Abstract: In complex industrial production processes, accurate multivariate time series forecasting is essential for operational control, process optimization, and safety assurance. However, the ...

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 ...

In this tutorial, we build an advanced agentic AI system that autonomously handles time series forecasting using the Darts library combined with a lightweight HuggingFace model for reasoning. We ...