting. Lifeng Shen, Weiyu Chen, James Kwok", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your valuable comments!\n\n>**W1. Comparison with [1]**\n\nThanks for pointing it out, and we will include a discussion and comparison of [1] in the revised version. Time Series Editing (TSE) proposed in our work is essentially different to [1], though these two works share some similarities in conditional generation.\n\nSpecifically, the term \"constraint\" mentioned in [1] is actually similar to the term \"condition\" or \"attribute\" used in our work and [2]. The task of [1] can be formulated as $\\mathbf{x}^{tgt}=\\Phi(\\mathbf{a}^{tgt})$, where $\\mathbf{a}^{tgt}$ is the target constraint, $\\mathbf{x}^{tgt}$ is the desired output and $\\Phi$ denotes the function of generative model to be learned. However, our proposed TSE is formulated as $\\mathbf{x}^{tgt}=\\Phi(\\mathbf{x}^{src}, \\mathbf{a}^{src}|\\mathbf{a}^{tgt})$, which generates $\\mathbf{x}^{tgt}$ based on the input source sample $(\\mathbf{x}^{src}, \\mathbf{a}^{src})$ and the target condition $\\mathbf{a}^{tgt}$. Since [1] and [2] are both \"conditional generation\" studies, we compare with the more recent work [2] in our current version. We will discuss and compare with [1] in the revised verison.\n\n>**W2. Averaged results**\n\nThanks for your feedback.\n\nBelow we provide the experimental results of the average performance of editing different attributes for reference, hoping to help you better understand the performance of different methods. We also provide the results for all 6 edit-preserve attribute groups in the pdf of the general response.\n\nIn the following tabels we present the avergaed results of editing different attribute combinations on the Synthetic and Air datasets. The Synthetic dataset has 3 attribtues: trend type, trend direction and season cycle. The Air dataset has 2 attributes: city and season.\nThe results show that our proposed method TEdit outperforms the baseline on all the evaluation metrics.\n\n- ### Table 1. Average performance of editing different attributes on Synthetic. \n|Method|MSE$\\downarrow$|MAE$\\downarrow$|RaTS_edited$\\uparrow$|RaTS_abs_preserved$\\downarrow$|\n|:--------:|:---------:|:--------:|:---------:|:--------:|\n|Time Weaver|0.1502|0.2953|0.8537|0.1206|\n|TEdit-TW|**0.1184**|**0.2641**|**0.9599**|**0.1069**|\n\n- ### Table 2. Average performance of editing different attributes on Air.\n|Method|RaTS_edited$\\uparrow$|RaTS_preserved$\\downarrow$|\n|:--------:|:--------:|:--------:|\n|Time Weaver|0.8799|0.2017|\n|TEdit-TW|**0.9970**|**0.1753**|\n\n>**W3. Multi-resolution**\nThank you for sharing these works, it will actually enhance the discussion of related works of our paper.\n\nAs mentioned by your comments, one work is almost concurrent with our paper and these works primarily focus on the forecasting task, aiming to make more accurate predictions.\nOur work, however, focuses on the editing task, which requires to generate the corresponding time series regarding the given editing attributes as condition. The reason we propose the multi-resolution approach is that different attributes impact the time series at different scales. For example, the trend type has a global impact, while the season number focuses on more local details. In our work, we consider both the multi-resolution aspects of the time series and attributes, and leverage multi-resolution mechanism on both modeling and generation, which is more reflective of reality.\nThank you again for mentioning these related works. We will discuss them in the revised version of our paper.\n\n[1] Coletta et al. On the constrained time-series generation problem. NeurIPS'2023\n\n[2] Narasimhan, Sai Shankar, et al. Time weaver: A conditional time series generation model. ICML'2024", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ehzs9QILbd", "reviewer": "Reviewer_FqWw", "summary": "This paper introduces Time Series Editing (TSE), a method for generating time series data with control over their attributes. TSE generates a sample by modifying an existing time series through manipulation of specific attributes while maintaining consistency in others. This approach leverages the hypothesis that patterns in time series synthesis are transferable between attribute values with consistent shared attributes. The authors propose a novel multi-resolution diffusion-based model for sample generation, that is trained using a bootstrapped learning algorithm. They show that this architecture and training procedure choice overcomes challenges related to biased data distribution and varying degrees of influence different attributes have on time series generation.", "questions": "$\\textbf{Correlated attributes}$: In many real-world applications, the underlying attributes are not independent. As a result, randomly changing attributes while fixing others can result in out of distribution or unrealistic samples. I'm wondering if this becomes a problem especially in the bootstrap self-scoring step where the model will end up overfitting to out of distribution samples. \n\n$\\textbf{Distributional coverage}$: I'm not sure the conclusion drawn from Figure 5 is correct. The figure shows T-SNE projection of the data and the bootstrapped data, and the conclusion is that the generated samples fill in the gaps in the original data space. However, with methods like T-SNE, the projection is a function of the data they are trained on. Which means comparing the three subfigures that are representing different data cohorts is not possible. Can the authors provide more explanation on this?