Abstract: Long-term time series forecasting (LTF) aims to predict time series over extended time horizons, offering significant cross-domain advantages and competitive insights. However, there is no ...

The project uses a forecast horizon of 96 hours (4 days) and evaluates models using rolling-origin cross-validation with proper temporal data splitting to avoid data leakage. The validation and test ...

Time series forecasting has attracted significant attention in the field of AI. Previous works have revealed that the Channel-Independent (CI) strategy improves forecasting performance by modeling ...

Abstract: Time series forecasting is widely used in finance, meteorology, and industrial systems. Although existing methods have made progress in modeling trends and periodicity, they still face ...

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This project provides a modern, well-structured implementation of hierarchical time series forecasting methods. It supports various forecasting algorithms (ARIMA, Prophet, LSTM) and reconciliation ...