Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time-Series Forecast. (arXiv:2204.13908v1 [cs.LG])

Task embeddings in multi-layer perceptrons for multi-task learning and
inductive transfer learning in renewable power forecasts have recently been
introduced. In many cases, this approach improves the forecast error and
reduces the required training data. However, it does not take the seasonal
influences in power forecasts within a day into account, i.e., the diurnal
cycle. Therefore, we extended this idea to temporal convolutional networks to
consider those seasonalities. We propose transforming the embedding space,
which contains the latent similarities between …

arxiv convolution embedding forecast learning networks power series temporal time transfer transfer learning