PERAMALAN DENGAN METODE SARIMA PADA DATA INFLASI DAN IDENTIFIKASI TIPE OUTLIER (Studi Kasus: Data Inflasi Indonesia Tahun 2008-2014)

Inflation is defined as rising prices of goods in general and continuously. The effect inflation on the economy can cause currency to decline, resulting country's economic power becoming weak. Time series data arranged order time or collected over time. Changes rate tend make unstable affect forecasting process data. method used this study seasonal autoregressive integrated moving (SARIMA) predict one two periods ahead. This also outlier identifiers models that still have tendencies residuals. results SARIMA become inaccurate when residual contains outliers. presence residuals not a normal distribution. obtained best model results, namely (0,1,1) (0,1,1)12 with forecast value for January May 2015 range 5-6 %. On (1,1,1)12 (1,1,0) (2,1,0)12 outliers are detected Additive Outlier (AO) Temporary Change (TC) type.