Financial Time series Forecasting using Holt-Winters in H-step Ahead

Document Type : Research Paper


1 Prof. of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran

2 MSc. Student in Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran


Up to now various methods have been used to predict stock prices and profits. According to financial volatility markets, the most important thing is which of the methods can be applied to predict the optimal decision to help managers and decision makers and business sectors. Most studies have been done up to now to predict the time series, autoregressive method known as Box-Jenkins has been used to predict the time series. While there are many time series with seasonal variations or cyclic which can not be adequately modeled by a polynomial. In this study, Holt-Winters method is used to predict non-stationary time series data for the profit sale of an intermediate product. The results show that the proposed method compared with classical methods and procedures S-filtered has a higher performance in forecasting future values.


Main Subjects

Abrishami, A. & Yousefi Zenouz, R. (2014). Portfolio Selection by Robust Optimization. Financial Research, 16(2), 201-218. (in Persian)
Bahiraie, A., Etemadi, K., & Gerami–Asl, A. (2016). Predicting Companies Financial bankruptcy Listed in Tehran Stock Exchange using ANN, ANFIS, LOGIT. New Marketing Research Journal, 6 (2), 153-166. (in Persian)
Brockwell, P.J. & Davis, R.A. (1991). Introduction to time series and forecasting, New York: Springer.
Croux, C., Gelper, S. & Mahieu, K. (2011). Robust control charts for time series data. Expert Systems with Applications, 38(11), 13810–13815.
Daliri, S. & Khalilian, A. (2006). Forecasting growth and inflation rate in Iran’s agricultural section. Economical Analysis journal, 74, 183-215. (in Persian)
Fahimifard, S., Keikha, A. & Salarpour, M. (2009). Distinguished Agricultural products price forecasting by nurual network-self regression method with exogenous variables. Iranian Journal of Agricultural Economics and Development Research, 23(2), 68-85. (in Persian)
Gelper, S., Fried, R. & Croux, C. (2010). Robust Forecasting with Exponential and Holt–Winters Smoothing. Journal of Forecasting, 29(3), 285–300.
Giordani, P. & Villani, M. (2010). Forecasting macroeconomic time series with locally adaptive signal extraction. International Journal of Forecasting, 26(2), 312–325.
Kafaie, S. M. A. & Rahmani Fazli, H. (2014). Measuring event risk (A case study of Tehran Stock Exchange). Financial Research, 16(2), 345-358. (in Persian)
Kalekar, P.S. (2004). Time series forecasting using holt-winters exponential smoothing. Kanwal Rekhi School of Information Technology.
Khodaiari, A. & Rahimi, M. (2006). Determining a Proper Model to Forecast Cupper Price. International Journal of Industrial Engineering & Production Research, 40(1), 13-22. (in Persian)
Lai, C. P., Chung, P. C. & Tseng, V. S. (2010). A novel two-level clustering method for time series data analysis. Expert Systems with Applications, 37(9), 6319–6326.
Li, G., Gai, Z., Kang, X., Wu, Z. & Wang, Y. (2014). ESPSA: A prediction-based algorithm for streaming time series. Expert Systems with Applications, 41, 6098–6105.
Marcellino, M., Stock, J. H. & Watson, M.W. (2006). A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series. Journal of Econometrics, 135, 499-526.
Mohammad Alizadeh, A., Raei, R. & Mohammadi, S. (2015). Prediction of stock market crash using self-organizing maps. Financial Research, 17(1), 159-178. (in Persian)
Oguri, K., Adachi, H., Yi, C.H. & Sugiyama, M. (1992). Study on Egg Price Forecasting in Japan. Research Bulletin of the Faculty of Agriculture - Gifu University, 57, 157-164.
Panahi, H., Asadzadeh, A. & Jalili Marand, A. (2014). A Five-Year-Ahead Bankruptcy Prediction: the Case of Tehran Stock Exchange. Financial Research, 16(1), 57-76. (in Persian)
Rosseeuw, P.J. & Yohai, V.J. (1984). Robust regression by means of S-estimators, Robust and nonlinear time series analysis. Lecture Note in Statistics, 26, 256-272.
Rostami-tabar, B., Babaei, M. Z., Ducq, Y. & Syntetos, A. (2015). Non-stationary demand forecasting by cross-sectional aggregation. International Journal of Production Economics, 170(Part A), 297–309.
Salibian-Barrera M. & Yohai, V.J. (2006). A fast algorithm for S-regression estimates. Journal of Computational and Graphical Statistics, 15(2), 414–427.
Shahriari, H., Shariati, N. & Moslemi, A. (2012). Proposing a robust forecating time series method by application in finacial issues with robust methodology. Financial knowledge of Securities Analysis, 5(15), 97-114. (in Persian)
Tayebi, S.K. & Bayari, L. (2008). A Prediction of Iran’s Chicken Price by the ANN and Time Series Methods American –Eurasian. J. Agric. & Environ.Sci. No. 02.
Ye, F., Zhang, L., Zhang, D., Fujita, H., & Gong, Z. (2016). A novel forecasting method based on multi-order fuzzy time series and technical analysis. Information Sciences, 5, 1-38.