https://github.com/jzaragosa06/univariate_ts_forecast
Using Large Language Model (Gemini-Pro) to Provide Context-Specific Explanation to Time Series Forecast
https://github.com/jzaragosa06/univariate_ts_forecast
flask timeseries-forecasting xgboost
Last synced: over 1 year ago
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Using Large Language Model (Gemini-Pro) to Provide Context-Specific Explanation to Time Series Forecast
- Host: GitHub
- URL: https://github.com/jzaragosa06/univariate_ts_forecast
- Owner: jzaragosa06
- License: mit
- Created: 2024-08-19T12:36:42.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-09-28T06:06:23.000Z (almost 2 years ago)
- Last Synced: 2025-01-15T00:37:44.300Z (over 1 year ago)
- Topics: flask, timeseries-forecasting, xgboost
- Language: HTML
- Homepage:
- Size: 21.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Automated Univariate Time Series Forecasting
This project focuses on integrating a large language model (LLM) to offer context-specific explanations for forecasts generated by XGBRegressor. Users input context-specific data, enabling the LLM to provide understandable detailed explanations.