A Time-Series Trend Forecast Method Based on Principal Component Analysis

Shu Di Wei; Hui Huang Zhao

doi:10.4028/www.scientific.net/AMR.472-475.2984

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 472-475A Time-Series Trend Forecast Method Based on...

A Time-Series Trend Forecast Method Based on Principal Component Analysis

Abstract:

The time-series is the collection of chronological varying numerical ordered by time. It has a wide existence of image data, text data, hand-written data and the brain scan data patterns. The present research of time-series concentrates on series data transformation, similarity search, forecast, classification, clustering and Visualization etc. Furthermore the trend forecast of time-series is the major basis of other related research. This paper analyses the existing time-series forecasting methods and puts forward a new time-series method based on principal component analysis. The example tests the validity of the method of other related research.

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Advanced Manufacturing Technology, ICMSE 2012

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Periodical:

Advanced Materials Research (Volumes 472-475)

Pages:

2984-2987

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.472-475.2984

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Online since:

February 2012

Authors:

Shu Di Wei, Hui Huang Zhao

Keywords:

Covariance Matrix, Principal Component Analysis (PCA), Time Series, Trend Forecast

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