How would you apply transfer learning to time series forecasting?
Instruction: Explain the principle of transfer learning and discuss how it can be leveraged in the context of time series forecasting.
Updated
Example Answer
I would transfer knowledge from related series when the target has limited history, then test whether that knowledge is useful. For a new product, a model trained on established products may offer a starting representation, but their demand patterns and launch conditions can differ.
I would align the units, sampling frequency, and forecast horizon, and check that source training does not include information from after the target forecast cutoff. I could compare a frozen model, limited fine-tuning, and training from scratch against a simple baseline. I would also inspect important target segments and cases where transfer makes errors worse. Negative transfer is possible, so lower training cost or a strong source model is not enough evidence to keep the approach.
Make it your own
Describe the source and target, what is transferred, and the baseline needed to demonstrate a benefit.
Why this works
It treats transfer as an empirical hypothesis rather than a promised improvement.
Follow-up to practice
What would make you reject the source model?