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Computationally Efficient Forecasting Methods for Large-Scale Real-Time Applications

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thesis
posted on 15.11.2019, 07:00 by THIYANGA SHAMINI TALAGALA
Forecasting is a key activity for any business to operate efficiently. It is becoming increasingly common to have to regularly forecast many millions of time series. The dramatic increase in the availability of large collections of time series raises the need for developing reliable efficient and automatic algorithms for forecasting. This thesis presents three such algorithms for large-scale applications based on the meta-learning approach. Each of these algorithms uses vectors of features computed from time series to guide the way the forecasts are computed. The results show the proposed algorithms perform well compared to competitive methods in large forecasting competitions.

History

Campus location

Australia

Principal supervisor

Rob Hyndman

Additional supervisor 1

George Athanasopoulos

Year of Award

2019

Department, School or Centre

Econometrics and Business Statistics

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Exports