Classification and Forecasting for Enterprise Data
- Resource Type
- Conference
- Authors
- Bai, Xiaogang; Kong, Fansen; Zhao, Hang
- Source
- 2018 Chinese Automation Congress (CAC) Automation Congress (CAC), 2018 Chinese. :3742-3745 Nov, 2018
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Time series analysis
Support vector machines
Correlation
Forecasting
Predictive models
Time measurement
Indexes
classification
support vector machines
forecasting
- Language
There is an increasing interest in classification and forecasting for enterprise data. Besides many similarity or dissimilarity measures, Support Vector Machines have also been used for time series classification. We classify the industry data with autocorrelation function-distance, an automatic adaptive dissimilarity index and Support Vector Machines respectively. Then we make forecasting for different class. Every class is fitted with particular season model according to Akaike Information Criteria. The orders of the particular model are estimated. A comparative study is presented. It is proposed that an automatic adaptive dissimilarity index outperforms autocorrelation function distance and Support Vector Machines.