\n\n$\\textbf{Unseen attributes}$: Does the approach extend to unseen attribute values? Can you generate a time series with an edited attribute where the value has never been seen in the training data?\n\nNote: I would be willing to increase my score if some of the concerns mentioned in the review are addressed.", "limitations": "$\\textbf{Evaluating generated time series samples}$: One of the biggest challenges for generative models in time series is evaluating the generated samples. The proposed metrics and evaluations are useful in assessing different aspects of the performance of TSE, but there is no discussion on how we can assess whether a generated sample is realistic or not. Even a low MSE by itself doesn't guarantee high-quality generated samples. I think this is something worth discussing in the paper.", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "$\\textbf{Importance of the task}$: I think the proposed method is tackling a very important problem that has potential in many applications. In fact, I would suggest that the authors highlight the potential of their approach even more. Generating such time series samples has many applications in different domains. Right now the paper focuses on the 'editing' aspect of TSE, but the proposed method has potential to generate counterfactual time series with control over attributes.  \n\n$\\textbf{Multi-resolution generative model}$: The proposed multi-resolution diffusion model is an interesting design to overcome the varying scale of impact in time series.", "weaknesses": "$\\textbf{Edited attribute set}$: In the experiment section, the results are for a specific type of change (a specific set of edited attributes). It is not clear why this specific combination was selected. I think the performance should be reported for all possible combinations, or the average of all possible combinations. Another interesting extension to this would be to look into these averaged performances, as a function of the size of the manipulated set of attributes. This might not be helpful for the selected real-world datasets because they only have 2 attributes, but at least the authors can assess this in the synthetic setting. \n\n$\\textbf{Standard errors}$: Some of the reported performances are very close and it is difficult to assess their significance. The authors need to add confidence intervals to the reported numbers in all tables. \n\n$\\textbf{RaTS}$: It is not clear how $p(a_k^{tgt}|x)$ is estimated in order to calculate the RaTS performance score? \n\n$\\textbf{MSE for real data}$: In general, it is difficult to know the ground-truth for edited time series in real-world dataset. However, I believe in the existing datasets, this is not impossible. For instance, in the Air dataset, if the changed attribute is the city, we have the corresponding time series in the data. This way the authors can also report MSE/MAE for the real-world datasets as well. This is important because even though the other metrics assess important qualities for the model, they cannot tell how well or realistic the generated signal is.  Adding this evaluation can help better assess the performance of TSE on real-data.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your constructive feedback!\n\n>**W1. Edited attribute set**\n\nThanks for your comments.\n\nIn the tables below, we show the average experimental results of editing different types and numbers of attributes on the Synthetic dataset. It can be seen that our TEdit outperforms baseline method on all metrics. The subscripts \"edit\" and \"prsv\" refer to the edited and preserved attributes. For detailed results of each combination, please refer to the PDF in the General Response. We will add more results in the revised version.\n\n- ### Table 1: averaged results of different edit-preserve attribute groups on Synthetic.\n|Method|MSE$\\downarrow$|MAE$\\downarrow$|RaTS_edit$\\uparrow$|RaTS_abs_prsv$\\downarrow$|\n|-|-|-|-|-|\n|Time Weaver|0.1502|0.2953|0.8537|0.1206|\n|TEdit-TW|**0.1184**|**0.2641**|**0.9599**|**0.1069**|\n\n>**W2. Standard errors**\n\nFor each setting, we run the experiment three times, and calculate the mean and standard deviation (STD). These results can be seen in the PDF file in the General Response. \nFrom the results, the improvement of our method against the baseline is significantly larger than the STD values in almost all the settings, which illustrates that our method's improvements are significant. Moreover, the STD values of our method is much smaller than the baseline, showing that our method has better stability.