PRAKIRAAN CURAH HUJAN KECAMATAN KAIRATU KABUPATEN SERAM BAGIAN BARAT DENGAN MODEL AUTOREGRESIVE INTEGRATED MOVING AVERAGE (ARIMA)

  • Grace Loupatty Jurusan Matematika FMIPA Universitas Pattimura
DOI: https://doi.org/10.30598/barekengvol1iss2pp40-48
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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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.
Issue
Vol 1 No 2 (2007): BAREKENG : Jurnal Ilmu Matematika dan Terapan
Section
Articles

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