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 ...
Discover how Fourier Analysis breaks down complex time series data into simpler components to identify trends and patterns, despite its limitations in stock forecasting.
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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