{"id":16830033,"url":"https://github.com/mwoss/timeseries-decision-trees","last_synced_at":"2025-03-17T22:08:04.496Z","repository":{"id":69423679,"uuid":"216915826","full_name":"mwoss/timeseries-decision-trees","owner":"mwoss","description":"The use of Gradient Boosted Decision Trees algorithms to predict timeseries data","archived":false,"fork":false,"pushed_at":"2019-12-10T21:28:03.000Z","size":5522,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-01-24T08:23:06.027Z","etag":null,"topics":["decision-trees","ets","timeseries","xgboost"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mwoss.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-10-22T21:49:03.000Z","updated_at":"2024-08-09T11:35:47.000Z","dependencies_parsed_at":null,"dependency_job_id":"d2f284b1-070d-4647-9b40-bb9b16bfbe8f","html_url":"https://github.com/mwoss/timeseries-decision-trees","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mwoss%2Ftimeseries-decision-trees","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mwoss%2Ftimeseries-decision-trees/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mwoss%2Ftimeseries-decision-trees/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mwoss%2Ftimeseries-decision-trees/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mwoss","download_url":"https://codeload.github.com/mwoss/timeseries-decision-trees/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244117652,"owners_count":20400743,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["decision-trees","ets","timeseries","xgboost"],"created_at":"2024-10-13T11:37:08.630Z","updated_at":"2025-03-17T22:08:04.441Z","avatar_url":"https://github.com/mwoss.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Forcasting time series data using Gradient Boosted Decision Trees and ETS models\nGradient Boosted Decision Trees algorithm for time series forcasting using XGBoost library\n## Datasets\n#### List of all dataset used for learning/research process:\n* [Coinbase - Bitcoin historical data | kaggle.com](https://www.kaggle.com/mczielinski/bitcoin-historical-data)\n* [European markets data (hourly/daily) | cryptodatadownload.com](http://www.cryptodatadownload.com/data/euro/)\n* [CoinMetrics cryptocurrency data | coinmetrics.io](https://coinmetrics.io/data-downloads/)\n\n#### Useful resources, articles, tutorials:\n* [Gradient boosting - Wikipedia](https://en.wikipedia.org/wiki/Gradient_boosting)\n---\n* [XGBoost - why we should using it?](https://towardsdatascience.com/https-medium-com-vishalmorde-xgboost-algorithm-long-she-may-rein-edd9f99be63d)\n* [Comparing Classical and ML Algorithms for Time Series Forecasting](https://machinelearningmastery.com/findings-comparing-classical-and-machine-learning-methods-for-time-series-forecasting/)\n* [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)\n* [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)\n---\n* [Hourly Time Series Forecasting using XGBoost - Kaggle](https://www.kaggle.com/robikscube/tutorial-time-series-forecasting-with-xgboost)\n* [Forecasting Markets using eXtreme Gradient Boosting (XGBoost)](https://blog.quantinsti.com/forecasting-markets-using-extreme-gradient-boosting-xgboost/)\n* [Predicting the number of London Fire Brigade Call outs using seasonal patterns](https://www.jpytr.com/post/time-series-with-gradient-boosted-models/)\n\n\n#### Future improvements\n* Check efectivness of ARIMA models [Kaggle notebook](https://www.kaggle.com/praneethji/bitcoin-price-prediction-with-arima)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmwoss%2Ftimeseries-decision-trees","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmwoss%2Ftimeseries-decision-trees","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmwoss%2Ftimeseries-decision-trees/lists"}