Change point-driven interrupted time series and machine learning models for forecasting Indian food grain production
This study develops interrupted models to forecast Indian food grain production under the influence of an intervention event. A Pettitt test identified 1987 as a significant change point (p < 0.0001), indicating a structural shift in the agricultural production system. The study employs interrupted...
| Main Authors: | , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Springer
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/174392 |
Similar Items: Change point-driven interrupted time series and machine learning models for forecasting Indian food grain production
- Advancing multivariate time series similarity assessment: an integrated computational approach
- Drought forecasting using artificial neural networks and time series of drought indices
- Multivariate time series modeling of short-term system scale irrigation demand
- Surveillance tool for FBD: Time Series Analysis
- Time Series Application for Evapotranspiration and NDVI in Ucayali, Peru and Acre, Brazil
- An AI-driven River Salinity Forecast and Advisory System for Climate Resilient Agriculture in Coastal Bangladesh