An Application of Deterministic Nonlinear Short-term Prediction to Timeseries Data of Water Demand

Tadashi Iokibe; Takasi Kimura; Kazuyuki Aihara

doi:10.1541/ieejias.114.409

Tadashi Iokibe, Takasi Kimura, Kazuyuki Aihara

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1994 Volume 114 Issue 4 Pages 409-414

DOI https://doi.org/10.1541/ieejias.114.409

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Published: April 20, 1994 Received: April 23, 1993 Available on J-STAGE: December 19, 2008 Accepted: - Advance online publication: - Revised: -
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Date of correction: December 19, 2008 Reason for correction: - Correction: ABSTRACT Details: Wrong : One of the possible applications of the chaos theory for engineering is nonlinear short-term prediction. Hetherto, many prediction methods have been proposed. But most of them are based upon linear theories. Therefore, it is difficult to obtain high performance when timeseries is produced by nonlinear dynamics. In this paper, we apply deterministic nonlinear short-term prediction to timeseries data of water demand. As a result, this paper shows timeseries data of water demand have structure of a possible attractor, and prediction accuracy by deterministic nonlinear shortterm prediction is higher than that by a typical conventional prediction method with an autoregression method combined with Kalman filter.
Right : One of the possible applications of the chaos theory for engineering is nonlinear short-term prediction. Hetherto, many prediction methods have been proposed. But most of them are based upon linear theories. Therefore, it is difficult to obtain high performance when timeseries is produced by nonlinear dynamics. In this paper, we apply deterministic nonlinear short-term prediction to timeseries data of water demand. As a result, this paper shows timeseries data of water demand have structure of a possible attractor, and prediction accuracy by deterministic nonlinear short-term prediction is higher than that by a typical conventional prediction method with an autoregression method combined with Kalman filter.

Date of correction: December 19, 2008 Reason for correction: - Correction: CITATION Details: [in Japanese]

Date of correction: December 19, 2008 Reason for correction: - Correction: PDF FILE Details: -

Abstract

One of the possible applications of the chaos theory for engineering is nonlinear short-term prediction. Hetherto, many prediction methods have been proposed. But most of them are based upon linear theories. Therefore, it is difficult to obtain high performance when timeseries is produced by nonlinear dynamics. In this paper, we apply deterministic nonlinear short-term prediction to timeseries data of water demand. As a result, this paper shows timeseries data of water demand have structure of a possible attractor, and prediction accuracy by deterministic nonlinear short-term prediction is higher than that by a typical conventional prediction method with an autoregression method combined with Kalman filter.

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