\n\n>**W3. RaTS**\n\nBriefly speaking, we follow [1] and leverage an external model TAP (Time series - Attribute Pretraining) similar to CLIP [2] to estimate this probability. The details of TAP are presented in Appendix G.\n\n$p(a_{k}^{tgt}|x)$ is calculated in the following steps. \n- Use TAP to encode time series $\\mathbf{x}$ and the $N_k$ possible values, e.g., linear and exponential, of the $k$-th attribute $a_k$, e.g., trend type, into embeddings $\\mathbf{h}_{x}$ , and $\\mathbf{h}\\_{a_k}^{n}$, where $n\\in[1,..., N_k]$.\n- Obtain $p(a_k^n|x)$ by applying a softmax over the cosine similarity between $\\mathbf{h}_{x}$ and $\\mathbf{h}\\_{a_k}^{n}$.\n\n>**W4. MSE for real data**\n\nMSE is indeed a straightforward metric for measuring the quality of generated samples. However, MSE can be used only when source and target samples share **all** the attributes, including hidden attributes such as noise, except for the edited attributes. We can guarantee such a 1-1 pair for synthetic data. However, in real-world datasets, there are many unseen attributes and interference which make the time series vary a lot even with the same attribute combination. For example, air quality is not only influenced by city and season, but also other factors such as weather, wind and the industrialization level. We may not say that the edited time series by modifying city attribute from \"Boston\" to \"New York\" is exactly the same as the specific data sample with city of New York in the dataset.\n\nFor the real-world datasets, we follow the practices in computer vision domain [1], by utilizing external models to evaluate the quality of the edited samples. We acknowledge that evaluating generative models is challenging, especially for the newly proposed time series editing task. We will keep investigating better benchmarking solutions in this novel direction.\n\n>**Q1. Correlated attributes**\n\nWe also had similar concerns at the early stage of experiments. However, after statistically visualizing the data generated by bootstrapping, we found that the data generated by bootstrapping is a more complete complement to the original real data distribution, as shown in Fig. 5 in the paper. Training on both the real and the bootstrapped data makes the model fit the complete distribution and derive better performance, as illustrated by our experiments. Therefore, there is almost no overfitting outside the real distribution.\n\n>**Q2. Distributional coverage**\n\nThanks for your comments and sorry for the misleading description. \n\nWe use all the data, including both real and bootstrapped data, for T-SNE. Thus, the three subfigures of Fig. 5 share the same T-SNE model with the same projection, which means their data cohorts are the same. So it is reasonable to comapre the distribution coverage of the original real dataset, bootstrapped synthetic dataset, and the full dataset of the above two sets, in the three subfigures. We will further clarify this in the revised version.\n\n>**Q3. Unseen attributes**\n\nOur understanding of the \"unseen attribute values\" is \"unseen attribute combinations\". In other words, the model has seen all $N_k$ values of each individual attribute $a_k$, but has not seen certain attribute combinations. This setting is the same as our synthetic datasets. There are 4 unseen attribute combinations in the test data. Therefore, our proposed TEdit could deal with the unseen attribute combinations. In the future, we plan to further explore this interesting setting for real-world datasets.\n\n>**L1. Evaluating generated time series samples**\n\nThanks for your insightful comments!\n\nAssessing the reality of the generated data has always been a critical open challenge. This is extremely challenging even for images, where attributes such as color and size can be easily interpreted, not to mention time series, where the data is difficult to be interpreted. The classic work [3] asked humans to evaluate the reality of the generated images. We follow the practice of recent works [1] to use external models as evaluation. Nevertheless, assessing the reality can be an interesting future direction. We will further discuss it in the revised version.\n\n[1] Li et al. Blip-diffusion: Pre-trained subject representation for controllable text-to-image generation and editing. NeurIPS'2023.\n\n[2] Radford et al. Learning transferable visual models from natural language supervision. ICML'2021.\n\n[3] Meng et al. SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations. ICLR'2022", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
# General Response

