Seasonal test for non-stationary time series data by periodogram analysis

Gumgum Darmawan, Budhi Handoko, Defi Yusti Faidah

Abstract


Abstract. Seasonal phenomenon is a common occurence in our daily activities. Many business and economic time series contain a seasonal phenomenon that repeats itself after a regular period of time. The smallest time period for this repetitive phenomenon is called the seasonal period. Seasonal test for time series data is well identified by Fisher’s exact test in Periodogram Analysis. However, this seasonal test is only accurate for stationary Seasonal time series data. So, in this research we apply seasonal test for non-stationary time series data from generated data. Performance of this test is determined by the  percentage  of fit identification. We apply this Periodogram Analysis to real data.


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References


Gladyshev 1961 Periodically correlated random sequences Sov.Math 2 385-388.

Fiering 1962 Mathematical synthesis of streamflow sequences for the analysis of river basins by simulation Myron 459-493

Noakes et al 1985 Forecasting monthly riverflow time series International Journal of Forecasting 1 179-190

Troutman 1979 Some Results in periodic autoregression Biometrika 6 219-228

Tiao and Grupe 1980 Hidden periodic autoregressive moving average models in time series data Biometrika 67 365-373

Pagano 1978 On periodic and multiple autoregressions”,Annals of Statistics 6 1310-1317

Salas et al. 1982 Estimation of ARMA models with seasonal parameters Water Resource Research 18 1006-1010

Vecchia 1985 Periodic autoregressive-moving average (PARMA) modelling with application to water resources Water Resources Bulletin 21 721-730

W K Li and Y V Hui 1988 An algorithm for the exact likelihood of periodic autoregressive moving average models Communication in Statistics 17 1483-1494

P LAnderson, M M Meerschaert and A V Vecchia 1999 Innovations algorithm for periodically stationary time series Stochastic Processes and their Applications 83 149-169

Lund and Basawa 2000 Recursive prediction and likelihood evaluation for periodic ARMA models Journal of Time Series Analysis 20 75-93

Ansley 1979 An algorithm for the exact likelihood of a mixed autoregressive moving average process Biometrika 66 59-65

Shao and Lund 2004 Computation and characteristization of autocorrelations and partial autocorrelations ini periodic ARMA models Journal of Time Series Analysis 25 359-372

Wold 1938 A study in the analysis of stationary Time series (Upsala)

Zellner 1978 Seasonal Analysis of Economic Time Series (U.S Department of commerce Washongton DC)

Dagum 1900 The X-11-ARIMA Seasonal Adjusment method,Catalogue,12-564 (Ontario : Statistics Canada)

Pierce 1980 A Survey of recent developments in seasonal adjustment The American Statistician 34 125-134

Hilmer and Tiao 1982An ARIMA based approach to seasonal adjustment Am. statist. Assoc. 77 63-70

Bell and Hillmer 1984 Issues Involved with the Seasonal Adjustment of Economic Time Series Journal of Business & Economic Statistics 2

Cupingood and Wei 1986 Seasonal Adjustment of Time Series Using One-Sided Filters Journal of Business & Economic Statistics October 4

G Darmawan, , B Handoko and Suparman 2016 Proceeding ICAS II (Bandung : Statistics Department Padjadjaran University)

Schuster 1898 On the investigation Hidden Periodicities with application to a supposed 26-days period of meteorogical phenomena Terr.mag.Atmos,elect 3 13-41

Wei W W S 2006 Time series Analysis univariate and multivariate method (USA : Department of Statistics Temple University)

Meerschaert and vecchia 1999 Innovations algorithm for periodically stationary time series


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