Time Series Forecasting

Time Series Forecasting is the task of fitting a model to historical, time-stamped data in order to predict future values. Traditional approaches include moving average, exponential smoothing, and ARIMA, though models as various as RNNs, Transformers, or XGBoost can also be applied. The most popular benchmark is the ETTh1 dataset. Models are typically evaluated using the Mean Square Error (MSE) or Root Mean Square Error (RMSE).

( Image credit: ThaiBinh Nguyen )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1476–1500 of 1609 papers

Title	Date	Tasks	Status
Conditional Mutual information-based Contrastive Loss for Financial Time Series Forecasting	Feb 18, 2020	Representation LearningTime Series	—Unverified
Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data	Feb 6, 2020	ManagementPrediction	—Unverified
Error-feedback stochastic modeling strategy for time series forecasting with convolutional neural networks	Feb 3, 2020	Time SeriesTime Series Analysis	—Unverified
A clustering approach to time series forecasting using neural networks: A comparative study on distance-based vs. feature-based clustering methods	Jan 27, 2020	Anomaly DetectionClustering	CodeCode Available
For2For: Learning to forecast from forecasts	Jan 14, 2020	BIG-bench Machine LearningTime Series	—Unverified
Temporally Folded Convolutional Neural Networks for Sequence Forecasting	Jan 10, 2020	Time SeriesTime Series Analysis	—Unverified
Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability	Jan 2, 2020	BenchmarkingManagement	—Unverified
Towards Better Forecasting by Fusing Near and Distant Future Visions	Dec 11, 2019	DecoderMultivariate Time Series Forecasting	CodeCode Available
Hidden Markov Model: Tutorial	Dec 10, 2019	Action RecognitionBayesian Inference	—Unverified
Warped Input Gaussian Processes for Time Series Forecasting	Dec 5, 2019	Gaussian ProcessesTime Series	CodeCode Available
Work-in-progress: a deep learning strategy for I/O scheduling in storage systems	Dec 3, 2019	Hardware Aware Neural Architecture SearchIrregular Time Series	CodeCode Available
On model selection for scalable time series forecasting in transport networks	Nov 29, 2019	ClusteringDeep Learning	—Unverified
Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019	Nov 29, 2019	Systematic Literature ReviewTime Series	—Unverified
Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values	Nov 22, 2019	Missing ValuesMultivariate Time Series Forecasting	—Unverified
Comparison of Deep learning models on time series forecasting : a case study of Dissolved Oxygen Prediction	Nov 17, 2019	Deep LearningSmall Data Image Classification	CodeCode Available
Performance evaluation of deep neural networks for forecasting time-series with multiple structural breaks and high volatility	Nov 14, 2019	Time SeriesTime Series Analysis	CodeCode Available
DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting	Nov 3, 2019	Multivariate Time Series ForecastingTime Series	CodeCode Available
Textual Data for Time Series Forecasting	Oct 25, 2019	Time SeriesTime Series Analysis	—Unverified
You May Not Need Order in Time Series Forecasting	Oct 21, 2019	Time SeriesTime Series Analysis	—Unverified
Probabilistic sequential matrix factorization	Oct 9, 2019	ImputationMultivariate Time Series Forecasting	CodeCode Available
DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps	Oct 3, 2019	ClusteringDeep Clustering	CodeCode Available
Machine Learning vs Statistical Methods for Time Series Forecasting: Size Matters	Sep 29, 2019	BIG-bench Machine LearningTime Series	CodeCode Available
COMBINED FLEXIBLE ACTIVATION FUNCTIONS FOR DEEP NEURAL NETWORKS	Sep 25, 2019	image-classificationImage Classification	—Unverified
Core Network Management Procedures for Self-Organized and Sustainable 5G Cellular Networks	Sep 19, 2019	Edge-computingManagement	—Unverified
AI Modelling and Time-series Forecasting Systems for Trading Energy Flexibility in Distribution Grids	Sep 18, 2019	Time SeriesTime Series Analysis	—Unverified

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All datasets ETTh1 (336) Multivariate ETTh1 (720) Multivariate ETTh2 (336) Multivariate ETTh2 (720) Multivariate ETTh1 (192) Multivariate ETTh2 (192) Multivariate ETTh2 (96) Multivariate ETTh1 (96) Multivariate Weather (192)ETTh1 (720) Univariate Weather (96)Electricity (96)

