Time Series Forecasting

382 papers with code • 66 benchmarks • 27 datasets

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 )

Libraries

Use these libraries to find Time Series Forecasting models and implementations

Most implemented papers

GRATIS: GeneRAting TIme Series with diverse and controllable characteristics

ykang/gratis 7 Mar 2019

The explosion of time series data in recent years has brought a flourish of new time series analysis methods, for forecasting, clustering, classification and other tasks.

Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case

LiamMaclean216/Pytorch-Transfomer 23 Jan 2020

In this paper, we present a new approach to time series forecasting.

Temporal Pattern Attention for Multivariate Time Series Forecasting

gantheory/TPA-LSTM 12 Sep 2018

To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved to some good extent by recurrent neural network (RNN) with attention mechanism.

Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics

Yunbo426/MIM CVPR 2019

Natural spatiotemporal processes can be highly non-stationary in many ways, e. g. the low-level non-stationarity such as spatial correlations or temporal dependencies of local pixel values; and the high-level variations such as the accumulation, deformation or dissipation of radar echoes in precipitation forecasting.

Probabilistic Forecasting with Temporal Convolutional Neural Network

oneday88/deepTCN 11 Jun 2019

We present a probabilistic forecasting framework based on convolutional neural network for multiple related time series forecasting.

Fast ES-RNN: A GPU Implementation of the ES-RNN Algorithm

damitkwr/ESRNN-GPU 7 Jul 2019

Due to their prevalence, time series forecasting is crucial in multiple domains.

N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting

cchallu/n-hits 30 Jan 2022

Recent progress in neural forecasting accelerated improvements in the performance of large-scale forecasting systems.

A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

yuqinie98/patchtst 27 Nov 2022

Our channel-independent patch time series Transformer (PatchTST) can improve the long-term forecasting accuracy significantly when compared with that of SOTA Transformer-based models.

PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series

WenjieDu/PyPOTS 30 May 2023

PyPOTS is an open-source Python library dedicated to data mining and analysis on multivariate partially-observed time series, i. e. incomplete time series with missing values, A. K. A.

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

thuml/iTransformer 10 Oct 2023

These forecasters leverage Transformers to model the global dependencies over temporal tokens of time series, with each token formed by multiple variates of the same timestamp.