We thank all the reviewers' insightful and valuable feedback! 

We are encouraged by the positive comments from the reviewers such as:
- The proposed Time Series Editing (TSE) is an interesting and important task with a wide range of applications (Reviewer tfFr, ZaGr, EFAk and FqWw).
- The proposed framework TEdit, including bootstrapping and multi-resolution mechanism, is technically sound (Reviewer tfFr, ZaGr, EFAk, FqWw).
- Experimental results demonstrate the advantages of the proposed TEdit method as a new baseline (Reviewer tfFr, ZaGr, EFAk).
- Paper is clearly written and well-structured (Reviewer ZaGr).

Here, we also try to address some common concerns raised by the reviewers.

**Q1. Averaged results of different modifying attributes.**

As raised by reviewers ZaGr, EFAk, FqWw, the average value of modifying different attributes should be used as a more robust evaluation metric. 
***We have provided the corresponding results in Tables 1 and 2, and also put the detailed experimental results of editing different attributes in the below attached PDF file in this General Response.*** Due to the time limit of rebuttal, we compared the performance of Time Weaver and our method on Synthetic dataset and Air real-world dataset. We will conduct full experiments in the revised version of our paper.

|Method|MSE$\downarrow$|MAE$\downarrow$|TAP_trend_type$\uparrow$|TAP_trend_direction$\uparrow$|TAP_season_cycle$\uparrow$|RaTS_edited$\uparrow$|RaTS_abs_preserved$\downarrow$|
|:--------:|:--------:|:--------:|:--------:|:---------:|:--------:|:---------:|:--------:|
|Time Weaver|0.1502|0.2953|0.6441|0.9856|0.5760|0.8537|0.1206|
|TEdit-TW (ours)|**0.1184**|**0.2641**|**0.7125**|**0.9870**|**0.7122**|**0.9599**|**0.1069**|
#### Table 1. Average performance of editing different attributes on Synthetic dataset. 

|Method|TAP_city$\uparrow$|TAP_season$\uparrow$|RaTS_edited$\uparrow$|RaTS_preserved$\downarrow$|
|:--------:|:--------:|:--------:|:--------:|:--------:|
|Time Weaver|0.6386|0.2955|0.8799|0.2017|
|TEdit-TW (ours)|**0.7530**|**0.3303**|**0.9970**|**0.1753**|
#### Table 2. Average performance of editing different attributes on Air dataset.

From the above tables of average performance, our proposed method (TEdit) outperformed the baseline in all aspects on Synthetic dataset and real-world Air dataset.

However, averaging the performance on editing different attributes of time series neglects important insights on time series editing task.
From the detailed experiment results (as listed in the PDF file below), we found that the difficulty of editing different attributes is significantly different, and the average result may discard the difference. Specifically, we conclude the observations of the detailed performance on editing different attributes of time series as below.
* On both Synthetic dataset and real-world Air dataset, our method performs better than the baseline in almost all the settings.
* On Synthetic dataset, we notice that editing the attribute "trend type" is the easiest (better performance) while editing "season_cycle" is the most difficult.
* The difficulty of modifying multiple attributes is higher than that of modifying an individual attribute. 
* On Air dataset, our method far exceeds the performance of the baseline in terms of the metrics of editing attributes.

We will add these discussions in the revised paper accordingly.


**Q2. About the baselines and related works.**

Some reviewers have recommended some other related works for discussion, including wider applications on real-world datasets (Reviewer tfFr) and more discussion on differentiation to the other works (Reviewer EFAk). These suggestions indeed help improve our work. 

We briefly explain the baseline selection of our paper. As is been recognized by all the reviewers, the proposed task of time series editing is novel, while there are very few related works on this new direction. Our work compares two baselines, namely Time Weaver [1] which focuses on conditional generation of time series, and CSDI [2] which focuses on time series imputation tasks. Note that, these works are either the most cutting-edge research [1], or the classic yet pioneering work [2] in their respective fields. Both are representative in the latest research frontier in time series generation.

The works mentioned by Reviewer tfFr cover more datasets in addition to that in our paper. These new datasets extends the application of our work, and they also share similar processing pipeline to that used in our paper. We plan to leverage those new datasets to further enhance the benchmark of the proposed time series editing task, as detailed in the response of W1 of Reviewer tfFr.

Reviewer EFAk has provided some related works about time series generation and the model architecture. We have detailed the discussion of differentiation to these works in the response to W1 and W3 of Reviewer EFAk. We will add the discussion of these works in our revised paper.

[1] Narasimhan, Sai Shankar, et al. "Time weaver: A conditional time series generation model." ICML'2024.

[2] Tashiro, Yusuke, et al. "Csdi: Conditional score-based diffusion models for probabilistic time series imputation." NeurIPS'2021.

Meta-review: This paper presents a novel approach to time series editing (TSE), a critical advancement in time series data synthesis. Unlike traditional methods that generate time series from scratch, which may lead to unexpected outcomes or trivial attribute prioritization, this work focuses on editing existing time series data to match specified attributes while preserving others. The authors propose a multi-resolution modeling framework, combined with bootstrap tuning on self-generated instances, to achieve this. The experiments demonstrate that this method is effective on both synthetic and real-world datasets, showcasing its potential to directly modify attributes while maintaining data integrity.

The significance of this work lies in its practical applicability and the innovation it brings to time series analysis. By enabling controlled edits on time series data, the proposed method addresses key challenges such as data sparsity and bias in time series generation. This paper not only offers a robust solution for generating high-quality time series data but also lays the groundwork for future research in domains where precise control over data attributes is necessary. The combination of a strong methodological foundation, comprehensive experimentation, and practical relevance makes this paper a valuable contribution to the field.
Decision: Accept (poster)