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Informer	MSE	0.88	—	Unverified
2	QuerySelector	MSE	0.85	—	Unverified
3	Transformer	MSE	0.83	—	Unverified
4	Aaren	MSE	0.65	—	Unverified
5	RPMixer	MSE	0.52	—	Unverified
6	MOIRAILarge	MSE	0.51	—	Unverified
7	ATFNet	MSE	0.51	—	Unverified
8	Autoformer	MSE	0.51	—	Unverified
9	SCINet	MSE	0.5	—	Unverified
10	S-Mamba	MSE	0.49	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	QuerySelector	MSE	1.12	—	Unverified
2	Transformer	MSE	1.11	—	Unverified
3	Informer	MSE	0.94	—	Unverified
4	GLinear	MSE	0.59	—	Unverified
5	SCINet	MSE	0.54	—	Unverified
6	MoLE-DLinear	MSE	0.51	—	Unverified
7	PRformer	MSE	0.49	—	Unverified
8	TEFN	MSE	0.48	—	Unverified
9	DLinear	MSE	0.47	—	Unverified
10	FiLM	MSE	0.47	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Transformer	MSE	2.66	—	Unverified
2	QuerySelector	MSE	2.32	—	Unverified
3	Informer	MSE	1.67	—	Unverified
4	DLinear	MSE	0.45	—	Unverified
5	TEFN	MSE	0.42	—	Unverified
6	MoLE-DLinear	MSE	0.42	—	Unverified
7	FiLM	MSE	0.38	—	Unverified
8	MoLE-RLinear	MSE	0.37	—	Unverified
9	SCINet	MSE	0.37	—	Unverified
10	PRformer	MSE	0.36	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Transformer	MSE	3.18	—	Unverified
2	QuerySelector	MSE	3.07	—	Unverified
3	Informer	MSE	2.34	—	Unverified
4	DLinear	MSE	0.61	—	Unverified
5	MoLE-DLinear	MSE	0.61	—	Unverified
6	SCINet	MSE	0.48	—	Unverified
7	FiLM	MSE	0.44	—	Unverified
8	TEFN	MSE	0.43	—	Unverified
9	TiDE	MSE	0.42	—	Unverified
10	MoLE-RLinear	MSE	0.41	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MoLE-DLinear	MSE	0.45	—	Unverified
2	TEFN	MSE	0.43	—	Unverified
3	FiLM	MSE	0.41	—	Unverified
4	PatchTST/64	MSE	0.41	—	Unverified
5	TiDE	MSE	0.41	—	Unverified
6	NLinear	MSE	0.41	—	Unverified
7	DiPE-Linear	MSE	0.41	—	Unverified
8	DLinear	MSE	0.41	—	Unverified
9	RLinear	MSE	0.4	—	Unverified
10	MoLE-RLinear	MSE	0.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DLinear	MSE	0.38	—	Unverified
2	TEFN	MSE	0.38	—	Unverified
3	MoLE-DLinear	MSE	0.36	—	Unverified
4	FiLM	MSE	0.36	—	Unverified
5	NLinear	MSE	0.34	—	Unverified
6	PatchTST/64	MSE	0.34	—	Unverified
7	MoLE-RLinear	MSE	0.34	—	Unverified
8	TiDE	MSE	0.33	—	Unverified
9	LTBoost (drop_last=false)	MSE	0.33	—	Unverified
10	PRformer	MSE	0.33	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DLinear	MSE	0.29	—	Unverified
2	TEFN	MSE	0.29	—	Unverified
3	MoLE-DLinear	MSE	0.29	—	Unverified
4	FiLM	MSE	0.28	—	Unverified
5	NLinear	MSE	0.28	—	Unverified
6	TSMixer	MSE	0.28	—	Unverified
7	DiPE-Linear	MSE	0.28	—	Unverified
8	PatchTST/64	MSE	0.27	—	Unverified
9	MoLE-RLinear	MSE	0.27	—	Unverified
10	TiDE	MSE	0.27	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TEFN	MSE	0.38	—	Unverified
2	MoLE-DLinear	MSE	0.38	—	Unverified
3	TiDE	MSE	0.38	—	Unverified
4	MoLE-RLinear	MSE	0.38	—	Unverified
5	FiLM	MSE	0.37	—	Unverified
6	PatchTST/64	MSE	0.37	—	Unverified
7	DiPE-Linear	MSE	0.37	—	Unverified
8	TSMixer	MSE	0.37	—	Unverified
9	RLinear	MSE	0.37	—	Unverified
10	TTM	MSE	0.36	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TEFN	MSE	0.23	—	Unverified
2	DLinear	MSE	0.22	—	Unverified