Review Article of American Journal of Computer Sciences and Applications
Application of Artificial Intelligence in Forecasting: A Systematic Review
Albert Annor-Antwi and Ayman A. M. Al-Dherasi
Supervisor: Dr. Yang Chunting
School of Electrical and Electronic Technology and Computer Science, Zhejiang University of Science and Technology
Purpose: The aim of this reach is to identify how Artificial Intelligence (AI) could be used in enhancing forecasting to achieve more accurate outcomes. The research also explores the influence that forecasting has on global economy and the reasons why it needs to be accurate. Also, the research explains various pitfalls identified in forecasting. Method: This research implements two research approaches which are review of literature and formulation of hypotheses. Seven hypotheses are created. Findings: AI, when integrated with other technologies such as Machine Learning (ML) and when provided with the right computer power, yields much more accurate results than many other forecasting methods. The technology is costly, however, and it is prone to cyber-attacks. Conclusion: The future of business is highly reliant on forecasting, which directly impacts the global economy. But, not every business will have the power to own the forecasting technology due to the cost, and business will need to increase security to protect the forecasting systems.
Keywords: Artificial Intelligence, Forecasting, Business, Finance, Market, Machine Learning
How to cite this article:
Albert Annor-Antwi, Ayman A. M. Al-Dherasi, Supervisor: Dr. Yang Chunting. Application of Artificial Intelligence in Forecasting: A Systematic Review. American Journal of Computer Sciences and Applications, 2019; 2:22. DOI: 10.28933/ajcsa-2019-11-0605
1. H. Asorey, “How AI Is Transforming Forecasting For the Better,” Salesforce.com. [Online]. Available: https://www.salesforce.com/quotable/articles/ho w-AI-is-transforming-forecasting-for-the-better/. [Accessed: 21-Sep-2019].
2. Jones JW, Hansen JW, Royce FS, Messina CD. Potential benefits of climate forecasting to agriculture. Agriculture, ecosystems & environment. 2000 Dec 1;82(1-3):169-84.
3. Pal Singh Toor T, Dhir T. Benefits of integrated business planning, forecasting, and process management. Business Strategy Series. 2011 Nov 8;12(6):275-88.
4. B. Bass, “Advantages and Disadvantages of Forecasting Methods of Production and Operations Management,” Small Business -Chron.com, 05-Feb-2019. [Online]. Available: https://smallbusiness.chron.com/advantages-disadvantages-forecasting-methods-production-operations-management-19309.html. [Accessed: 21-Sep-2019].
5. Daut MA, Hassan MY, Abdullah H, Rahman HA, Abdullah MP, Hussin F. Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review. Renewable and Sustainable Energy Reviews. 2017 Apr 1;70:1108-18.
6. N. Baird, “Six Ways AI Can Impact Retail Forecasting: Hype Vs. Reality,” Forbes, 22-Feb-2019. [Online]. Available: https://www.forbes.com/sites/nikkibaird/2019/0 2/21/six-ways-ai-can-impact-retail-forecasting-hype-vs-reality/#4b9ef78f5d93. [Accessed: 21-Sep-2019].
7. J. Wiley, “Technological forecasting,” Innovation Portal, 2019. [Online]. Available: http://www.innovation-portal.info/toolkits/technological-forecasting/. [Accessed: 21-Sep-2019].
8. J. Feizabadi and A. Shrivastava, “Does Artificial Intelligence Enabled Demand Forecasting Improve Supply Chain Efficiency? – Supply Chain 24/7,” Supply Chain 24 7, 20-Nov-2018. [Online]. Available: https://www.supplychain247.com/article/does_ai_enabled_demand_forecasting_improve_supplychain_efficiency. [Accessed: 21-Sep-2019].
9. Hyndman RJ, Athanasopoulos G. Forecasting: principles and practice. OTexts; 2018 May 8.
10. Baliyan A, Gaurav K, Mishra SK. A review of short term load forecasting using artificial neural network models. Procedia Computer Science. 2015 Jan 1;48:121-5.
11. “Transforming Financial Forecasting with Data Science and Machine Learning at Uber,” Uber Engineering Blog, 05-Jul-2018. [Online]. Available: https://eng.uber.com/transforming-financial-forecasting-machine-learning/. [Accessed: 21-Sep-2019].
