Comparative Study of Concepts and Implementation of Various Univariate Time Series Analysis Methods

  • Adi Nugroho Fakultas Teknologi Informasi Universitas Kristen Satya Wacana
Keywords: Comparison of Time Series Analysis Methods, Time Series Analysis


Univariate time series analysis is a very important method in everyday life because this method has many practical applications. Various methods of time series analysis have been previously discovered by experts. However, until now the experts have not been able to determine exactly which method is best implemented in certain time series data. In this paper we will make a comparison of the 8 (eight) time series analysis methods that are most often used, namely SMA, EMA, WMA, SES, Holt's Method, Holt's Winter Seasonal Method, ARIMA, and SARIMA, with the aim of providing guidance to readers to choose which method is most appropriate for a given time series data. Research for this was conducted using the same data for all methods.


Download data is not yet available.


[1] Bivan, RS, Pebema, EJ, Gomez, V., Rubio, 2008, Applied Spatial Data Analysis with R. SpringerScience + Business Media, LLC, New York.
[2] Chatfield, C., 2000, Time Series Forecasting. Chapman & Hall / CRC, New York.
[3] Datta, K., 2011, ARIMA Forecasting of Inflation in the Bangladesh Economy, The IUP Journal of Bank Management, Vol 10, No. 4, pg. 7-15.
[4] De Gooijer, JG, Hindman, RJ, 2006, 25 Years of Time Series Forecasting, International Journal of Forecasting, Vol. 22, pg. 443-473.
[5] Gumma, MK, Thenkabail, P., Muralikrishna, IV, Velpuri, MN, Parthasarathi, T., Gangadharara, OJ, Dheeravath, V., Chandrasekhar, MB, Nalan, SA, Gaur, A., 2011, Changes in Agricultural Cropland Areas Between a Water-Surplus Year and a Water-Deficit Year Impacting Food Security, Determined Using MODIS 250 m Time-Series Data and Spectral Matching Techniques, in the Krishna River Basin (India). International Journal of Remote Sensing Vol. 32, No. 12, pg. 3495– 3520
[6] Hartomo, KD, 2016, Rainfall Prediction Model Using Exponential Smoothing Seasonal Planting Index (ESSPI) Method to Determine Planting Patterns. Doctoral Dissertation, Faculty of Mathematics and Natural Sciences, Gadjah Mada University, Jogyakarta.
[7] Huda, AM, Choiruddin, A., Budiarto, O., Sutikno, 2012, Forecasting Rainfall Data Using the Seasonal Autoregression Moving Average (SARIMA) with Outlier Detection as Efforts to Optimize Agricultural Production in Mojokerto Regency. National Seminar on Food and Energy Sovereignty, Faculty of Agriculture, Trunojoyo University, Madura.
[8] Liu, X., Swift, S., Tucker, A., Cheng, G., Loizou, G., 2000, Multivariate Time Series Modeling, Department of Computer Science, Birbeck College, University of London.
[9] Maruddani, DA, Safitri, D, 2008, Vector Autoregression For Stock Price Forecasting PT Indofood Sukses Makmur Tbk. Mathematical Journal, Vol 1. No 1. Mauriccio, JA, 1999, An Algorithm for the Exact Likelihood of a Stationary Vector Autoregression Moving Average, Journal of Time Series Analysis, Vol. 23, No. 4, pg. 473-486.
[10] McLeod, AI, Sales, PR H, 1983, AS 191 Algorithm: An Algorithm for Approximate Likelihood Calculation of ARMA and Seasonal ARMA, Journal of Applied Statistics, Vol. 32, Issue 2, p. 211-223.
[11] Nugroho, Adi, 2021. Data Science Using the R Language: Data Analysis, Visualization, and Modeling. ANDI Offset Publisher, Jogyakarta.
[12] Nugroho, Adi, 2017. Rainfall Prediction Model and Land Classification in Semarang Regency that Consider Water Conservation Principles Based on Multivariate Time Series Methods and Fuzzy Logic Using Climatological and Hydrological Data. Doctoral Dissertation, Faculty of Mathematics and Natural Sciences, Gadjah Mada University, Jogyakarta.
[13] Nugroho, A., Simanjuntak, BH, 2014, ARMA (Autoregressive Moving Average) Model for Prediction of Rainfall in Regency of Semarang - Central Java - Republic of Indonesia, International Journal of Computer Science Issues, Vol. 11, Issue 3.
[14] Rufino, C., 2012, Signal Extraction from Philipine National Account Statistics Using ARIMA Model-based Methodology, De La Salle University, Philipine.
[15] Schumway, RH, Stoffer, DS, 2011, Time Series Analysis and Its Applications with R Examples, Springer Science + Business Media, New York.
How to Cite
Nugroho, A. (2021). Comparative Study of Concepts and Implementation of Various Univariate Time Series Analysis Methods. Indonesian Journal of Computing and Modeling, 3(2). Retrieved from