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Time Series Forecasting

399 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 )

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iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

thuml/iTransformer • • 10 Oct 2023

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.

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Conditional Time Series Forecasting with Convolutional Neural Networks

litanli/wavenet-time-series-forecasting • • 14 Mar 2017

The proposed network contains stacks of dilated convolutions that allow it to access a broad range of history when forecasting, a ReLU activation function and conditioning is performed by applying multiple convolutional filters in parallel to separate time series which allows for the fast processing of data and the exploitation of the correlation structure between the multivariate time series.

3
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Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering Approach

EvgeniyaMartynova/MLiP_M5 • • 9 Oct 2017

In particular, in terms of mean sMAPE accuracy, it consistently outperforms the baseline LSTM model and outperforms all other methods on the CIF2016 forecasting competition dataset.

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Deep Adaptive Input Normalization for Time Series Forecasting

passalis/dain • • 21 Feb 2019

Deep Learning (DL) models can be used to tackle time series analysis tasks with great success.

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions

HansikaPH/time-series-forecasting • • 2 Sep 2019

Recurrent Neural Networks (RNN) have become competitive forecasting methods, as most notably shown in the winning method of the recent M4 competition.

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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.

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Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models

vincent-leguen/STDL • • NeurIPS 2019

We introduce a differentiable loss function suitable for training deep neural nets, and provide a custom back-prop implementation for speeding up optimization.

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Meta-learning framework with applications to zero-shot time-series forecasting

nixtla/transfer-learning-time-series • 7 Feb 2020

Can meta-learning discover generic ways of processing time series (TS) from a diverse dataset so as to greatly improve generalization on new TS coming from different datasets?

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Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

LeiBAI/AGCRN • • 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.

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VEST: Automatic Feature Engineering for Forecasting

vcerqueira/vest • 14 Oct 2020

Time series forecasting is a challenging task with applications in a wide range of domains.

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