https://github.com/mwoss/timeseries-decision-trees
The use of Gradient Boosted Decision Trees algorithms to predict timeseries data
https://github.com/mwoss/timeseries-decision-trees
decision-trees ets timeseries xgboost
Last synced: over 1 year ago
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The use of Gradient Boosted Decision Trees algorithms to predict timeseries data
- Host: GitHub
- URL: https://github.com/mwoss/timeseries-decision-trees
- Owner: mwoss
- Created: 2019-10-22T21:49:03.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-12-10T21:28:03.000Z (over 6 years ago)
- Last Synced: 2025-01-24T08:23:06.027Z (over 1 year ago)
- Topics: decision-trees, ets, timeseries, xgboost
- Language: Jupyter Notebook
- Homepage:
- Size: 5.27 MB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
## Forcasting time series data using Gradient Boosted Decision Trees and ETS models
Gradient Boosted Decision Trees algorithm for time series forcasting using XGBoost library
## Datasets
#### List of all dataset used for learning/research process:
* [Coinbase - Bitcoin historical data | kaggle.com](https://www.kaggle.com/mczielinski/bitcoin-historical-data)
* [European markets data (hourly/daily) | cryptodatadownload.com](http://www.cryptodatadownload.com/data/euro/)
* [CoinMetrics cryptocurrency data | coinmetrics.io](https://coinmetrics.io/data-downloads/)
#### Useful resources, articles, tutorials:
* [Gradient boosting - Wikipedia](https://en.wikipedia.org/wiki/Gradient_boosting)
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* [XGBoost - why we should using it?](https://towardsdatascience.com/https-medium-com-vishalmorde-xgboost-algorithm-long-she-may-rein-edd9f99be63d)
* [Comparing Classical and ML Algorithms for Time Series Forecasting](https://machinelearningmastery.com/findings-comparing-classical-and-machine-learning-methods-for-time-series-forecasting/)
* [Short overview about using Decision Trees, Random Forest and Gradient Boosting for Time Series Prediction](https://medium.com/@jakhotiaprerana21/using-decision-trees-random-forest-and-gradient-boosting-for-time-series-prediction-6d6064e3f270)
* [How (not) to use Machine Learning for time series forecasting](https://towardsdatascience.com/how-not-to-use-machine-learning-for-time-series-forecasting-avoiding-the-pitfalls-19f9d7adf424)
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* [Hourly Time Series Forecasting using XGBoost - Kaggle](https://www.kaggle.com/robikscube/tutorial-time-series-forecasting-with-xgboost)
* [Forecasting Markets using eXtreme Gradient Boosting (XGBoost)](https://blog.quantinsti.com/forecasting-markets-using-extreme-gradient-boosting-xgboost/)
* [Predicting the number of London Fire Brigade Call outs using seasonal patterns](https://www.jpytr.com/post/time-series-with-gradient-boosted-models/)
#### Future improvements
* Check efectivness of ARIMA models [Kaggle notebook](https://www.kaggle.com/praneethji/bitcoin-price-prediction-with-arima)