Abstract
Abstract. With the growing ability of organizations in the public and private sector to collect large volumes of real-time data, the mounting pile of information presents specific challenges for storage, processing, and analysis. Many organizations do need data analysis for the purposes of planning and logistics. Likewise, governments and regulators will need analysis to support policy-making, implementation and controlling. All this leads to the importance of being able to generate large-scale analytics under (sometimes severe) resource constraints. This paper investigates a possible solution – automating analytics with a special focus on forecasting time series. Such approach has the benefit of being able to produce scalable forecasting of thousands of variables with relatively high accuracy for a short period of time and few resources. We first review the literature on time series forecasting with a particular focus on the M, M-2, and M-3 forecasting competition and outline a few major conclusions supported across different empirical studies. The paper then proceeds to explore the typical structure of a time-series variables using Bulgarian GDP growth and show how the ARIMA modeling with a seasonal component can be used to fit economic data of this class. We also review some major approaches to automating forecasting and outline the benefits of selecting the optimal model from a large set of ARIMA alternatives using an information criterion. A possible approach to fit an automated forecasting algorithm on four crucial economic time series from the Bulgarian economy is demonstrated. We use data on GDP growth, inflation, unemployment, and interest rates and fit a large number of possible models. The best ones are selected by taking recourse to the Akaike Information Criterion. The optimal ARIMA models are studied and commented. Forecast accuracy metrics are presented and a few major conclusions and possible model applications are outlined. The paper concludes with directions for further research.
Keywords. Automated analytics, Forecasting, Time series, ARIMA, Business forecasting.
JEL. C22, C53, E37.
References
Adya, M., Collopy, F., Armstrong, J.S., & Kennedy, M. (2001). Automatic identification of time series features for rule-based forecasting. International Journal of Forecasting, 17(2), 143-157. doi. 10.1016/S0169-2070(01)00079-6
Burnham, K.P., & Anderson, D.R. (2003). Model selection and multimodel inference: a practical information-theoretic approach. Springer Science & Business Media. doi. 10.1007/b97636.
Burnham, K.P., & Anderson, D.R. (2004). Multimodel inference understanding AIC and BIC in model selection. Sociological methods & research, 33(2), 261-304. doi. 10.1177/0049124104268644
Chakrabarti, D., & Faloutsos, C. (2002, November). F4: large-scale automated forecasting using fractals. In Proceedings of the eleventh international conference on Information and knowledge management (pp. 2-9). ACM. doi. 10.1145/584792.584797
Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1). Springer, Berlin: Springer series in statistics. doi. 10.1007/978-0-387-84858-7
Hamilton, J.D. (1994). Time series analysis (Vol. 2). Princeton: Princeton university press.
Hyndman, R.J., & Athanasopoulos, G. (2014). Forecasting: principles and practice. OTexts. doi. 10.1111/j.1467-856X.2005.00178.x
Hyndman, R.J., & Khandakar, Y. (2008). Automatic time series forecasting: the forecast package for R. Journal of Statistical Software 27(3) , 1-22.doi. 10.18637/jss.v027.i03
Makridakis, S., & Hibon, M. (2000). The M3-Competition: results, conclusions and implications. International journal of forecasting, 16(4), 451-476. doi. 10.1016/S0169-2070(00)00057-1
Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen, E. & Winkler, R. (1982). The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of Forecasting, 1(2), 111-153. doi. 10.1002/for.3980010202
Makridakis, S., Hibon, M., & Moser, C. (1979). Accuracy of forecasting: An empirical investigation. Journal of the Royal Statistical Society. Series A (General), 97-145. doi. 10.2307/2345077
Melard, G., & Pasteels, J.M. (2000). Automatic ARIMA modeling including interventions, using time series expert software. International Journal of Forecasting, 16(4), 497-508. doi. DOI: 10.1016/S0169-2070(00)00067-4
