Univariate Time Series Forecasting
20 papers with code • 3 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in Univariate Time Series Forecasting
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ETT
Electricity
ExtMarker
Hurricane
Most implemented papers
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.
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.
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning.
Temporal Pattern Attention for Multivariate Time Series Forecasting
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.
Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering Approach
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.
SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction
cure-lab/SCINet
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One unique property of time series is that the temporal relations are largely preserved after downsampling into two sub-sequences.
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
nnzhan/MTGNN
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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
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
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
We present Greykite, an open-source Python library for forecasting that has been deployed on over twenty use cases at LinkedIn.
