Univariate Time Series Forecasting

10 papers with code • 2 benchmarks • 5 datasets

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Most implemented papers

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

laiguokun/LSTNet 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.

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.

Temporal Pattern Attention for Multivariate Time Series Forecasting

gantheory/TPA-LSTM 12 Sep 2018

To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved to some good extent by recurrent neural network (RNN) with attention mechanism.

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.

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.

Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction

cure-lab/SCINet 17 Jun 2021

Motivated by the above, in this paper, we propose a novel neural network architecture that conducts sample convolution and interaction for temporal modeling and apply it for the time series forecasting problem, namely \textbf{SCINet}.

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

nnzhan/MTGNN 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.

Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN

koochali/forgan 29 Mar 2019

To investigate probabilistic forecasting of ForGAN, we create a new dataset and demonstrate our method abilities on it.

On projection methods for functional time series forecasting

aefdz/nnFTS 10 May 2021

The second one is based on a selection of curves, termed \emph{the curve envelope}, that aims to be representative in shape and magnitude of the most recent functional observation, either a whole curve or the observed part of a partially observed curve.

Greykite: Deploying Flexible Forecasting at Scale at LinkedIn

linkedin/greykite 15 Jul 2022

We present Greykite, an open-source Python library for forecasting that has been deployed on over twenty use cases at LinkedIn.