Kalman Filtering Applied to Statistical Forecasting

This paper describes the use of the Kalman Filter in a certain ciass of forecasting problems. The time series is assumed to be modeled as a time varying mean with additive noise. The mean of the time series is assumed to be a linear combination of known functions. The coefficients appearing in the linear combination are unknown. Under such assumptions, the time series can be described as a linear system with the state vector of the system being the unknown parameters and present value of the mean of the process. The Kalman Filter can be used under these circumstances to obtain an “optimal” estimate of the state vector. One of the distinct advantages of the Kalman Filter is that time varying coefficients can be permitted in the model. Examples using the Kalman Filter in forecasting are presented.

• 저널Management Science · 23(7) · 768–774
• 토픽Target Tracking and Data Fusion in Sensor Networks · Artificial Intelligence
• DOI10.1287/mnsc.23.7.768
• 저자G.W. Morrison, D.H. Pike