Overlapping trend detection and application in prediction
- Resource Type
- Conference
- Authors
- Gao, Xuedong; Gu, Kan; Wang, Danyue
- Source
- 2016 International Conference on Logistics, Informatics and Service Sciences (LISS) Logistics, Informatics and Service Sciences (LISS), 2016 International Conference on. :1-6 Jul, 2016
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Transportation
Market research
Time series analysis
Fitting
Transforms
Autoregressive processes
Signal processing algorithms
Prediction algorithms
Overlapping trend
Sliding window
Prediction
- Language
Traditional time series studies pay more attention to the representation effect of original sequence rather than to the trend contained in it. And overlapping trends are ignored because of the usual way for extracting trend from time series is decomposition or segmentation. A sufficient long sequence is also required when doing prediction with trend analysis, or only disappointing results would be gotten. This paper introduces sliding window to improve the inertia test based trend detection algorithm. The data of experiments show that the improved algorithm gets the target of overlapping trend detection and short sequence prediction.