Abstract
Prediction method for time series of imagery data on eigen space is proposed. Although the conventional prediction method is defined on the real world space and time domains, the proposed method is defined on eigen space. Prediction accuracy of the proposed method is supposed to be superior to the conventional methods. Through experiments with time series of satellite imagery data, validity of the proposed method is confirmed.
Highlights
There are conventional prediction methods which allow prediction of future imagery data by using the acquired imagery data in the past [1]-[6]
The conventional prediction method is defined on real world space and time domains, in particular, is defined as an auto regressive model
The vectors are projected in the eigen space, time series of imagery data can be represented as the vector in the eigen space
Summary
There are conventional prediction methods which allow prediction of future imagery data by using the acquired imagery data in the past [1]-[6]. The conventional prediction method is defined on real world space and time domains, in particular, is defined as an auto regressive model. Time series of data can be projected onto eigen space from the real world space and time domains. Time series of data behavior can be well described on the eigen space rather than the real world space and time domains in particular for the irregularly changed data. Through experiments with GMS/VISSR images which are acquired every one hour, comparative study on prediction accuracy between the proposed and the conventional methods is conducted. The experimental results show advantage of the proposed method in terms of prediction accuracy. The following section describes the proposed prediction method for time series analysis followed by experimental results.
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