Time Series
2137 papers with code • 5 benchmarks • 9 datasets
Benchmarks
These leaderboards are used to track progress in Time Series
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Libraries
Use these libraries to find Time Series models and implementationsDatasets
ETT
QM9
Weather
neuronIO
BorealTC
MOSAD
HASCD
Most implemented papers
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
google-research/google-research
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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.
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.
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
Probabilistic forecasting, i. e. estimating the probability distribution of a time series' future given its past, is a key enabler for optimizing business processes.
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.
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
microsoft/qlib
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The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades.
Latent ODEs for Irregularly-Sampled Time Series
Time series with non-uniform intervals occur in many applications, and are difficult to model using standard recurrent neural networks (RNNs).
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to inter- and intra-subject differences, as well as to inherent noise associated with such data.
Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline
cauchyturing/UCR_Time_Series_Classification_Deep_Learning_Baseline
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We propose a simple but strong baseline for time series classification from scratch with deep neural networks.
Multitask learning and benchmarking with clinical time series data
yerevann/mimic3-benchmarks
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Health care is one of the most exciting frontiers in data mining and machine learning.
InceptionTime: Finding AlexNet for Time Series Classification
TSC is the area of machine learning tasked with the categorization (or labelling) of time series.
