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Alro10 / deep-learning-time-series

Star 1.8k

List of papers, code and experiments using deep learning for time series forecasting

deep-neural-networks deep-learning time-series tensorflow prediction python3 pytorch recurrent-neural-networks lstm series-analysis forecasting-models lstm-neural-networks demand-forecasting series-forecasting sales-forecasting time-series-classification time-series-prediction time-series-forecasting series-classification forecasting-competitions
  • Updated Jun 28, 2022
  • Jupyter Notebook
pmdarima

alkaline-ml / pmdarima

Star 1.2k
Open

Support `simulate` after fitting with `auto_arima`

2
nicolaschapados
nicolaschapados commented Nov 14, 2021

Is your feature request related to a problem? Please describe.

It would be nice to directly support simulating from a fitted ARIMA model, e.g. to have a simulate method to call that would delegate to statsmodels.tsa.arima.model.ARIMA.simulate. Right now, the only way I found is to use arima_res_ member of the fitted object.

Describe the solution you'd like

Class `pmdarima.arima.ar

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good first issue feature request

ajayarunachalam / Deep_XF

Star 60

Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.

python machine-learning deep-neural-networks timeseries deep-learning time-series forecasting multivariate ecg-signal sensors forecasting-models time-series-analysis auto-ml interpretable-ai explainable-ml siamese-neural-network auto-timeseries nowcasting-models
  • Updated Dec 27, 2021
  • Jupyter Notebook

KrishnanSG / holt-winters

Star 34

The repository provides an in-depth analysis and forecast of a time series dataset as an example and summarizes the mathematical concepts required to have a deeper understanding of Holt-Winter's model. It also contains the implementation and analysis to time series anomaly detection using brutlag algorithm.

python data-science time-series analysis jupyter-notebook data-visualization forecasting modelling forecasting-models time-series-analysis anomaly-detection holt-winters triple-exponential-smoothing seasonality exponential-smoothing-models brutlag holt-winters-forecasting brutlag-algorithm holt-winter-model
  • Updated Jun 4, 2021
  • Jupyter Notebook

kennedyCzar / FORECASTING-1.0

Star 25

Predictive algorithm for forecasting the mexican stock exchange. Machine Learning approach to forecast price and Indicator behaviours of MACD, Bollinger and SuperTrend strategy

neural-networks forecasting rnn lightgbm adaboost fibonacci-series prophet forecasting-models ema rsi time-series-analysis facebook-prophet-forecasting time-series-prediction macd relative-strength-index time-series-forecasting facebook-prophet signal-generator supertrend pivot-point
  • Updated Mar 28, 2020
  • Python

openclimatefix / graph_weather

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Star 18
Open

Add TPU support

jacobbieker
jacobbieker commented Jun 16, 2022

Detailed Description

Currently, the graph neural network library dependencies don't support TPUs with pytorch geometric, or don't seem to at least because of custom kernels. We could add a Jax version for TPU support? The original model was implemented in Jax apparently.

Context

Being able to use TPUs could speed up

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enhancement good first issue help wanted

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