Time Series
 
Edit

Time Series Forecasting

397 papers with code • 66 benchmarks • 28 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

Most implemented Social Latest No code

Sequence to Sequence Learning with Neural Networks

bentrevett/pytorch-seq2seq • • NeurIPS 2014

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.

73
Paper
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.

34
Paper
Code

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

laiguokun/multivariate-time-series-data • 21 Mar 2017

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.

19
Paper
Code

DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks

jdb78/pytorch-forecasting • • 13 Apr 2017

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

19
Paper
Code

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

unit8co/darts • • ICLR 2020

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

19
Paper
Code

Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

liyaguang/DCRNN • • ICLR 2018

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

16
Paper
Code

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.

7
Paper
Code

Deep and Confident Prediction for Time Series at Uber

PawaritL/BayesianLSTM • • 6 Sep 2017

Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing.

6
Paper
Code

GluonTS: Probabilistic Time Series Models in Python

awslabs/gluonts • • 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.

6
Paper
Code

Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

VeritasYin/STGCN_IJCAI-18 • • 14 Sep 2017

Timely accurate traffic forecast is crucial for urban traffic control and guidance.

5
Paper
Code
Contact us on: hello@paperswithcode.com . Papers With Code is a free resource with all data licensed under CC-BY-SA.
Terms Data policy Cookies policy