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

2017 ◽  
Vol 3 (2) ◽  
pp. 74-82
Author(s):  
Deltha Airuzsh Lubis ◽  
Muhamad Budiman Johra ◽  
Gumgum Darmawan
Keyword(s):  
Spectral Analysis ◽  
Price Index ◽  
Moving Average ◽  
Consumer Price Index ◽  
Parametric Model ◽  
Seasonal Patterns ◽  
Autoregressive Integrated Moving Average ◽  
Goods And Services ◽  
High Inflation ◽  
Sarima Models

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.

scholarly journals Pemodelan Dan Peramalan Indeks Harga Konsumen (IHK) Kota Sampit Dengan Seasonal Arima (Sarima)

2020 ◽  
Vol 6 (2) ◽  
pp. 61-72
Author(s):  
Agustina Elisa Dyah Purwandari
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Consumer Price Index ◽  
Real Income ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Goods And Services ◽  
Seasonal Factors ◽  
The Government ◽  
The Right

AbstractSampit is one of 82 cities in Indonesia which calculate inflation. Inflation is an increase of prices on goods and services in a region. Government’s control is very important because inflation relates to the real income, the exchange rate, import exports, and so on. Inflation is based on the Consumer Price Index (CPI). Because of CPI is a monthly data prices, it is highly influenced by seasonal factors. Therefore, CPI data modelling is needed because it helps the government to make appropriate policies. Method that can be used for time series data with seasonal influences is Seasonal Autoregressive Integrated Moving Average (SARIMA). The results of the study show that the right model for Sampit’s CPI is SARIMA with the order p = 1, d = 1, P = 1, D = 1, Q = 1, s = 12. It is the best model that can built and be used for forecasting because with 95 percent of confidence, the model explains 87.23 percent of data. Forecasting in this research use interval analysis and found that January 2020 may be the highest increase of CPI (inflation) in 2020. Keywords: CPI, Inflation, SARIMA

scholarly journals Time Series Modelling of Inflation in Botswana Using Monthly Consumer Price Indices

2016 ◽  
Vol 8 (3) ◽  
pp. 15
Author(s):  
Kesaobaka Molebatsi ◽  
Mpho Raboloko
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Price Index ◽  
Moving Average ◽  
Consumer Price Index ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Integrated Moving Average ◽  
Time Series Modelling ◽  
Error Terms ◽  
Autoregressive Conditional Heteroscedasticity

<p>This paper identifies an autoregressive integrated moving average (ARIMA (1,1,1)) model that can be used to model inflation measured by the consumer price index (CPI) for Botswana. The paper proceeds to improve the model by incorporating the generalized autoregressive conditional heteroscedasticity (ARCH/GARCH) model that takes into consideration volatility in the series. Ultimately, CPI is forecast using the two models, ARIMA (1, 1, 1) and ARIMA (1, 1, 1) + GARCH (1, 2) and compared with the actual CPI. Both models perform well in terms of forecasting as their 95 percent confidence intervals cover the actual CPI. Marginal differences that favour the inclusion of the ARCH/GARCH components were observed when testing for normality among error terms. The paper also reveals that volatility for Botswana’s CPI is low as shown by small values of ARCH/GARCH components.</p>

scholarly journals Forecasting Commodity Price Index of Food and Beverages in Kenya Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Models

2021 ◽  
Vol 2 (6) ◽  
pp. 50-63
Author(s):  
Teddy Mutugi Wanjuki ◽  
Adolphus Wagala ◽  
Dennis K. Muriithi
Keyword(s):  
Monetary Policy ◽  
Fiscal Policy ◽  
Price Index ◽  
Moving Average ◽  
Estimation Method ◽  
Series Data ◽  
Policy Objective ◽  
Autoregressive Integrated Moving Average ◽  
Food And Beverages ◽  
Sarima Models

