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.

Figure 1. Overall structure of FiLM

Figure 2. Frequency Enhanced Layer (FEL)	Figure 3. Legendre Projection Unit (LPU)

Main Results

Get Started

1. Install Python 3.9, PyTorch 1.11.0.
2. 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.
3. 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

https://github.com/laiguokun/multivariate-time-series-data

https://github.com/thuml/Autoformer