A General Framework for Enhancing Prediction Performance on Time Series Data
碩士 === 國立臺灣大學 === 資訊工程學研究所 === 101 === Traditionally, researchers apply the latest data to predict the near future of Time Series Data prediction. However, we proposed a novel framework to use not only latest data but also potential accurate predicted results. And it also be able to predict much fur...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Others |
| Language: | zh-TW |
| Published: |
2013
|
| Online Access: | http://ndltd.ncl.edu.tw/handle/00616418460500687137 |
| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 101 === Traditionally, researchers apply the latest data to predict the near future of Time Series Data prediction. However, we proposed a novel framework to use not only latest data but also potential accurate predicted results. And it also be able to predict much further results for enhancing the prediction. The framework adopts generic predict methods and extract specific features ac- cording to the data property. Three type of feature sets are designed to capture the Statistic, Reliability and Periodicity of the Time Series Data. Short-Term and Long-Term Prediction Enhancement algorithms are also introduced to im- prove the prediction performance. The experiments show that Short-Term En- hancement increases the accuracy of +20.04% and Long-Term Enhancement +9.59% compared to well-known baseline approaches, ARIMA and HW-ES.
|
|---|