  In this paper, we propose an innovative Transfer learning for Time series
classification method. Instead of using an existing dataset from the UCR
archive as the source dataset, we generated a 15,000,000 synthetic univariate
time series dataset that was created using our unique synthetic time series
generator algorithm which can generate data with diverse patterns and angles
and different sequence lengths. Furthermore, instead of using classification
tasks provided by the UCR archive as the source task as previous studies did,we
used our own 55 regression tasks as the source tasks, which produced better
results than selecting classification tasks from the UCR archive
