Skip to main content
SpringerLink
Log in

A comparison of time series and machine learning models for inflation forecasting: empirical evidence from the USA

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This study compares time series and machine learning models for inflation forecasting. Empirical evidence from the USA between 1984 and 2014 suggests that out of sixteen conditions (four different inflation indicators and four different horizons), machine learning models provide more accurate forecasting results in seven conditions and the time series models are better in nine conditions. Moreover, multivariate models give better results in fourteen conditions, and univariate models are better only in two conditions. This study shows that machine learning model prevails against time series models for the core personal consumption expenditure (core-PCE) inflation forecasting, and the time series model (ARDL) is better for the core consumer price (core-CPI) index inflation forecasting in all horizons.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. One can visit Stock and Watson [49] for a comprehensive review of the univariate and multivariate models and the literature since the great moderation.

  2. The gap is estimated as the difference between variable and Hodrick–Prescott (1997, HB) filtered trend, and the long-run trend is obtained by HP.

References

  1. Atkeson A, Ohanian LE (2001) Are Phillips curves useful for forecasting inflation? Fed Reserve Bank Minneap Q Rev 25:2

    Google Scholar 

  2. Adhiraki R, Agrawal RK (2014) A combination of artificial neural network and random walk models for financial time series forecasting. Neural Comput Appl 24:1441–1449

    Article  Google Scholar 

  3. Alamili M (2011) Exchange rate prediction using support vector machines: a comparison with artificial neural networks, Delft University of Technology Master of Science in Management of Technology. Thesis

  4. Anandhi V, Chezian MR (2013) Support vector regression in forecasting. Int J Adv Res Comput Commun Eng 2(10):4148–4151

    Google Scholar 

  5. Berument H, Kose N, Sahin A (2010) Seasonal patterns of inflation uncertainty for the US economy: an EGARCH model results. IUP J Monetary Econ 8(2):7–22

    Google Scholar 

  6. Brooks Chris (2014) Introductory econometrics for finance, 3rd edn. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  7. Chen S, Hardle W, Jeong K (2010) Forecasting volatility with support vector machine based GARCH model. J Forecast 29:406–433

    MathSciNet  MATH  Google Scholar 

  8. Chen G, Hayi G (2011) A support vector regression approach to estimate forest biophysical parameters at the object level using airborne lidar transects and quickbird data. Photogramm Eng Remote Sens 77(7):733–741

    Article  Google Scholar 

  9. Co HC, Boosarawongse R (2007) Forecasting Thailand’s rice export: statistical techniques vs. artificial neural networks. Comput Ind Eng 53:610–627

    Article  Google Scholar 

  10. Cao LJ (2003) Support vector machines experts for time series forecasting. Neurocomputing 51:321–339

    Article  Google Scholar 

  11. Cao LJ, Tay FEH (2001) Financial forecasting using support vector machines. Neural Comput Appl 10:184–192

    Article  MATH  Google Scholar 

  12. Enders W (2015) Applied econometric time series, 4th edn. Wiley, New York

    Google Scholar 

  13. Feng L, Zhang J (2014) Application of artificial neural networks in tendency forecasting of economic growth. Econ Model 40:76–80

    Article  Google Scholar 

  14. Gordon RJ (1990) US inflation, labor’s share, and the natural rate of unemployment. In: König H (ed) Economics of wage determination. Springer, Berlin, pp 1–34

    Google Scholar 

  15. Guegan D, Rakotomarolahy P (2010) Alternative methods for forecasting GDP. In: Jawadi F, Barnett WA, Group E (eds) Nonlinear modelling of economic and financial time series. Emerald Group Publishing, Boston, pp 161–187

    Chapter  Google Scholar 

  16. Hamdi M, Aloui C (2015) Forecasting crude oil price using artificial neural networks: a literature survey. Econ Bull 3(2):1339–1359

