Automating Analytics: Forecasting Time Series in Economics and Business

Gerunov, Anton (2016): Automating Analytics: Forecasting Time Series in Economics and Business.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Automated analytics, forecasting, time series, ARIMA, business forecasting
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications

References

Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles and practice. OTexts, 2014.

Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. Vol. 1. Springer, Berlin: Springer series in statistics, 2001.

Makridakis, Spyros, Michele Hibon, and Claus Moser. "Accuracy of forecasting: An empirical investigation." Journal of the Royal Statistical Society. Series A (General) (1979): 97-145.

Makridakis, S., A. Andersen, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, and R. Winkler. "The accuracy of extrapolation (time series) methods: Results of a forecasting competition." Journal of forecasting 1, no. 2 (1982): 111-153.

Makridakis, Spyros, and Michele Hibon. "The M3-Competition: results, conclusions and implications." International journal of forecasting 16.4 (2000): 451-476.

Hamilton, James Douglas. Time series analysis. Vol. 2. Princeton: Princeton university press, 1994.

Melard, Guy, and J-M. Pasteels. "Automatic ARIMA modeling including interventions, using time series expert software." International Journal of Forecasting 16.4 (2000): 497-508.

Adya, Monica, Fred Collopy, J. Scott Armstrong, and Miles Kennedy. "Automatic identification of time series features for rule-based forecasting."International Journal of Forecasting 17, no. 2 (2001): 143-157.

Chakrabarti, Deepayan, and Christos Faloutsos. "F4: large-scale automated forecasting using fractals." Proceedings of the eleventh international conference on Information and knowledge management. ACM, 2002.

Hyndman, R. J., and Y. Khandakar. "Automatic time series forecasting: the forecast package for R." Journal of Statistical Software 27.3 (2008): 1-22.

Burnham, Kenneth P., and David R. Anderson. Model selection and multimodel inference: a practical information-theoretic approach. Springer Science & Business Media, 2003.

Burnham, Kenneth P., and David R. Anderson. "Multimodel inference understanding AIC and BIC in model selection." Sociological methods & research 33.2 (2004): 261-304.