Scenario Based Time Series Prediction Using Seasonal ARIMA
Rapidly evolving businesses generate massive amounts of time-stamped data sequences and cause a demand for both univariate and multivariate time series forecasting. For such data, traditional predictive models based on autoregression are often not sufficient to capture complex nonlinear relationships between multidimensional features and the time series outputs. An architecture for combined, multilevel time series prediction is proposed, which is suitable for many different universal regressors and combination methods. This architecture did not perform the results which are scenario based. Due to this the predicted result was not correct. Therefore an algorithm called seasonal ARIMA is used to predict the weather conditions where the seasonal and periodic item sets are taken. Regression and combination will be performed according to the threshold value, finally clusters are formed which will show the predicted result. The proposed system is evaluated in different scenarios and showed a clear prediction performance gain
M. Aiolfi and A. Timmermann, ―Persistence in Forecasting Performance and Conditional Combination Strategies,‖ J. Econometrics, vol. 127, nos. 1/2, pp. 31-53, 2006.
V. Assimakopoulos and K. Nikolopoulos, ―The Theta Model: A Decomposition Approach to Forecasting,‖ Int’l J. Forecasting, vol. 16, no. 4, pp. 521-530, 2000.
D.W. Bunn, ―A Bayesian Approach to the Linear Combination of Forecasts (1975),‖ Operational Research Quarterly, vol. 26, no. 2, pp. 325-329, 1975.
M. Casdagli, ―Nonlinear Prediction of Chaotic Time Series,‖ Physica, vol. 35, pp. 335-356, 1989.
E.S. Gardner, ―Exponential Smoothing: The State of the Art—Part II,‖ Int’l J. Forecasting, vol. 22, no. 4, pp. 637-666, 2006.
C.L. Giles, S. Lawrence, and A.C. Tsoi, ―Noisy Time Series Prediction Using Recurrent Neural Networks and Grammatical Inference,‖ Machine Learning, vol. 44, no. 1, pp. 335-356, 2001.
T. Dietterich and R. Michalski, ―Learning to Predict Sequences,‖ Machine Learning: An Artificial Intelligence Approach, R. Michalski, J. Carbonell, and T. Mitchell, eds., vol. 2, pp. 63-106, Morgan
Dymitr Ruta, Bogdan Gabrys, and Christiane Lemke ―A Generic Multilevel Architecture for Time Series Prediction‖, vol. 23, no. 3, March 2011
C. Granger and R. Ramanathan, ―Improved Methods of Combining Forecasts,‖ J. Forecasting, vol. 3, no. 2, pp. 197-204, 1984.
S.G. Makridakis, S.C. Wheelwright, and R.J. Hyndman, Forecasting: Methods and Applications. Wiley, 1998.
S. Mukherjee, E. Osuna, and F. Girosi, ―Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines,‖ Proc. IEEE Workshop Neural Networks for Signal Processing, pp. 511-520, 1997.
P. Newbold and C. Granger, ―Experience with Forecasting Univariate Time Series and the Combination of Forecasts,‖J. Royal Statistical Soc., Series A (General), vol. 137, no. 2, pp. 131- 165, 1974.
J. Peng and J.A.D. Aston, ―The SSM Toolbox for MATLAB,,‖ Inst. of Statistical Science, Academia Sinica, 2007.
J.W. Taylor, ―Exponential Smoothing with a Damped Multiplicative Trend,‖ Int’l J. Forecasting, vol. 19, no. 4, pp. 715-725, 2003.
R.J. Hyndman and B. Billah, ―Unmasking the Theta Method,‖ Int’l J. Forecasting, vol. 19, no. 2, pp. 287-290, 2003.
―Delft Center for Systems and Control—Software,‖ http://www. dcsc.tudelft.nl/research/software, 2007.
C. Lemke and B. Gabrys, ―Do We Need Experts for Time Series Forecasting?‖ Proc 16th European Symp. Artificial Neural Networks (ESANN ’08), 2008.
J.G.D. Gooijer and R.J. Hyndman, ―25 Years of Time Series Forecasting,‖ Int’l J. Forecasting, vol. 22, no. 3, pp. 443-473, 2006.
J.H. Stock and M.W. Watson, ―Combination Forecasts of Output Growth in a Seven-Country Data Set,‖ J. Forecasting, vol. 23, no. 6, pp. 405-430, 2004.
T. Tera¨svirta, D. van Dijk, and M.C. Medeiros, ―Linear Models, Smooth Transition Autoregressions, and Neural Networks for Forecasting Macroeconomic Time Series: A Reexamination,‖ Pontifı´cia Univ. Cato´ lica de Rio de Janeiro, Dept. de Economı´a, 2004.
A. Timmermann, ―Forecast Combinations,‖ Handbook of Economic Forecasting. G. Elliott, C. Granger,, and A. Timmermann, eds., pp. 135-196, Elsevier, 2006
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