Abstract
The evolution of modern computing techniques has targeted researchers and technical professionals to delve into an era where forecasting charts future business plans and events. Time series plays a vital role in forecasting processes. Diverse applications including physics portray time in multiple dimensions. In an attempt at recording and analyzing information, the early methods of forecasting used charts, indicators, and numbers. Many business cycles are not regular because they tend to vary due to multiple factors including Weather, Holiday, Season and Flood. Sustaining the commercial balance under such a scenario is done smoothly with the help of capturing weekly, monthly and daily behaviours. Selection and implementation of right forecasting technique require insights into historical data and mapping it with the market expectations which many a time is not aptly forcing for organizations. This paper represents various forecasting models and an approach to predict the operational/sales data on a daily basis using combined estimators. Markov Chain Monte Carlo (MCMC) has played an extraordinary role in modern engineering applications including economics, physics, statistics and beyond for the past decade. The heart of Monte Carlo simulation lies in the art of drawing random statistical samples. Empirical evaluation of various forecasting models resulted in the understanding of the stochastic nature of the processes. It is observed that out of all the time series models, MCMC yields a satisfactory outcome. Moreover, this research provides a comparison of the available forecasts and formulates the procedure for the best-suited technique. The results show that the outcome is a combined estimate of all the established prediction models.
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References
Friedman, W.A.: Fortune Tellers: The Story of America’s First Economic Forecaster. Princeton University Press, Princeton (2014)
Brillinger, D.R.: Time series: general. Contract no: 20851A2/1/020
Hyndman, R.J.: Moving Averages. 8 Nov 2009
Adhikari, R., Agrawal, R.K.: An Introductory Study on Time Series Modeling and Forecasting
Brooks, S.P.: Markov Chain Monte Carlo method and its application. J. Royal Stat. Soc. Ser. D (The Statistician) 47(1), 69–100 (1998)
Welch, G., Bishop, G.: An introduction to the Kalman Filter, TR 95-041. Department of Computer Science, University of North Carolina at Chapel Hill Chapel Hill, NC 27599-3175
Brockwell, P.J., Davis, R.A.: Introduction to Time Series and Forecasting©. Springer, New York (2002)
Karapanagiotidis, P.: Literature Review of Modern Time Series Forecasting Methods. 31 Jul 2012
Müller, K.-R., Smola, A., Rätsch, G., Schölkopf, B., Kohlmorgen, J., Vapnik, V.: Using support vector machines for time series prediction, pp. 243–253, https://doi.org/10.1515/9783110915990.1 (1999)
Kong, D.: Local government revenue forecasting: The California County experience. J. Public Budgeting, Acc. Financ. Manag. 19(2), 178–199 (2007)
Liu, T.: Application of Markov Chains to analyze and predict the time series. In: Modern Applied Science, Specialized Scientific Research Program on Scientific Research of high-level talents in Ankang University, (Program No. AYQDZR200705)
Scott, S.L., Varian, H.: Predicting the Present with Bayesian Structural Time Series. 28 Jun 2013
Breiman, L.: Bagging predictors. Mach. Learn. 26(2), 123–140 (1996)
Freund, Y., Schapire, R.: Experiments with a new boosting algorithm. In: Machine Learning: Proceedings of the 13th International Conference, pp. 148–156 (1996)
Hoeting, J., Madigan, D., Raftery, A., Volinsky, C.: Bayesian Model Averaging: A Practical Tutorial. www.stat.colostate.edu/~jah/documents/bma2.ps (1999)
Chen, C., Twycross, J., Garibaldi, J.M.: A new accuracy measure based on bounded relative error for time series forecasting. PLoS ONE 12(3), e0174202 (2017). https://doi.org/10.1371/journal.pone.0174202
Petris, G., Petrone, S.: State space models in R. J. Stat. Softw. 41(4), 1–25 (2011)
Kayacan, K., Ulutas, B., Kaynak, O.: Grey system theory-based models in time series prediction. Expert Syst. Appl. 37(2), 1784–1789 (2010)
Jarrett, J.E., Kyper, E.: ARIMA modelling with intervention to forecast and analyze Chinese stock prices. INTECH Open Access Publisher; Received 22 Jan 2011; Accepted 30 Apr 2011
De Livera, A.M., Hyndman, R.J., Snyder, R.D.: Forecasting time series with complex seasonal patterns using exponential smoothing. Department of Econometrics and Business Statistics, Monash University
Thomopoulos, N.T.: Applied forecasting methods. J. Pol. Prentice Hall, Englewood Cliffs, N.J. (1980)
Lancaster, G.A., Lomas R.A.: Forecasting for sales and materials management (1985)
Mentzer, J.T., Moon M.A.: Sales forecasting management: a demand management approach, 2nd edn. Sage Publications, Thousand Oaks, London (2005). ISBN 1-4129-0571-0 Softcover, 347 pages, Int. J. Forecasting, 22(4), 821–821, Elsevier
Pegels, C.C.: Exponential forecasting: some new variations. Manage. Sci. 15, 311–315 (1969)
Hyndman et al.: Time series and forecasting in R (2008)
De Livera et al.: Forecasting time series with complex seasonal patterns using exponential smoothing (2010)
Acknowledgements
We would like to thank Tata Consultancy Services (TCS) Innovation Labs (IIT Madras Research Park) for helping us carry out this research. They all helped us to share our thoughts and views on the final version of the paper. We would also like to extend our thanks to the special volume of editors at IcoRD’19 for inviting our contribution.
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Meenakshisundaram, S., Srikanth, A., Ganesan, V.K., Vijayarangan, N., Srinivas, A.P. (2019). Forecasting: Bayesian Inference Using Markov Chain Monte Carlo Simulation. In: Chakrabarti, A. (eds) Research into Design for a Connected World. Smart Innovation, Systems and Technologies, vol 134. Springer, Singapore. https://doi.org/10.1007/978-981-13-5974-3_19
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