Awesome
FiLM
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting https://arxiv.org/abs/2205.08897
In long-term forecasting, FiLM achieves SOTA, with a 19% relative improvement on six benchmarks, covering five practical applications: energy, traffic, economics, weather and disease.
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| Figure 1. Overall structure of FiLM |
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| Figure 2. Frequency Enhanced Layer (FEL) | Figure 3. Legendre Projection Unit (LPU) |
Main Results
Get Started
- Install Python 3.9, PyTorch 1.11.0.
- Download data. You can obtain all the six benchmarks from Tsinghua Cloud or Google Drive. All the datasets are well pre-processed and can be used easily.
- Train the model. We provide the experiment scripts of all benchmarks under the folder
./scripts. You can reproduce the Multivariate/Univariate experiment results by:
bash ./script/ETT_script/FiLM/FiLM_ETTm2.sh
bash ./script/ECL_script/FiLM/FiLM.sh
bash ./script/Exchange_script/FiLM/FiLM.sh
bash ./script/Traffic_script/FiLM/FiLM.sh
bash ./script/Weather_script/FiLM/FiLM.sh
bash ./script/ILI_script/FiLM/FiLM.sh
bash ./script/ETT_script/FiLM/FiLM_ETTm2_S.sh
bash ./script/ECL_script/FiLM/FiLM_S.sh
bash ./script/Exchange_script/FiLM/FiLM_S.sh
bash ./script/Traffic_script/FiLM/FiLM_S.sh
bash ./script/Weather_script/FiLM/FiLM_S.sh
bash ./script/ILI_script/FiLM/FiLM_S.sh
Acknowledgement
We appreciate the following github repos a lot for their valuable code base or datasets:
https://github.com/zhouhaoyi/Informer2020
https://github.com/zhouhaoyi/ETDataset