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Welcome to the FECAM wiki!Our work is featured by:

• Support Six popular time-series forecasting datasets, namely Electricity Transformer Temperature (ETTh1, ETTh2 and ETTm1,ETTm2) , Traffic, National Illness, Electricity and Exchange Rate , ranging from power, energy, finance,illness and traffic domains.
• We generalize FECAM into a module which can be flexibly and easily applied into any deep learning models within just few code lines.
• Provide all training logs.

To-do items

• Integrate FECAM into other mainstream models(eg:Pyraformer,Bi-lstm,etc.) for better performance and higher efficiency on real-world time series.
• Validate FECAM on more spatial-temporal time series datasets.
• As a sequence modelling module,we believe it can work fine on NLP tasks too,like Machine Translation and Name Entity Recognization.Further more,as a frequency enhanced module it can theoretically work in any deep-learning models like Resnet.

Stay tuned!