https://github.com/rbhatia46/automl-timeseries-forecasting
Use of ATSPy, a library to automate time series forecasting, using multiple models and ensembles.
https://github.com/rbhatia46/automl-timeseries-forecasting
automl-timeseries-forecasting forecasting time-series
Last synced: 6 months ago
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Use of ATSPy, a library to automate time series forecasting, using multiple models and ensembles.
- Host: GitHub
- URL: https://github.com/rbhatia46/automl-timeseries-forecasting
- Owner: rbhatia46
- Created: 2020-07-18T10:54:25.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2020-09-03T05:22:08.000Z (about 5 years ago)
- Last Synced: 2025-03-25T00:04:50.242Z (7 months ago)
- Topics: automl-timeseries-forecasting, forecasting, time-series
- Language: Jupyter Notebook
- Homepage:
- Size: 294 KB
- Stars: 8
- Watchers: 2
- Forks: 5
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# AutoML-TimeSeries-Forecasting
Use of [ATSPy](https://github.com/firmai/atspy), a library to automate time series forecasting, using multiple models and ensembles.
#### Automated Models
1. ```ARIMA``` - Automated ARIMA Modelling
1. ```Prophet``` - Modeling Multiple Seasonality With Linear or Non-linear Growth
1. ```HWAAS``` - Exponential Smoothing With Additive Trend and Additive Seasonality
1. ```HWAMS``` - Exponential Smoothing with Additive Trend and Multiplicative Seasonality
1. ```PYAF``` - Feature Generating Model (slow and underforms)
1. ```NBEATS``` - Neural basis expansion analysis (now fixed at 20 Epochs)
1. ```Gluonts``` - RNN-based Model (now fixed at 20 Epochs)
1. ```TATS``` - Seasonal and Trend no Box Cox
1. ```TBAT``` - Trend and Box Cox
1. ```TBATS1``` - Trend, Seasonal (one), and Box Cox
1. ```TBATP1``` - TBATS1 but Seasonal Inference is Hardcoded by Periodicity
1. ```TBATS2``` - TBATS1 With Two Seasonal Periods
#### Why AtsPy?
1. Implements all time series models in a unified manner by simply running ```AutomatedModel(df)```.
2. Automatically identify the seasonalities in your data using singular spectrum analysis, periodograms, and peak analysis.
3. Identifies and makes accessible the best model for your time series using in-sample validation methods.
4. Combines the predictions of all these models in a simple (average) and complex (GBM) ensembles for improved performance.
5. Where appropriate models have been developed to use GPU resources to speed up the automation process.
A blog post on Atspy : https://analyticsindiamag.com/hands-on-guide-to-atspy-for-automating-the-time-series-forecasting/