In this thesis we discuss machine learning methods performing automated variable selection for learning sparse predictive models.
Continual learning is the ability to sequentially learn over time by accommodating knowledge while retaining previously learned experiences.
We present a new method for forecasting systems of multiple interrelated time series.
Lifelong learning is the problem of learning multiple consecutive tasks in a sequential manner, where knowledge gained from previous tasks is retained and used to aid future learning over the lifetime of the learner.
We consider the problem of learning models for forecasting multiple time-series systems together with discovering the leading indicators that serve as good predictors for the system.