A crucial, high impact application of learning systems is forecasting. While machine learning has already been applied to time series analysis and signal processing, the recent big data revolution allows processing and prediction of vast data flows and forecasting of high dimensional, spatiotemporal series using massive multi-modal streams as predictors. Wider data bandwidths allow machine learning techniques such as connectionist and deep learning methods to assist traditional forecasting methods from fields such as engineering and econometrics, while probabilistic methods are uniquely suited to address the stochastic nature of many processes requiring forecasting.

This workshop will bring together multi-disciplinary researchers from signal processing, statistics, machine learning, computer vision, economics and causality looking to widen their application or methodological scope. It will begin by providing a forum to discuss pressing application areas o forecasting: video compression and understanding, energy and and smart grid management, economics and finance, environmental and health policy (e.g. epidemiology), as well as introduce challenging new datasets. A large dataset, created for an industry-driven data competition, will be presented - this dataset not only helps develop and compare new methods for forecasting, but also addresses deeper underlying learning theory questions: do effective learning systems truly infer underlying structure or merely output accuracy in data streams?, is transfer learning available at no loss to specificity? and is semi-supervised learning is an inherent property of powerful, accurate, learning machines? What strategies are scalable so they perform well on sparse as well as big data? What exactly is a good forecasting machine? Therefore a forum is also planned to discuss such pressing issues,- dedicated poster sessions and panels are scheduled. We plan for a varied list of reknowned speakers, presenting data sources, rich open-source platforms for forecasting, prediction performance evaluation metrics, past forecasting competitions and state-of-the-art methods.