Price stability is the primary monetary policy objective in any economy since it protects the interests of both consumers and producers. As a result, forecasting is a common practice and a vital aspect of monetary policymaking. Future predictions guide monetary and fiscal policy tools that that be used to stabilize commodity prices. As a result, developing an accurate and precise forecasting model is critical. The current study fitted and forecasted the food and beverages price index (FBPI) in Kenya using seasonal autoregressive integrated moving average (SARIMA) models. Unlike other ARIMA models like the autoregressive (AR), Moving Average (MA), and non-seasonal ARMA models, the SARIMA model accounts for the seasonal component in a given time series data better forecasts. The study relied on secondary data obtained from the KNBS website on monthly food and beverage price index in Kenya from January 1991 to February 2020. R-statistical software was used to analyze the data. The parameter estimation was done using the Maximum Likelihood Estimation method. Competing SARIMA models were compared using the Mean Absolute Error (MAE), Mean Absolute Scaled Error (MASE),.and Mean Absolute Percentage Error (MAPE). A first-order differenced SARIMA (1,1,1) (0,1,1)12 minimized these model evaluation criteria (AIC = 1818.15, BIC =1833.40). The forecasting ability evaluation statistics MAE = 2.00%, MAPE = 1.62% and MASE = 0.87%. The 24-step ahead forecasts showed that the FPBI is unstable with an overall increasing trend. Therefore, the monetary policy committee ought to control inflation through monetary or fiscal policy, strengthening food security and trade liberalization.

scholarly journals THE LONG-RUN RELATIONSHIP BETWEEN HOUSE PRICES AND INFLATION IN SOUTH AFRICA: AN ARDL APPROACH

2013 ◽  
Vol 17 (2) ◽  
pp. 188-198 ◽  
Author(s):  
Roula Inglesi-Lotz ◽  
Rangan Gupta
Keyword(s):  
South Africa ◽  
Price Index ◽  
House Prices ◽  
Housing Prices ◽  
Consumer Price Index ◽  
Data Series ◽  
Middle Segment ◽  
Goods And Services ◽  
Long Run ◽  
Housing Costs

This paper investigates whether house prices provide a suitable hedge against inflation in South Africa by analysing the long-run relationship between house prices and the prices of non-housing goods and services. Quarterly data series are collected for the luxury, large middle-segment, medium middle-segment, small middle-segment and the entire middle segment of house prices, as well as, the consumer price index excluding housing costs for the period 1970:Q1–2011:Q1. Based on autoregressive distributed lag (ARDL) models, the empirical results indicate long-run cointegration between the house prices of all the segments and the consumer price index excluding housing costs. Moreover, the long-run elasticity of house prices with respect to prices of non-housing goods and services, i.e., the Fisher coefficient is greater than one for the luxury segment, virtually equal to one for the small middle-segment, and less than one for the large and medium middle-segments, as well as the affordable segments. More importantly though, the estimated Fisher coefficients are not statistically different from unity – a result consistent with the proposed theoretical framework relating housing prices and consumer prices excluding housing expenditure. In general, we infer that house prices in South Africa provide a stable inflation hedge in the long-run.

scholarly journals Forecasting wholesale price of coarse rice in Bangladesh: A seasonal autoregressive integrated moving average approach

2014 ◽  
Vol 11 (2) ◽  
pp. 271-276
Author(s):  
MF Hassan ◽  
MA Islam ◽  
MF Imam ◽  
SM Sayem
Keyword(s):  
Moving Average ◽  
Wholesale Price ◽  
Information Criteria ◽  
Suitable Model ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Lag Times ◽  
Predicted Values ◽  
Absolute Percent Error ◽  
Sarima Models

This article attempts to develop the model and to forecast the wholesale price of coarse rice in Bangladesh. Seasonal Autoregressive Integrated Moving Average (SARIMA) models have been developed on the monthly data collected from July 1975 to December 2011and validated using the data from December 2010 to December 2011. The results showed that the predicted values were consistent with the upturns and downturns of the observed series. The model with non seasonal autoregressive 1, difference 1 and moving average 1 and seasonal difference 1 and moving average 1 that is SARIMA (1,1,1)(0,1,1)12 model has been found as the most suitable model with least Root Mean Square Error (RMSE) of 61.657, Normalised Bayesian Information Criteria (BIC) of 8.300 and Mean Absolute Percent Error (MAPE) of 3.906. The model was further validated by Ljung-Box test (Q18=17.394 and p>.20) with no significant autocorrelation between residuals at different lag times. Finally, a forecast for the period January 2012 to December 2013 was made. DOI: http://dx.doi.org/10.3329/jbau.v11i2.19925 J. Bangladesh Agril. Univ. 11(2): 271-276, 2013

scholarly journals Analisis Perkembangan Indeks Harga Konsumen di Kota Medan

2020 ◽  
Vol 3 (2) ◽  
pp. 412-418
Author(s):  
Sari Wulandari ◽  
Muhammad Dani Habra
Keyword(s):  
Price Index ◽  
Inflation Rate ◽  
Consumer Price Index ◽  
Commodity Prices ◽  
Household Expenditure ◽  
The Public ◽  
Goods And Services ◽  
City Community ◽  
The Subject ◽  
The City

