Summaries - Research
Back Time Series Forecasting for Big Data
|Division||Graduate School of Engineering & Applied Science|
|Department||Mechanical & Aerospace Engineering|
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||Big Data Time Series Forecasting|
|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|