# Time Series Forecasting

540 papers with code • 72 benchmarks • 32 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## Datasets

## Subtasks

## Most implemented papers

# Sequence to Sequence Learning with Neural Networks

Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector.

# Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

Multi-horizon forecasting problems often contain a complex mix of inputs -- including static (i. e. time-invariant) covariates, known future inputs, and other exogenous time series that are only observed historically -- without any prior information on how they interact with the target.

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

Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation.

# DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks

Probabilistic forecasting, i. e. estimating the probability distribution of a time series' future given its past, is a key enabler for optimizing business processes.

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

We focus on solving the univariate times series point forecasting problem using deep learning.

# Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain.

# AA-Forecast: Anomaly-Aware Forecast for Extreme Events

Moreover, the framework employs a dynamic uncertainty optimization algorithm that reduces the uncertainty of forecasts in an online manner.

# iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

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.

# 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.

# GluonTS: Probabilistic Time Series Models in Python

We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library for deep-learning-based time series modeling.