The Consumer Price Index (CPI) is one of the important economic indicators that can provide information about the development of prices of goods and services (commodities) paid by consumers or the public especially the city community. This study aims to analyze the Development of the Consumer Price Index in Medan City. The benefits of this research are a description of the fluctuations in commodity prices for basic needs of the community at the level of consumers or retail traders. This type of research is descriptive qualitative. The subject in this study is the Central Statistics Agency and the object in this study is the Consumer Price Index through seven groups of household expenditure in 2018-2019. The results showed that the development of price indices in Medan City tends to fluctuate from seven types of household expenditure groups. During the January-December 2019 period the highest inflation of the seven types of expenditure was foodstuffs. Keywords: Consumer Price Index, Inflation Rate

scholarly journals PERAMALAN LAJU INFLASI DENGAN METODE AUTO REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA)

2017 ◽  
Vol 14 (4) ◽  
pp. 524 ◽  
Author(s):  
Djawoto Djawoto
Keyword(s):  
Time Series ◽  
Price Index ◽  
Inflation Rate ◽  
Moving Average ◽  
Arima Model ◽  
Consumer Price Index ◽  
Stationary Time Series ◽  
Stationary Time ◽  
Auto Regressive ◽  
Auto Regression

Auto Regression Integrated Moving Average (ARIMA) or the combination model of Auto Regression with moving average, is a linier model which is able to represent the stationary time series or non stationary time series. The purpose of this research is to forecast the inflation rate in November 2010 with the Consumer Price Index (CPI) by using ARIMA. The inflation indicator is very important to anticipate in making the Government’s policy and decision as well as for the citizen is for the information to determine what to do in related with savings and investment. By looking at the existing criteria, it is determined that the best model is ARIMA (1,1,0) or AR (1). Model ARIMA (1,1,0), the coefficient value AR (1) is significant,which has the most minimum value of Akaike Info Criterion (AIC) and Schwars Criterion (SC) compare toARIMA (0,1,1) or MA (1) and ARIMA (1,1,1) or AR (1) MA (1). In summarize, the ARIMA model used to forecast the valueof IHK is ARIMA (1,1,0).

scholarly journals Analysis of the demand for gastronomic tourism in Andalusia (Spain)

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246377
Author(s):  
Mª Genoveva Dancausa Millán ◽  
Mª Genoveva Millán Vázquez de la Torre ◽  
Ricardo Hernández Rojas
Keyword(s):  
Olive Oil ◽  
Raw Materials ◽  
Moving Average ◽  
Food Products ◽  
Southern Spain ◽  
Autoregressive Integrated Moving Average ◽  
Wine Tourism ◽  
Protected Designations Of Origin ◽  
Sarima Models

In recent years, gastronomy has become a fundamental motivation to travel. Learning how to prepare gastronomic dishes and about the raw materials that compose them has attracted increasing numbers of tourists. In Andalusia (region of southern Spain), there are many quality products endorsed by Protected Designations of Origin, around which gastronomic routes have been created, some visited often (e.g., wine) and others remaining unknown (e.g., ham and oil). This study analyses the profile of gastronomic tourists in Andalusia to understand their motivations and estimates the demand for gastronomic tourism using seasonal autoregressive integrated moving average (SARIMA) models. The results obtained indicate that the gastronomic tourist in Andalusia is very satisfied with the places he/she visits and the gastronomy he/she savours. However, the demand for this tourist sector is very low and heterogeneous; while wine tourism is well established, tourism focusing on certain products, such as olive oil or ham, is practically non-existent. To obtain a homogeneous demand, synergies or pairings should be created between food products, e.g., wine-ham, oil-ham, etc., to attract a greater number of tourists and distinguish Andalusia as a gastronomic holiday destination.

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