TSGBench is the inaugural TSG benchmark designed for the Time Series Generation (TSG) task. We are excited to share that TSGBench has received the Best Research Paper Award Nomination at VLDB 2024 🏆

TSGAssist is an interactive assistant that integrates the strengths of TSGBench and utilizes Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) for TSG recommendations and benchmarking 🤖📊

We are actively exploring industrial collaborations in time series analytics. Please feel free to reach out (yihao_ang AT comp.nus.edu.sg) if interested 🤝✨

Table of Contents

• Overview of TSGBench
  • Time Series Generation (TSG)
  • TSG Methods
  • TSG Datasets
  • TSG Evaluation Measures
  • Benchmarking Results
    • Main Results
    • Visualization
    • Generalization Test
• Overview of TSGAssist
• Getting Started with TSGBench
  • Configuration
  • Running TSGBench

Overview of TSGBench

Time Series Generation (TSG)

Time Series Generation (TSG) is crucial in a range of applications, including data augmentation, anomaly detection, and privacy preservation. Given an input time series, TSG aims to produce time series akin to the original, preserving temporal dependencies and dimensional correlations while ensuring the generated time series remains useful for various downstream tasks.

TSG Methods

TSGBench surveys a diverse range of Time Series Generation (TSG) methods by different backbone models and their specialties. The table below provides an overview of these methods along with their references.

Time	Paper	Model	Specialty	Source Codes
2016	C-RNN-GAN	GAN	music	https://github.com/olofmogren/c-rnn-gan
2017	RGAN	GAN	general (w/ medical) TS	https://github.com/ratschlab/RGAN
2018	T-CGAN	GAN	irregular TS	https://github.com/gioramponi/GAN_Time_Series
2019	WaveGAN	GAN	audio	https://github.com/chrisdonahue/wavegan
2019	TimeGAN	GAN	general TS	https://github.com/jsyoon0823/TimeGAN
2020	TSGAN	GAN	general TS	Community implementation: https://github.com/Yashkataria/CGAN-for-time-series
2020	DoppelGANger	GAN	general TS	https://github.com/fjxmlzn/DoppelGANger
2020	SigCWGAN	GAN	long financial TS	https://github.com/SigCGANs/Conditional-Sig-Wasserstein-GANs
2020	Quant GANs	GAN	long financial TS	Community implementations: https://github.com/ICascha/QuantGANs-replication, https://github.com/JamesSullivan/temporalCN
2020	COT-GAN	GAN	TS and video	https://github.com/tianlinxu312/cot-gan
2021	Sig-WGAN	GAN	financial TS	https://github.com/SigCGANs/Sig-Wasserstein-GANs
2021	TimeGCI	GAN	general TS	No code available
2021	RTSGAN	GAN	general (w/ incomplete) TS	https://github.com/acphile/RTSGAN
2022	PSA-GAN	GAN	general (w/ forecasting) TS	https://github.com/mbohlkeschneider/psa-gan
2022	CEGEN	GAN	general TS	No code available
2022	TTS-GAN	GAN	general TS	https://github.com/imics-lab/tts-gan
2022	TsT-GAN	GAN	general TS	No code available
2022	COSCI-GAN	GAN	general TS	https://github.com/aliseyfi75/COSCI-GAN
2023	AEC-GAN	GAN	long TS	https://github.com/HBhswl/AEC-GAN
2023	TT-AAE	GAN	general TS	https://openreview.net/forum?id=fI3y_Dajlca
2021	TimeVAE	VAE	general TS	https://github.com/abudesai/timeVAE
2023	CRVAE	VAE	medical TS & causal discovery	https://github.com/sinhasam/CRVAE
2023	TimeVQVAE	VAE	general TS	https://github.com/ML4ITS/TimeVQVAE
2023	TimeVQVAE w/ ESS	VAE	general TS	https://github.com/ML4ITS/TimeVQVAE?tab=readme-ov-file#enhanced-sampling-scheme-2
2023	KVAE	VAE	general (w/ irregular) TS	No code available
2020	CTFP	Flow	general TS	https://github.com/BorealisAI/continuous-time-flow-process
2021	Fourier Flow	Flow	general TS	https://github.com/ahmedmalaa/Fourier-flows
2018	Neural ODE	ODE + RNN	general TS	https://github.com/rtqichen/torchdiffeq
2019	ODE-RNN	ODE + RNN	irregular TS	https://github.com/YuliaRubanova/latent_ode
2021	Neural SDE	ODE + GAN	general TS	https://github.com/google-research/torchsde
2022	GT-GAN	ODE + GAN	general (w/ irregular) TS	https://openreview.net/forum?id=ez6VHWvuXEx
2023	LS4	ODE + VAE	general (w/ forecasting) TS	https://github.com/alexzhou907/ls4
2023	SGM	Diffusion	general TS	No code available

TSG Datasets

TSGBench selects ten real-world datasets from various domains, ensuring a wide coverage of scenarios for TSG evaluation. Here, $R$ is the number of sub-matrics after preprocessing, $l$ is the series length, and $N$ is the number of series in the sub-matrics.

