PENERAPAN MODEL ARIMA-NEURAL NETWORK HYBRID UNTUK PERAMALAN TIME SERIES
ARIMA Model and Neural Network are methods that was usually used for forcasting time series data. Both of them have the differences, where ARIMA model better used to predict of linear time series data, while Neural Network better used to predict of nonlinear time series data. But in real-world time...
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| Format: | Thesis |
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[Yogyakarta] : Universitas Gadjah Mada
2011
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| Summary: | ARIMA Model and Neural Network are methods that was usually used for
forcasting time series data. Both of them have the differences, where ARIMA model
better used to predict of linear time series data, while Neural Network better used to
predict of nonlinear time series data. But in real-world time series problems not only
linear or nonlinear, usually both of them (linear and nonlinear). So that created hybrid
model, called ARIMA-Neural Network Hybrid model. This model is applied to data
that contain seasonal time series. Based on the test using the Root Mean Square Error
(RMSE) and Mean Absolute Percentage Error (MAPE) shows that the error from
ARIMA-Neural Network Hybrid is smaller than a single ARIMA. This indicates that
the ARIMA model-Hybrid Neural Network is better used for forecasting than a single
ARIMA model. |
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