Persistence Initialization: A novel adaptation of the Transformer architecture for Time Series Forecasting. (arXiv:2208.14236v1 [cs.LG])

Time series forecasting is an important problem, with many real world
applications. Ensembles of deep neural networks have recently achieved
impressive forecasting accuracy, but such large ensembles are impractical in
many real world settings. Transformer models been successfully applied to a
diverse set of challenging problems. We propose a novel adaptation of the
original Transformer architecture focusing on the task of time series
forecasting, called Persistence Initialization. The model is initialized as a
naive persistence model by using a multiplicative …

architecture arxiv forecasting series time time series time series forecasting transformer transformer architecture