The impact of data set similarity and diversity on transfer learning success in time series forecasting

arXiv:2404.06198v1 Announce Type: new
Abstract: Models, pre-trained on a similar or diverse source data set, have become pivotal in enhancing the efficiency and accuracy of time series forecasting on target data sets by leveraging transfer learning. While benchmarks validate the performance of model generalization on various target data sets, there is no structured research providing similarity and diversity measures explaining which characteristics of source and target data lead to transfer learning success. Our study pioneers in systematically evaluating the impact …

abstract accuracy arxiv become benchmarks cs.lg data data set data sets diverse diversity efficiency forecasting impact model generalization performance pivotal series set source data success time series time series forecasting transfer transfer learning type