PRAKIRAAN CURAH HUJAN KECAMATAN KAIRATU KABUPATEN SERAM BAGIAN BARAT DENGAN MODEL AUTOREGRESIVE INTEGRATED MOVING AVERAGE (ARIMA)
Keywords:
Forecasting, time series data, stationerity, Mean Squared Error (MSE), Mean Absolute Error (MAE).
Abstract
Forecasting is an activity to use the past data as the basic to predict the future event that will occur. The result from the prediction is an un-sure event or just a guess, but with some certain methods then the prediction will be more than a guess, it means that it is a scientific guess. The data analysis technique with autoregressive integrated moving average (ARIMA) method can give the early information that is needed as the material for the consideration to take the decision and the action itself
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References
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H. Imam Ghozali. (2006). Analisis Multivariat Lanjutan Dengan SPSS. Edisi pertama, UNDIP, Semarang.
Iqbal Hasan. (2004). Analisis Data Penelitian Dengan Statistik. PT. Bumi Aksara, Jakarta.
Sugiarto dan Harijono. (2000). Statistik Teori dan Aplikasi. Erlangga, Jakarta.
Surjadi, PA. (1990). Teori kemungkinan dan Statistik. Edisi keempat, ITB, Bandung.
(Soejoenti & Zanzawi. 1987). Materi Pokok Analisis Runtun Waktu. Karunia, Jakarta.
Spyros Markidakis, Steven C.W & V.E. McGee. (1999). Peramalan: Metode dan Aplikasi. Terjemahan dari Forecasting, diterjemahkan oleh Untung Sus Andriyanto & Abdul Basith. Erlangga, Jakarta.
Published
2007-12-01
How to Cite
[1]
G. Loupatty, “PRAKIRAAN CURAH HUJAN KECAMATAN KAIRATU KABUPATEN SERAM BAGIAN BARAT DENGAN MODEL AUTOREGRESIVE INTEGRATED MOVING AVERAGE (ARIMA)”, BAREKENG: J. Math. & App., vol. 1, no. 2, pp. 40-48, Dec. 2007.
Section
Articles
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