Browse > Article
http://dx.doi.org/10.5351/KJAS.2022.35.5.645

Performance comparison for automatic forecasting functions in R  

Oh, Jiu (Department of Applied Statistics, Chung-Ang University)
Seong, Byeongchan (Department of Applied Statistics, Chung-Ang University)
Publication Information
The Korean Journal of Applied Statistics / v.35, no.5, 2022 , pp. 645-655 More about this Journal
Abstract
In this paper, we investigate automatic functions for time series forecasting in R system and compare their performances. For the exponential smoothing models and ARIMA (autoregressive integrated moving average) models, we focus on the representative time series forecasting functions in R: forecast::ets(), forecast::auto.arima(), smooth::es() and smooth::auto.ssarima(). In order to compare their forecast performances, we use M3-Competiti on data consisting of 3,003 time series and adopt 3 accuracy measures. It is confirmed that each of the four automatic forecasting functions has strengths and weaknesses in the flexibility and convenience for time series modeling, forecasting accuracy, and execution time.
Keywords
automatic forecasting functions; exponential smoothing models; ARIMA; forecast package; smooth package; M3-competition;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Bergs J, Heerinckx P, and Verelst S (2014). Knowing what to expect, forecasting monthly emergency departme nt visits: A time-series analysis, International Emergency Nursing, 22, 112-115.   DOI
2 Canova F and Hansen BE (1995). Are seasonal patterns constant over time? A test for seasonal stability, Journal of Business and Economic Statistics, 13, 237-252.
3 Hyndman RJ and Koehler AB (2006). Another look at measures of forecast accuracy, International Journal of Forecasting, 22, 679-688.   DOI
4 Akaike H (1974). A new look at the statistical model identification, IEEE Transactions on Automatic Control, 19, 716-723.   DOI
5 Armstrong JS (1978). Long-range Forecasting: From Crystal Ball to Computer, Wiley-Interscience.
6 Box GEP and Jenkins GM (1970). Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco.
7 Flores BE (1986). A pragmatic view of accuracy measurement in forecasting, Omega, 14, 93-98.   DOI
8 Makridakis S, Spiliotis E, Assimakopoulos V (2018). The M4 competition: results, findings, conclusion and way forward, International Journal of Forecasting, 34, 802-808.   DOI
9 Hyndman RJ, Koehler AB, Ord JK, and Snyder RD (2008). Forecasting with Exponential Smoothing: The State Space Approach, Springer, Berlin.
10 Hyndman RJ and Khandakar Y (2008) Automatic time series forecasting: The forecast package for R, Journal of Statistical Software, 27, 1-22.
11 Kwiatowski D, Phillips PCB, and Schmidt P (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?, Journal of Econometrics, 54, 159-178.   DOI
12 Svetunkov I (2017). Statistical models underlying functions of 'smooth' package for R, Working Paper of Department of Management Science, Lancaster University 2017, 1-52.
13 Makridakis S and Hibon M (2000). The M3-Competition: results, conclusions and implications, International Journal of Forecasting, 16, 451-476.   DOI
14 Papacharalampous G, Tyralis H, and Koutsoyiannis D (2018). Predictability of monthly temperature and precipitation using automatic time series forecasting methods, Acta Geophys, 66, 807-883.   DOI