Summary
For cyclical processes, for example economic cycles or biological cycles, it is often of interest to detect the turning points, by methods for on-line detection. This is the case in prediction of the turning point time in the business cycle, by detection of a turn in monthly or quarterly leading economic indicators. Another application is natural family planning, where we want to detect the peak in the human menstrual cycle in order to predict the days of the most fertile phase. We make continual observation of the process with the goal of detecting the turning point as soon as possible. At each time, an alarm statistic and an alarm limit are used to make a decision as to whether the time series has reached a turning point. Thus we have repeated decisions. An optimal alarm system is based on the likelihood ratio method. The full likelihood ratio method is optimal. Here we use a maximum likelihood ratio method, which does not require any parametric assumptions about the cycle. The alarm limit is set to control the false alarms.
The influence, on the maximum likelihood ratio method, of some turning point characteristics is evaluated (shape at the turn, symmetry and smoothness of curve). Results show that the smoothness has little effect, whereas a non-symmetric turn, where the post-turn slope is steeper, is easier to detect.
If a parametric method is used, then the alarm limit is set in accordance with the specified model and if the model is mis-specified then the false alarm property will be erroneous. By using the maximum likelihood ratio method, the false alarms are controlled at the nominal level.
Another characteristic is the intensity, i.e. the frequency of the turns. From historical data we construct an empirical density for the turning point times. However, using this information only benefits the surveillance system if the time to the turn we want to detect, agrees with the empirical density. If, on the other hand, the turn we want to detect occurs “earlier than expected”, then the time until detection is long. A method that does not use this prior information works well for all turning point times.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Andersson, E. (2002) Monitoring cyclical processes. A non-parametric approach. Journal of Applied Statistics, 29, 973–990.
Andersson, E. (2004) The impact of intensity in surveillance of cyclical processes. Communications in Statistics-Simulation and Computation, 33, 889–913.
Andersson, E., Bock, D. and Frisén, M. (2005) Statistical surveillance of cyclical processes with application to turns in business cycles. Accepted for publication in Journal of Forecasting.
Barlow, R. E., Bartholomew, D. J., Bremer, J. M. and Brunk, H. D. (1972) Statistical inference under order restrictions, Wiley, London.
Baron, M. (2002) Bayes and asymptotically pointwise optimal stopping rules for the detection of influenza epidemics. In Case Studies in Bayesian Statistics, Vol. 6 (Ed, Verdinelli, I.) Springer-Verlag, New York, pp. 153–163.
Bock, D. (2004) Aspects on the control of false alarms in statistical surveillance and the impact on the return of financial decision systems. Research report 2004:2, Department of Statistics, Göteborg University, Sweden.
Bock, D., Andersson, E. and Frisén, M. (2003) The relation between statistical surveillance and certain decision rules in finance. Research report 2003:4, Department of Statistics, Göteborg University, Sweden.
Chu, C. S. J., Stinchcombe, M. and White, H. (1996) Monitoring Structural Change. Econometrica, 64, 1045–1065.
Dewachter, H. (2001) Can Markov switching models replicate chartist profits in the foreign exchange market? Journal of International Money and Finance, 20, 25–41.
Frisén, M. (1986) Unimodal regression. The Statistician, 35, 479–485.
Frisén, M. (1992) Evaluations of Methods for Statistical Surveillance. Statistics in Medicine, 11, 1489–1502.
Frisén, M. (1994) Statistical Surveillance of Business Cycles. Research report 1994:1 (Revised 2000), Department of Statistics, Göteborg University, Sweden.
Frisén, M. (2003) Statistical surveillance. Optimality and methods. International Statistical Review, 71, 403–434.
Frisén, M. and de Maré, J. (1991) Optimal Surveillance. Biometrika, 78, 271–80.
Frisén, M. and Wessman, P. (1999) Evaluations of likelihood ratio methods for surveillance. Differences and robustness. Communications in Statistics. Simulations and Computations, 28, 597–622.
Gan, F. (1993) An optimal-design of EWMA control charts based on median run-length. Journal of Statistical Computation and Simulation, 45, 169–184.
Göb, R., Del Castillo, E. and Ratz, M. (2001) Run length comparisons of Shewhart charts and most powerful test charts for the detection of trends and shifts. Communications in Statistics-Simulation and Computation, 30, 355–377.
Hamilton, J. D. (1989) A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57, 357–384.
Hawkins, D. M. (1992) A fast accurate approximation for average run lengths of cusum control charts. Journal of Quality Technology, 24, 37–43.
Le Strat, Y. and Carrat, F. (1999) Monitoring epidemiologic surveillance data using hidden Markov models. Statistics in Medicine, 18, 3463–3478.
Marsh, I. W. (2000) High-frequency Markov Switching Models in the Foreign Exchange Market. Journal of Forecasting, 19, 123–134.
Morais, M. C. and Pacheco, A. (2000) On the performance of combined EWMA schemes for mu and sigma: A Markovian approach. Communications in Statistics. Simulations and Computations, 29, 153–174.
National Institute of Economic Research (1992) Konjunkturläget Maj 1992. National Institute of Economic Research, Stockholm, Sweden.
Neftci, S. (1982) Optimal prediction of cyclical downturns. Journal of Economic Dynamics and Control, 4, 225–41.
Page, E. S. (1954) Continuous inspection schemes. Biometrika, 41, 100–114.
Pollak, M. and Siegmund, D. (1975) Approximations to the Expected Sample Size of Certain Sequential Tests. Annals of Statistics, 3, 1267–1282.
Roberts, S. W. (1959) Control Chart Tests Based on Geometric Moving Averages. Technometrics, 1, 239–250.
Roberts, S. W. (1966) A Comparison of some Control Chart Procedures. Technometrics, 8, 411–430.
Robertson, T., Wright, F. T. and Dykstra, R. L. (1988) Order Restricted Statistical Inference, John Wiley & Sons Ltd.
Royston, P. (1991) Identifying the fertile phase of the human menstrual cycle. Statistics in Medicine, 10, 221–240.
Sepúlveda, A. and Nachlas, J. A. (1997) A simulation approach to multivariate quality control. Computers & Industrial Engineering, 33, 113–116.
Shewhart, W. A. (1931) Economic Control of Quality of Manufactured Product, MacMillan and Co., London.
Shiryaev, A. N. (1963) On optimum methods in quickest detection problems. Theory of Probability and its Applications., 8, 22–46.
Sonesson, C. and Bock, D. (2003) A review and discussion of prospective statistical surveillance in public health. Journal of the Royal Statistical Society A, 166, 5–21.
Woodall, W. H. and Ncube, M. M. (1985) Multivariate Cusum Quality Control Procedures. Technometrics, 27, 285–292.
Wu, C., Zhao, Y. and Wang, Z. (2002) The median absolute deviations and their applications to Shewhart control charts. Communications in Statistics-Simulation and Computation, 31, 425–443.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Physica-Verlag Heidelberg
About this chapter
Cite this chapter
Andersson, E. (2006). Robust On-Line Turning Point Detection. The Influence of Turning Point Characteristics. In: Lenz, HJ., Wilrich, PT. (eds) Frontiers in Statistical Quality Control 8. Physica-Verlag HD. https://doi.org/10.1007/3-7908-1687-6_14
Download citation
DOI: https://doi.org/10.1007/3-7908-1687-6_14
Publisher Name: Physica-Verlag HD
Print ISBN: 978-3-7908-1686-0
Online ISBN: 978-3-7908-1687-7
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)