ANALISA DEBIT BANGKITAN MENGGUNAKAN MODEL ARIMA (AUTOREGRESIF INTEGRATED MOVING AVERAGE)

Flow discharge data must be available in a time series and accurate manner, so there should no empty periods. Therefore, model is needed that can reconstruct or estimate the flow of period stochastically. One way to solve this problem by filling generation.The philosophy create new sets based on generally incomplete short historical obtain longer more complete data. The long made with properties as well source (Sri Harto Sudjarwadi, 1989). Model ARIMA represents three modeling namely autoregressive (AR), moving average (MA), (ARMA) which has characteristic two models. First stage testing stationary data, identification model, estimation parameter forecasting. Data used Arima station Maribaya DAS Cikapundung Hulu if from years 1978 research. Results produces correlation values 0.657 target value 1. For absolute relative error rate (KAR) (RMS) each 0.0052 0.017 0. fill in, generate predict future rates. In forecasting, only able accurately span. long-term resulting forecast will tend flat (flat/constant).