FreqLite: A Lightweight Frequency-Decomposed Linear Model with Adaptive Reversible Normalization for Robust Long-Term Time-Series Forecasting
arXiv:2606.01339v1 Announce Type: cross Abstract: Long-term time-series forecasting needs models that are accurate yet efficient enough for commodity hardware. Lightweight linear forecasters are remarkably strong in this regime, yet they leave two openings: reversible instance normalization (RevIN) de-normalizes the entire horizon with a single lookback statistic, which is inaccurate under non-stationarity, and time-domain trend/seasonal decomposition relies on a fixed, non-adaptive filter. We present FreqLite, an ultra-lightweight, channel-independent frequency-decomposed linear forecaster: a
The continuous growth in demand for efficient, accurate long-term time-series forecasting, especially on commodity hardware, drives ongoing research into more lightweight and robust models.
Improved long-term time-series forecasting with lightweight models can significantly enhance operational efficiency, resource allocation, and predictive capabilities across various industries without requiring extensive computational resources.
The introduction of FreqLite, a more accurate and efficient linear forecaster with adaptive normalization and frequency decomposition, offers a practical advancement over existing methods for long-term forecasting.
- · Industries reliant on forecasting (e.g., energy, logistics, finance)
- · Edge computing platforms
- · AI/ML researchers in time-series analysis
- · Compute-intensive forecasting solutions
- · Legacy time-series analysis methods
Wider adoption of advanced forecasting techniques due to lower computational overhead and improved accuracy.
Enhanced planning and optimization across global supply chains and energy grids, reducing waste and increasing resilience.
Potential for new applications in highly distributed or resource-constrained environments that previously lacked robust forecasting capabilities.
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