    Google Scholar 

  17. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer, New York, p 745

    Book  MATH  Google Scholar 

  18. Hossain A, Nasser M (2011) Recurrent support and relevance vector machines based model with application to forecasting volatiltiy of financial retuns. J Intell Learn Syst Appl 3:230–241

    Google Scholar 

  19. Hu TF, Luja IG, Su HC, Chang CC (2007) Forecasting inflation under globalization with artificial neural network-based thin and thick models. In: Si AO, Douglas C, Grundfest WS, Schruben L, Wu X, Iaeng (eds) World Congress on Engineering and Computer Science, USA, pp. 909–914

  20. Karlik B, Olgac AV (2011) Performance analysis of various activation functions in generalized MLP architectures of neural networks. Int J Artif Intell Expert Syst 1(4):111–122

    Google Scholar 

  21. Kim KJ (2003) Financial time series forecasting using support vector machines. Neurocomputing 55:307–319

    Article  Google Scholar 

  22. Kitov I, Kltov O (2013) Does Banque de France control inflation and unemployment? MPRA Paper No. 50239

  23. Kristjanpoller W, Minutolo MC (2015) Gold price volatility: a forecasting approach using te artificial neural network-GARCH model. Expert Syst Appl 42:7245–7251

    Article  Google Scholar 

  24. Laboissiere LA, Fernandes RAS, Lage GG (2015) Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks. Appl Soft Comput 35:66–74

    Article  Google Scholar 

  25. L C-T, Yeh H-Y (2009) Empirical of the Taiwan stock index option price forecasting model- applied artificial neural network. Appl Econ 41:1965–1972

    Article  Google Scholar 

  26. Lam M (2004) Neural network techniques for financial performance prediction: integrating fundamental and technical analysis. Decis Support Syst 37:567–581

    Article  Google Scholar 

  27. Lee TS, Chen NJ (2002) Investigating the information content of non-cash-trading index futures using neural networks. Expert Syst Appl 22:225–234

    Article  Google Scholar 

  28. Leigh W, Purvis R, Ragusa JM (2002) Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support. Decis Support Syst 32:361–377

    Article  Google Scholar 

  29. Lisi F, Medio A (1997) Is a random walk the best exchange rate predictor. Int J Forecast 13:255–267

    Article  Google Scholar 

  30. Manzan S, Zerom D (2013) Are macroeconomic variables useful for forecasting the distribution of U.S. inflation? Int J Forecast 29(3):469–478

    Article  Google Scholar 

  31. Mendez GC, Kapetanios G, Weale MR, Smith RJ (2004) The forecasting performance of the OECD composite leading indicators for France, Germany, Italy and the U.K. In: Michael PC, David FH (eds) A companion to economic forecasting. Blackwell Publishing, Hoboken, pp 386–408

    Chapter  Google Scholar 

  32. Mills TC (2004) Forecasting financial variables. In: Michael PC, David FH (eds) A comparison to economic forecasting. Blackwell Publishing, Hoboken, pp 510–539

    Chapter  Google Scholar 

  33. Mizrach B (1992) Multivariate nearest—Neighbour forecasts of EMS exchange rates. J Appl Econom 7:151–163

    Article  Google Scholar 

  34. Öğünç F, Akdoğan K, Başer S, Chadwick MG, Ertuğ D, Hülagü T, Tekatlı N (2013) Short-term inflation forecasting models for Turkey and a forecast combination analysis. Econ Model 33:312–325. doi:10.1016/j.econmod.2013.04.001

    Article  Google Scholar 

  35. Panda C, Narasimhan V (2007) Forecasting exchange rate better with artificial neural network. J Pol Model 29:227–236

    Article  Google Scholar 

  36. Pesaran MH, Shin Y (1999) An autoregressive distributed-lag modelling approach to cointegration analysis. In: Strom S (ed) Econometrics and economic theory in the 20th century. Cambridge University Press, Cambridge, pp 371–413

    Chapter  Google Scholar 

  37. Pesaran MH, Shin Y, Smith RJ (2001) Bounds testing approaches to the analysis of level relationships. J Appl Econom 16(3):289–326

