Machine learningTime-series forecasting
FiLM: 频率改进的勒让德记忆模型
FiLM 是由 Tian Zhou 及其同事在 NeurIPS 2022 上提出的一种长期时间序列预测架构。它将历史输入的勒让德多项式投影与应用于由此产生的系数序列的可学习频率域滤波器相结合。通过将历史表示为紧凑的多项式系数集并在频率域中过滤这些系数，FiLM 能够实现高效的长期预测外推，而无需全自注意力机制的二次成本。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- 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. link ↗
如何引用本页
ScholarGate. (2026, June 2). FiLM (Frequency Improved Legendre Memory Model). ScholarGate. https://scholargate.app/zh/deep-learning/film
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Autoformer：用于长期时间序列预测的分解Transformer深度学习↔ compare
- FEDformer：频率增强分解Transformer深度学习↔ compare
- 状态空间模型（卡尔曼滤波器）计量经济学↔ compare