I am faced with a time series forecasting cold-start problem, specifically I am forecasting energy consumption of businesses where historic consumption data is available only for training but not new businesses. What I have for new (and existing) businesses is metadata such as business type, turnover etc.
I am trying to approach this problem as a multi-outcome regression where the predictors is represented by the metadata, and the outcomes are each time point in the consumption time-series. Thus, the outcomes are correlated. Some articles mentioned Predictive Clustering Trees (see this and this articles for example) as a possible solution to this problem.
Do you know if there is an implementation in Python or any similar algorithm? Alternatively, do you know of any other algorithm already implemented in Python that could be used for cold-start forecasting/multi-output regression?