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 )

Benchmarks

Libraries

Datasets

Subtasks

Most implemented papers

Sequence to Sequence Learning with Neural Networks

Content

Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks

Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

GluonTS: Probabilistic Time Series Models in Python

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Deep and Confident Prediction for Time Series at Uber