Research Summaries

Back Time Series Forecasting for Big Data

Fiscal Year 2017
Division Graduate School of Engineering & Applied Science
Department Mechanical & Aerospace Engineering
Investigator(s) Proulx, Ronald J.
Ross, Isaac M.
Sponsor Department of Defense Space (DoD)
Summary There are various methods to analyze data. ARIMA is represented by three parameters: degree of autoregressive, degree of integration and degree of moving average. This method is popular due to its statistical properties and the availability of classical models (e.g. Box-Jenkins). The assumption of linearity imposes a major limitation on their use. In recent years other models have been developed such as the use of ANNs and hybrid techniques. In certain situations, such models have shown improvements in prediction over ARIMA models. The objectives of this proposal are to analyze a given "big data set" and develop models to describe relationships and behavior between various data sets.
Keywords
Publications Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal
Data Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal