Stellenbosch University
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Seminar: Department of Logistics
Start: 22/02/2019, 13:00
End: 22/02/2019, 14:00
Contact:Linke Potgieter -
Location: VDS 3022

Presenter: Noé Fouotsa Manfouo (PhD candidate in Operations Research and Machine Learning Fellow @ Fellowship AI)

Title of talk: Time series forecasting and machine learning

Summary: The increasing availability of large amounts of historical data and the need of performing accurate forecasting of future behaviour in several scientific and applied domains demands the definition of robust and efficient techniques able to infer from observations the stochastic dependency between past and future. The forecasting domain has been influenced, from the 1960s onward, by linear statistical methods generally denoted as Box-Jenkins or autoregressive methods. These methods have the main drawbacks of being limited to structured data inferences, and not being able to predict extreme events. More recently, machine learning models have drawn attention and have established themselves as serious contenders to classical statistical models in the forecasting community. This talk presents an overview of machine learning techniques in time series forecasting by focusing on three aspects: the formalization of one-step forecasting problems as supervised learning tasks, the discussion of local learning techniques as an effective tool for dealing with temporal data and the role of the forecasting strategy when we move from one-step to multiple-step forecasting. Snippets of python codes are also discussed for practical implementation.

Keywords: Time series forecasting, machine learning, local learning, lazy learning, MIMO, python