TimesNAS

This is the official implementation of the Time Series-Aware Zero-Shot Neural Architecture Search for General Time-Series Analysis paper Under Review by IEEE Transactions on Neural Networks and Learning Systems

Abstract

Designing effective neural networks from scratch for various time-series analysis tasks, such as activity recognition, fault detection, and traffic forecasting, is time-consuming and heavily relies on human labor. This paper thereby aims to answer the following question: how to build a unified zero-shot neural architecture search framework that effectively designs neural ar- chitectures for a variety of tasks and input time series? However, building a universal framework for different tasks comes with challenges. First, we need a unified backbone search space that performs well across diverse analysis tasks. Second, we need a time series-aware zero-shot proxy that consistently correlates with the downstream performance across different characteristics of time-series datasets. To address these challenges, for the first time, we propose a framework for general Time-series analysis with zero-shot Neural Architecture Search named TimesNAS. TimesNAS extends a state-of-the-art foundation model into a two- level search space and enhances a zero-shot proxy by exploiting decomposed time-series properties. Empirically, we show that the architectures found by TimesNAS gain improvement up to 23.6% over state-of-the-art hand-crafted baselines in five mainstream time-series data mining tasks, including short- and long-term forecasting, classification, anomaly detection, and imputation.

Benchmark Datasets

Downstream Analysis Tasks	Benchmarks	Evaluation Metrics	Series Length
Short-term Forecasting	M4 (6 subsets)	SMAPE, MASE, OWA	6 ~ 48
Long-term Forecasting	ETT (4 subsets), Electricity, Traffic, Weather, Exchange	MSE, MAE	96 ~ 720
Classification	UEA (10 subsets)	Accuracy	29 ~ 1751
Anomaly Detection	SMD, MSL, SMAP, SWaT, PSM	Precision, Recall, F1	100
Imputation	ETT (4 subsets), Electricity, Weather	MSE, MAE	96

Dataset descriptions. The dataset size is organized in (Train, Validation, Test)

Tasks	Dataset	Dim	Series Length	Dataset Size	Information (Freq.)
Short-term Forecasting	M4-Yearly	1	6	(23000, 0, 23000)	Demographic
	M4-Quarterly	1	8	(24000, 0, 24000)	Finance
	M4-Monthly	1	18	(48000, 0, 48000)	Industry
	M4-Weekly	1	13	(359, 0, 359)	Macro
	M4-Daily	1	14	(4227, 0, 4227)	Micro
	M4-Hourly	1	48	(414, 0, 414)	Other
Long-term Forecasting	ETTm1, ETTm2	7	{96, 192, 336, 720}	(34465, 11521, 11521)	Electricity (15 mins)
	ETTh1, ETTh2	7	{96, 192, 336, 720}	(8545, 2881, 2881)	Electricity (15 mins)
	Electricity	321	{96, 192, 336, 720}	(18317, 2633, 5261)	Electricity (Hourly)
	Traffic	862	{96, 192, 336, 720}	(12185, 1757, 3509)	Transportation (Hourly)
	Weather	21	{96, 192, 336, 720}	(36792, 5271, 10540)	Weather (10 mins)
	Exchange	8	{96, 192, 336, 720}	(5120, 665, 1422)	Exchange rate (Daily)
Classification	EthanolConcentration	3	1751	(261, 0, 263)	Alcohol Industry
	FaceDetection	144	62	(5890, 0, 3524)	Face (250Hz)
	Handwriting	3	152	(150, 0, 850)	Handwriting
	Heartbeat	61	405	(204, 0, 205)	Heart Beat
	JapaneseVowels	12	29	(270, 0, 370)	Voice
	PEMS-SF	963	144	(267, 0, 173)	Transportation (Daily)
	SelfRegulationSCP1	6	896	(268, 0, 293)	Health (256Hz)
	SelfRegulationSCP2	7	1152	(200, 0, 180)	Health (256Hz)
	SpokenArabicDigits	13	93	(6599, 0, 2199)	Voice (11025Hz)
	UWaveGestureLibrary	3	315	(120, 0, 320)	Gesture
Anomaly Detection	SMD	38	100	(566724, 141681, 708420)	Server Machine
	MSL	55	100	(44653, 11664, 73729)	Spacecraft
	SMAP	25	100	(108146, 27037, 427617)	Spacecraft
	SWaT	51	100	(396000, 99000, 449919)	Infrastructure
	PSM	25	100	(105984, 26497, 87841)	Server Machine
Imputation	ETTm1, ETTm2	7	96	(34465, 11521, 11521)	Electricity (15 mins)
	ETTh1, ETTh2	7	96	(8545, 2881, 2881)	Electricity (15 mins)
	Electricity	321	96	(18317, 2633, 5261)	Electricity (15 mins)
	Weather	21	96	(36792, 5271, 10540)	Weather (10 mins)

Default Training Hyperparamters

Tasks / Configurations	LR*	Loss	Batch Size	Epochs
Long-term Forecasting	$10^{−4}$	MSE	32	10
Short-term Forecasting	$10^{−3}$	SMAPE	16	10
Imputation	$10^{−3}$	MSE	16	10
Classification	$10^{−3}$	Cross Entropy	16	30
Anomaly Detection	$10^{−4}$	MSE	128	10
* LR means the initial learning rate.

Usage

1. Installation. Install Python 3.9. For convenience, please run the following command.

pip install -r requirements.txt

2. Prepare Data. You can obtained the well pre-processed datasets from Dropbox. Then, place the downloaded data under the folder ./dataset.

3. Search and Train-Test Models. Examples of search and train scripts are in the scripts.sh file. For example,

# run search for classification task on JapaneseVowels dataset
python -u searcher.py --task_name classification --gpu 3 --population_size 1000 --data_name JapaneseVowels
# run the found architecture training and testing
python -u trainer.py --task_name classification --gpu 0 --population_size 1000 --method_name TimesNAS --data_name JapaneseVowels

Notebook Example for Demonstration: TimesNAS_Example_Demo.ipynb (Long-term Forecasting Task) with N = 100

Citation

TBD

Acknowledgement

This project and datasets are constructed based on https://github.com/thuml/Time-Series-Library.