Time Series Forecasting

108 papers with code • 10 benchmarks • 5 datasets

Time series forecasting is the task of predicting future values of a time series (as well as uncertainty bounds).

( Image credit: DTS )

Greatest papers with code

Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

google-research/google-research 19 Dec 2019

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.

Interpretable Machine Learning Time Series +1

Ludwig: a type-based declarative deep learning toolbox

uber/ludwig 17 Sep 2019

In this work we present Ludwig, a flexible, extensible and easy to use toolbox which allows users to train deep learning models and use them for obtaining predictions without writing code.

Image Captioning Image Classification +12

GluonTS: Probabilistic Time Series Models in Python

awslabs/gluon-ts 12 Jun 2019

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

Anomaly Detection Time Series +2

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

zhouhaoyi/Informer2020 14 Dec 2020

Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning.

Multivariate Time Series Forecasting Time Series +1

Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

benedekrozemberczki/pytorch_geometric_temporal NeurIPS 2020

We further propose an Adaptive Graph Convolutional Recurrent Network (AGCRN) to capture fine-grained spatial and temporal correlations in traffic series automatically based on the two modules and recurrent networks.

Graph Generation Multivariate Time Series Forecasting +4

Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

benedekrozemberczki/pytorch_geometric_temporal 24 May 2020

Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic.

Graph Learning Multivariate Time Series Forecasting +2

Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting

AIStream-Peelout/flow-forecast NeurIPS 2019

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

Image Generation Time Series +1