The impact of data set similarity and diversity on transfer learning
  success in time series forecasting
Claudia Ehrig Catherine Cleophas Germain Forestier
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 of
source-target similarity and source diversity on zero-shot and fine-tuned
forecasting outcomes in terms of accuracy, bias, and uncertainty estimation. We
investigate these dynamics using pre-trained neural networks across five public
source datasets, applied in forecasting five target data sets, including
real-world wholesales data. We identify two feature-based similarity and
diversity measures showing: Source-target similarity enhances forecasting
accuracy and reduces bias, while source diversity enhances forecasting accuracy
and uncertainty estimation and increases the bias.