Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Darts | 5,575 | 7 | 10 hours ago | 25 | June 22, 2022 | 206 | apache-2.0 | Python | ||
A python library for user-friendly forecasting and anomaly detection on time series. | ||||||||||
Tsai | 3,205 | 1 | 3 days ago | 41 | April 19, 2022 | 22 | apache-2.0 | Jupyter Notebook | ||
Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai | ||||||||||
Pytorch Forecasting | 2,666 | 4 | 9 days ago | 33 | May 23, 2022 | 359 | mit | Python | ||
Time series forecasting with PyTorch | ||||||||||
Deep Learning Time Series | 1,811 | 7 months ago | 8 | apache-2.0 | Jupyter Notebook | |||||
List of papers, code and experiments using deep learning for time series forecasting | ||||||||||
Awesome_time_series_in_python | 1,811 | a month ago | 4 | |||||||
This curated list contains python packages for time series analysis | ||||||||||
Flow Forecast | 1,365 | 9 hours ago | 87 | gpl-3.0 | Python | |||||
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting). | ||||||||||
Pmdarima | 1,329 | 6 | 45 | 7 days ago | 21 | February 22, 2022 | 40 | mit | Python | |
A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function. | ||||||||||
Pytorch Ts | 943 | 4 days ago | 9 | April 24, 2022 | 57 | mit | Python | |||
PyTorch based Probabilistic Time Series forecasting framework based on GluonTS backend | ||||||||||
Autots | 690 | 1 | 5 days ago | 40 | June 20, 2022 | 13 | mit | Python | ||
Automated Time Series Forecasting | ||||||||||
N Beats | 686 | 20 days ago | mit | Python | ||||||
Keras/Pytorch implementation of N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. |
DISLCLAIMER: THIS IS NOT THE PAPERS CODE. THIS DOES NOT HAVE SPARSITY. THIS IS TEACHER FORCED LEARNING. Only tried to replicate the simple example without sparsity. Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting (NeurIPS 2019)
Able to match the results of the paper for the synthetic dataset as shown in the table below
The synthetic dataset was constructed as shown below
A nice visualization of how the attention layers look at the signal for predicting the last timestep t=t0+24-1