Time Series Analysis
1880 papers with code • 3 benchmarks • 20 datasets
Time Series Analysis is a statistical technique used to analyze and model time-based data. It is used in various fields such as finance, economics, and engineering to analyze patterns and trends in data over time. The goal of time series analysis is to identify the underlying patterns, trends, and seasonality in the data, and to use this information to make informed predictions about future values.
( Image credit: Autoregressive CNNs for Asynchronous Time Series )
Benchmarks
These leaderboards are used to track progress in Time Series Analysis
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Libraries
Use these libraries to find Time Series Analysis models and implementationsDatasets
Speech Commands
PhysioNet Challenge 2012
EigenWorms
TSSB
Hurricane
Subtasks
Most implemented papers
Recurrent Neural Networks for Multivariate Time Series with Missing Values
Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values.
A log-linear time algorithm for constrained changepoint detection
This leads to a new algorithm which can solve problems with arbitrary affine constraints on adjacent segment means, and which has empirical time complexity that is log-linear in the amount of data.
Multivariate LSTM-FCNs for Time Series Classification
houshd/MLSTM-FCN
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Over the past decade, multivariate time series classification has received great attention.
Deep learning for time series classification: a review
hfawaz/dl-4-tsc
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We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC.
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.
Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs
We also describe novel evaluation methods for GANs, where we generate a synthetic labelled training dataset, and evaluate on a real test set the performance of a model trained on the synthetic data, and vice-versa.
Deep and Confident Prediction for Time Series at Uber
Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing.
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
ratschlab/SOM-VAE
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ICLR 2019
We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set.
Discovering physical concepts with neural networks
eth-nn-physics/nn_physical_concepts
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Despite the success of neural networks at solving concrete physics problems, their use as a general-purpose tool for scientific discovery is still in its infancy.
GluonTS: Probabilistic Time Series Models in Python
awslabs/gluonts
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We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library for deep-learning-based time series modeling.
