Peramalan Indeks Harga Konsumen dengan Metode Singular Spectral Analysis (SSA) dan Seasonal Autoregressive Integrated Moving Average (SARIMA)

Consumer Price Index (CPI) are the indicators used to measure the inflation and deflation of a group of goods and services in general. Forecasting CPI to be important as early detection in facing price hikes. This study uses the SSA and SARIMA. SARIMA a parametric model that requires various assumptions while SSA is a nonparametric technique that is free from a variety of assumptions, but both methods require seasonal patterns in the data. Based on the research results, methods of SSA with length window(L) of 24 and a grouping of 4 (1 group of seasonal and 3 groups of trends) and SARIMA models of order (0,1,1), (0,1,1) 6 is the most accurate and reliable models in forecasting CPI to the value Padang Sidempuan City. Forecasting CPI Padang Sidempuan City for the next 5 months with SSA method and SARIMA (0,1,1), (0,1,1) 6 shows the pattern of a trend is likely to increase but forecasting the 5th month with SSA method showed a surge in the value of CPI high or high inflation will occur

Implementation of Generalized Space Time Autoregressive (GSTAR)-Kriging Model for Predicting Rainfall Data at Unobserved Locations in West Java

A Generalized Space Time Autoregressive or GSTAR is a special model of Vector Autoregressive (VAR) model which is a combination of time series and spatial models which has the assumption of autoregressive parameter and space time parameter having different value for each location of observation. In addition, it assumes stationary time series data at the mean and variance levels and applies to locations with heterogeneous characteristics. One disadvantage of the GSTAR model is that it can not be used to predict at unobserved locations. In this paper we combine the GSTAR model with the Ordinary Kriging (OK) technique, named GSTAR-Kriging model so that the GSTAR model can be used to predict in unobserved locations. GSTAR parameters are estimated using the Ordinary Least Squares (OLS) method and these are used as inputs for the Kriging technique. Furthermore, by using linear semivariogram we can obtain simulations to predict the GSTAR parameters. For the case study we applied the model to annual rainfall data in wet season (Desember, January and February) from several locations in West Java, Indonesia, such as Majalengka, Kuningan and Ciamis Regencies. The GSTAR (1;1) model in observed location have Mean Average Percentage Error (MAPE) value overall less than 15 percent and residual of model have identically independent distributed normal. The results of GSTAR-Kriging model show that the GSTAR-Kriging parameter at unobserved locations are almost similar to GSTAR parameter at observed locations

Implementation of Generalized Space Time Autoregressive (GSTAR)-Kriging Model for Predicting Rainfall Data at Unobserved Locations in West Java

A Generalized Space Time Autoregressive or GSTAR is a special model of Vector Autoregressive (VAR) model which is a combination of time series and spatial models which has the assumption of autoregressive parameter and space time parameter having different value for each location of observation. In addition, it assumes stationary time series data at the mean and variance levels and applies to locations with heterogeneous characteristics. One disadvantage of the GSTAR model is that it can not be used to predict at unobserved locations. In this paper we combine the GSTAR model with the Ordinary Kriging (OK) technique, named GSTAR-Kriging model so that the GSTAR model can be used to predict in unobserved locations. GSTAR parameters are estimated using the Ordinary Least Squares (OLS) method and these are used as inputs for the Kriging technique. Furthermore, by using linear semivariogram we can obtain simulations to predict the GSTAR parameters. For the case study we applied the model to annual rainfall data in wet season (Desember, January and February) from several locations in West Java, Indonesia, such as Majalengka, Kuningan and Ciamis Regencies. The GSTAR (1;1) model in observed location have Mean Average Percentage Error (MAPE) value overall less than 15 percent and residual of model have identically independent distributed normal. The results of GSTAR-Kriging model show that the GSTAR-Kriging parameter at unobserved locations are almost similar to GSTAR parameter at observed locations