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FiLM

FiLM is a long-term time-series forecasting architecture introduced by Tian Zhou and colleagues at NeurIPS 2022. It combines Legendre polynomial projections of the historical input with learnable frequency-domain filters applied to the resulting coefficient sequences. By representing history as a compact set of polynomial coefficients and filtering those coefficients in the frequency domain, FiLM enables efficient extrapolation over long prediction horizons without the quadratic cost of full self-attention.

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FiLM (Frequency Improved Legendre Memory Model)

Rekodi ya mbinu ya kiajenda · ml-model / deep-learning

Zhou, T., Ma, Z., Wen, Q., Sun, L., Yao, T., Yin, W., & Jin, R. (2022). FiLM: Frequency improved Legendre memory model for long-term time series forecasting. NeurIPS. · URL

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Taxonomic bucketAutoformermachine-suggested · Relational suggestion, not evidence.Taxonomic bucketFEDformermachine-suggested · Relational suggestion, not evidence.See alsoState Space Modelmachine-suggested · Relational suggestion, not evidence.