    Article  Google Scholar 

  38. Perez-Cruz F, Rodriguez JA, Giner J (2003) Estimating GARCH Models using support vector machines. Quant Financ 3(3):163–172

    Article  MathSciNet  Google Scholar 

  39. Rawlings JO, Pantula S, Dickey DA (1998) Applied regression analysis: a research tool, 2nd edn. Springer, Berlin

    Book  MATH  Google Scholar 

  40. Rodriguez F, Rivero SS, Felix JA (1999) Exchange rate forecasts with simultaneous nearest—Neighbour methods: evidence from EMS. Int J Forecast 15:383–392

    Article  Google Scholar 

  41. Sermpinis G, Stasinakis C, Theofilatos K, Karathanasopoulos A (2014) Inflation and unemployment forecasting with genetic support vector regression. J Forecast 33:471–487

    Article  MathSciNet  MATH  Google Scholar 

  42. Sermpinis G, Stasinakis C, Theofilatos K, Karathanasopoulos A (2015) Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms-support vector regression forecast combinations. Eur J Oper Res 247:831–846

    Article  MathSciNet  MATH  Google Scholar 

  43. Sims C (1972) Money, income, and causality. Am Econ Rev 62(4):540–552

    Google Scholar 

  44. Sims CA (1980) Macroeconomics and reality. Econometrica 48(1):1–48

    Article  Google Scholar 

  45. Singhal D, Swarup KS (2011) Electricity price forecasting using artificial neural networks. Electr Power Energy Syst 33:550–555

    Article  Google Scholar 

  46. Smola AJ, Scholkopf B (1998) A tutorial on support vector regression. Royal Holloway College, NeuroCOLT Tech. Rep. TR., London

    MATH  Google Scholar 

  47. Stock JH, Watson MW (1999) Forecasting inflation. J Monet Econ 44(2):293–335

    Article  Google Scholar 

  48. Stock JH, Watson MW (2007) Why has U.S. inflation become harder to forecast? J Money Credit Bank 39:3–33

    Article  Google Scholar 

  49. Stock J, Watson M (2008) Phillips curve inflation forecasts. National Bureau of Economic Research, Cambridge

    Book  Google Scholar 

  50. Ye YF, Cao HB, Wang ZSY (2013) Exploring determinants of inflation in China based on L1-e-twin support vector regression. Proced Comput Sci 17:514–522

    Article  Google Scholar 

  51. Yu L, Wang SY, Lai KK (2009) A neural-network-based nonlinear metamodeling approach to financial time series forecasting. Appl Soft Comput 9:563–574

    Article  Google Scholar 

  52. Yu L, Wang SY, Lai KK (2007) Foreign-exchange-rate forecasting with artificial neural networks. Springer, New York

    Book  MATH  Google Scholar 

  53. Yu L, Wang SY, Lai KK (2005) A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates. Comput Oper Res 32(10):2523–2541

    Article  MATH  Google Scholar 

Download references

Acknowledgement

We would like to thank Osman Topac for his helpful comments.

Author information

Authors and Affiliations

  1. Department of Economics, Suleyman Sah University, 34956, Istanbul, Turkey

    Volkan Ülke

  2. Department of Banking, Gazi University, 06500, Ankara, Turkey

    Afsin Sahin

  3. Department of Computer Science, College of Engineering, Effat University, Jeddah, 21478, Saudi Arabia

    Abdulhamit Subasi

Authors
  1. Volkan Ülke
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Afsin Sahin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdulhamit Subasi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdulhamit Subasi.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 185 kb)

Appendix

Appendix

See Table 5.

Table 5 Data sources
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ülke, V., Sahin, A. & Subasi, A. A comparison of time series and machine learning models for inflation forecasting: empirical evidence from the USA. Neural Comput & Applic 30, 1519–1527 (2018). https://doi.org/10.1007/s00521-016-2766-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2766-x

Keywords

Search

Navigation