An unobserved component model for multi-rate forecasting of telephone call demand: the design of a forecasting support system

An Unobserved Components (UC) Model based on an enhanced version of the Dynamic Harmonic Regression model, including new multi-rate and modulated cycle procedures, is used to develop a customised package for forecasting and signal extraction applied to hourly telephone call numbers made to Barclaycard plc. service centres, with a forecasting horizon of up to several weeks in advance. The paper outlines both the methodological and algorithmic aspects of the modelling, forecasting and signal extraction procedures, including the design and implementation of forecasting support software with a specially designed Graphical User Interface within the Matlab® computing environment. The forecasting performance is evaluated comprehensively in comparison with the well-known seasonal ARIMA approach.

[1]  Peter C. Young,et al.  Recursive estimation and forecasting of non‐stationary time series , 1990 .

[2]  J. Ord,et al.  Estimation and Prediction for a Class of Dynamic Nonlinear Statistical Models , 1997 .

[3]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[4]  Peter R. Winters,et al.  Forecasting Sales by Exponentially Weighted Moving Averages , 1960 .

[5]  E. J. Hannan,et al.  The Estimation of a Changing Seasonal Pattern , 1964 .

[6]  Granville Tunnicliffe Wilson,et al.  Selection and estimation of component models for seasonal time series. , 2000 .

[7]  Peter C. Young,et al.  Identification and system parameter estimation , 1985 .

[8]  J. P. Norton,et al.  An Introduction to Identification , 1986 .

[9]  Erik Johnsen,et al.  C. West Churchman: Prediction and Optimal Decision. Prentice-Hall, Englewood Cliffs, N. J., 1961. 394 s. , 1963 .

[10]  P. Young,et al.  Dynamic harmonic regression. , 1999 .

[11]  J. Scott Armstrong.  Commentary on 'Generalizing About Univariate Forecasting Methods: Further Empirical Evidence' , 1998 .

[12]  Peter C. Young,et al.  Recursive Estimation and Time Series Analysis , 1984 .

[13]  Fred C. Schweppe,et al.  Evaluation of likelihood functions for Gaussian signals , 1965, IEEE Trans. Inf. Theory.

[14]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[15]  Jeff Harrison,et al.  Applied Bayesian Forecasting and Time Series Analysis , 1994 .

[16]  Robert Fildes,et al.  Generalising about univariate forecasting methods: Further empirical evidence , 1998 .

[17]  E. S. Gardner,et al.  Seasonal exponential smoothing with damped trends , 1989 .

[18]  Andrew Harvey,et al.  Forecasting, Structural Time Series Models and the Kalman Filter , 1990 .

[19]  Peter C. Young,et al.  Recursive and en-bloc approaches to signal extraction , 1999 .

[20]  Diego J. Pedregal,et al.  Recursive Fixed Interval Smoothing and the Evaluation Of Lidar Measurements: A Comment On The Paper By Holst, Hössjer, Björklund, Ragnarsson And Edner. , 1996 .

[21]  M. West,et al.  Bayesian forecasting and dynamic models , 1989 .

[22]  Peter C. Young,et al.  Recursive Extrapolation, Interpolation and Smoothing of Nonstationary Time-series , 1988 .

[23]  Peter Bloomfield,et al.  Fourier analysis of time series , 1976 .

[24]  Siem Jan Koopman,et al.  Stamp 5.0 : structural time series analyser, modeller and predictor , 1996 .