Time Series Forecasting
436 papers with code • 71 benchmarks • 30 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 implementationsDatasets
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Most implemented papers
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
Timely accurate traffic forecast is crucial for urban traffic control and guidance.
GRATIS: GeneRAting TIme Series with diverse and controllable characteristics
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
In this paper, we present a new approach to time series forecasting.
Are Transformers Effective for Time Series Forecasting?
Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task.
A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
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
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.
Temporal Pattern Attention for Multivariate Time Series Forecasting
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
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
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
Due to their prevalence, time series forecasting is crucial in multiple domains.