12. Ganpact, “The right AI can hone revenue forecasting for better decisions,” Genpact. [Online]. Available: https://www.genpact.com/insight/point-of-view/the-right-ai-can-hone-revenue-forecasting-for-better-business-decisions. [Accessed: 21-Sep-2019].
13. Atsalakis GS, Valavanis KP. Surveying stock market forecasting techniques–Part II: Soft computing methods. Expert Systems with Applications. 2009 Apr 1;36(3):5932-41.
14. Genpact, “Smooth sailing ahead: applying AI to financial forecasting,” Genpact. [Online]. Available:https://www.genpact.com/insight/article/smooth-sailing-ahead-applying-ai-to-financial-forecasting. [Accessed: 21-Sep-2019].
15. M. T. Wroblewski, “What Is the Relative Importance of Forecasting?,” Small Business -Chron.com, 08-Aug-2019. [Online]. Available: https://smallbusiness.chron.com/relative-importance-forecasting-35627.html. [Accessed: 21-Sep-2019].
16. Huh WT, Lall U. Optimal crop choice, irrigation allocation, and the impact of contract farming. Production and Operations Management. 2013 Sep;22(5):1126-43.
17. Miletić S. Modeling exchange rate volatility in CEEC countries: Impact of global financial and European sovereign debt crisis. Megatrend revija. 2015;12(1):105-22.
18. V. Duff, “How to Forecast the Stock Market,” Finance, 07-Feb-2017. [Online]. Available: https://finance.zacks.com/forecast-stock-market-7835.html. [Accessed: 21-Sep-2019].
19. Boyacioglu MA, Avci D. An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: the case of the Istanbul stock exchange. Expert Systems with Applications. 2010 Dec 1;37(12):7908-12.
20. C. McFadden, “AI Might Be the Future for Weather Forecasting,” Interesting Engineering,21-Mar-2019. [Online]. Available: https://interestingengineering.com/ai-might-be-the-future-for-weather-forecasting. [Accessed: 21-Sep-2019].
21. S. Barlow, “Can we trust machines to predict the stock market with 100% accuracy?,” Metro, 08-May-2019. [Online]. Available: https://metro.co.uk/2019/05/06/can-we-trust-machines-to-predict-the-stock-market-with-100-accuracy-9325480/. [Accessed: 21-Sep-2019].
22. Azati, “How much does artificial intelligence (AI) cost in 2019?,” AI, ML, NLP consulting and software development., 23-Jan-2019. [Online]. Available: https://azati.ai/how-much-does-it-cost-to-utilize-machine-learning-artificial-intelligence/. [Accessed: 21-Sep-2019].
23. Korinek A, Stiglitz JE. Artificial intelligence and its implications for income distribution and unemployment. National Bureau of Economic Research; 2017 Dec 29.
24. Agrawal A, Gans J, Goldfarb A. Prediction machines: the simple economics of artificial intelligence. Harvard Business Press; 2018 Apr 17.
25. J. Petersson, “Price Forecasting: Applying Machine Learning Approaches to Electricity,Flights, Hotels, Real Estate, and Stock Pricing,” AltexSoft, 26-Feb-2019. [Online]. Available: https://www.altexsoft.com/blog/business/price-forecasting-machine-learning-based-approaches-applied-to-electricity-flights-hotels-real-estate-and-stock-pricing/. [Accessed: 21-Sep-2019].
26. Dirican C. The impacts of robotics, artificial intelligence on business and economics. Procedia-Social and Behavioral Sciences. 2015 Jul 3;195:564-73.
27. Seto KC, Güneralp B, Hutyra LR. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences. 2012 Oct 2;109(40):16083-8.
28. Brundage M, Avin S, Clark J, Toner H, Eckersley P, Garfinkel B, Dafoe A, Scharre P, Zeitzoff T, Filar B, Anderson H. The malicious use of artificial intelligence: Forecasting, prevention, and mitigation. arXiv preprint arXiv:1802.07228. 2018 Feb 20.
29. Hauptman A, Sharan Y. Foresight of evolving security threats posed by emerging technologies. Foresight. 2013 Sep 16;15(5):375-91.