Record Details

Automating Analytics: Forecasting Time Series in Economics and Business

Journal of Economics and Political Economy

View Archive Info
 
 
Field Value
 
Title Automating Analytics: Forecasting Time Series in Economics and Business
 
Creator GERUNOV, Anton Antonov; Chief Assistant Professor, Sofia University “St. Kliment Ohridski”
 
Subject Automated analytics; Forecasting; Time series; ARIMA; Business forecasting.
—
 
Description 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.
 
Publisher Journal of Economics and Political Economy
Journal of Economics and Political Economy
 
Contributor —
 
Date 2016-06-18
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
—
—
 
Format application/pdf
 
Identifier http://www.kspjournals.org/index.php/JEPE/article/view/790
10.1453/jepe.v3i2.790
 
Source Journal of Economics and Political Economy; Vol 3, No 2 (2016): June; 340-349
Journal of Economics and Political Economy; Vol 3, No 2 (2016): June; 340-349
2148-8347
 
Language eng
 
Relation http://www.kspjournals.org/index.php/JEPE/article/view/790/860
http://www.kspjournals.org/index.php/JEPE/article/downloadSuppFile/790/351
http://www.kspjournals.org/index.php/JEPE/article/downloadSuppFile/790/352
http://www.kspjournals.org/index.php/JEPE/article/downloadSuppFile/790/353
 
Rights Copyright (c) 2016 Journal of Economics and Political Economy
http://creativecommons.org/licenses/by-nc/4.0