Dataset	$R$	$l$	$N$	Domain	Link
DLG	246	14	20	Traffic	http://archive.ics.uci.edu/dataset/157/dodgers+loop+sensor
Stock	3294	24	6	Financial	https://finance.yahoo.com/quote/GOOG/history?p=GOOG
Stock Long	3204	125	6	Financial	https://finance.yahoo.com/quote/GOOG/history?p=GOOG
Exchange	6715	125	8	Financial	https://github.com/laiguokun/multivariate-time-series-data
Energy	17739	24	28	Appliances	http://archive.ics.uci.edu/dataset/374/appliances+energy+prediction
Energy Long	17649	125	28	Appliances	http://archive.ics.uci.edu/dataset/374/appliances+energy+prediction
EEG	13366	128	14	Medical	https://archive.ics.uci.edu/dataset/264/eeg+eye+state
HAPT	1514	128	6	Medical	https://archive.ics.uci.edu/dataset/341/smartphone+based+recognition+of+human+activities+and+postura+transitions
Air	7731	168	6	Sensor	https://www.microsoft.com/en-us/research/project/urban-air/
Boiler	80935	192	11	Industrial	https://github.com/DMIRLAB-Group/SASA/tree/main/datasets/Boiler

TSG Evaluation Measures

TSGBench considers the following evaluation measures, ranking analysis, and a novel generalization test by Domain Adaptation (DA).

1. Model-based Measures
   • Discriminitive Score (DS)
   • Predictive Score (PS)
   • Contextual-FID (C-FID)
2. Feature-based Measures
   • Marginal Distribution Difference (MDD)
   • AutoCorrelation Difference (ACD)
   • Skewness Difference (SD)
   • Kurtosis Difference (KD)
3. Distance-based Measures
   • Euclidean Distance (ED)
   • Dynamic Time Warping (DTW)
4. Visualization
   • t-SNE
   • Distribution Plot
5. Training Efficiency
   • Training Time

Benchmarking Results

Main Results

Visualization

Generalization Test

Overview of TSGAssist

TSGAssist is an interactive assistant harnessing LLMs and RAG for time series generation recommendations and benchmarking.

• It offers multi-round personalized recommendations through a conversational interface that bridges the cognitive gap,
• It enables the direct application and instant evaluation of users' data, providing practical insights into the effectiveness of various methods.

Getting Started with TSGBench

Configuration

We recommend using conda to create a virtual environment for TSGBench.

conda create -n tsgbench python=3.7
conda activate tsgbench
conda install --file requirements.txt

The configuration file ./config/config.yaml contains various settings to run TSGBench. It is structured into the following sections:

• Preprocessing: Configures data preprocessing. Specify the input data path using the preprocessing.original_data_path and the output path for processed data using preprocessing.output_ori_path.
• Generation: Contains the settings related to data generation.
• Evaluation: Includes the parameters required for evaluating the model's performance.

Running TSGBench

1. Set Input Data: Update the preprocessing.original_data_path in config.yaml to specify the location of your input data.

2. Run TSGBench: Execute the main script by running python ./main.py. By default, this will run the preprocessing, generation, and evaluation stages in sequence. You can skip or adjust these steps by modifying the relevant sections in the configuration file. In particular,

   (1) Preprocessing: During preprocessing, data is processed and saved to the path specified by preprocessing.output_ori_path in the configuration file.

   (2) Generation: Place your designated model structure under the ./model directory. In ./src/generation, point the model entry to your model. If necessary, provide pretrained parameters by specifying them under generation.pretrain_path. Generated data will be saved at generation.output_gen_path.

   (3) Evaluation: Select specific evaluation measures by updating the evaluation.method_list in the configuration file. The evaluation results will be saved to the path specified in evaluation.result_path.

References

Please consider citing our work if you use TSGBench (and/or TSGAssist) in your research:

# TSGBench
@article{ang2023tsgbench,
  title        = {TSGBench: Time Series Generation Benchmark},
  author       = {Ang, Yihao and Huang, Qiang and Bao, Yifan and Tung, Anthony KH and Huang, Zhiyong},
  journal      = {Proc. {VLDB} Endow.},
  volume       = {17},
  number       = {3},
  pages        = {305--318},
  year         = {2023}
}

# TSGAssist
@article{ang2024tsgassist,
  title        = {TSGAssist: An Interactive Assistant Harnessing LLMs and RAG for Time Series Generation Recommendations and Benchmarking},
  author       = {Ang, Yihao and Bao, Yifan and Huang, Qiang and Tung, Anthony KH and Huang, Zhiyong},
  journal      = {Proc. {VLDB} Endow.},
  volume       = {17},
  number       = {12},
  pages        = {4309--4312},
  year         = {2024}
}