Measurement variance control for optimal forecasting
- 1 January 1989
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
This paper addresses the question of optimal allocation of measurement resources, when [1] the overall cost and time duration of the measurement process are fixed, and [2] the cost of an individual measurement varies inversely with the measurement variance. The goal is to determine the time-distribution of the measurement variance which maximizes the accuracy of forecasting a stochastic process. The solution is found to depend upon the autocorrelation properties,of the underlying stochastic process (assumed known) and upon the forecast horizon. An explicit solution is presented for an integrated Wiener process. In this case, optimal prediction accuracy is achieved with two measurements of unequal variance. The time separation of the two measurements depends upon the process noise, the prediction time, and the total measurement cost.Keywords
This publication has 3 references indexed in Scilit:
- Estimating random integrals from noisy observations: sampling designs and their performanceIEEE Transactions on Information Theory, 1988
- Optimal nonuniform sampling interval and test-input design for identification of physiological systems from very limited dataIEEE Transactions on Automatic Control, 1979
- Optimal input signals for parameter estimation in dynamic systems--Survey and new resultsIEEE Transactions on Automatic Control, 1974