Abstract: This paper introduces SparseTSF, a novel and extremely lightweight method for Long-term Time Series Forecasting (LTSF), designed to address the challenges of modeling complex temporal ...
Abstract: Transformer-based methods have demonstrated promising performance in long-term time series forecasting. However, existing methods often consider interactions among all variables when ...
A) Retail/E-commerce inventory (forecasting product demand for stores or online sales) B) Manufacturing raw materials (forecasting material needs for production) C) Distribution/logistics (forecasting ...
Time Series Forecasting Engine is a comprehensive, production-ready Python framework for advanced time series forecasting. It combines statistical models (ARIMA), machine learning approaches (Prophet) ...
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