Consumer Price Index Forecasting Based on Univariate Time Series and a Deep Neural Network

2021 ◽  
pp. 33-42
Author(s):  
Reynaldo Rosado ◽  
Aldis Joan Abreu ◽  
José C. Arencibia ◽  
Hector Gonzalez ◽  
Yanio Hernandez
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Neural Network ◽  
Price Index ◽  
Consumer Price Index ◽  
Univariate Time Series

scholarly journals A forecasting the consumer price index using time series model

2021 ◽  
Vol 107 ◽  
pp. 10002
Author(s):  
Volodymyr Shinkarenko ◽  
Alexey Hostryk ◽  
Larysa Shynkarenko ◽  
Leonid Dolinskyi
Keyword(s):  
Time Series ◽  
Price Index ◽  
Consumer Price Index ◽  
Exponential Smoothing ◽  
Time Series Model ◽  
Initial Time ◽  
Autocorrelation Functions ◽  
Minimum Error ◽  
Initial Time Series

This article examines the behavior of the consumer price index in Ukraine for the period from January 2010 to September 2020. The characteristics of the initial time series, the analysis of autocorrelation functions made it possible to reveal the tendency of their development and the presence of annual seasonality. To model the behavior of the consumer price index and forecast for the next months, two types of models were used: the additive ARIMA*ARIMAS model, better known as the model of Box-Jenkins and the exponential smoothing model with the seasonality estimate of Holt-Winters. As a result of using the STATISTICA package, the most adequate models were built, reflecting the monthly dynamics of the consumer price index in Ukraine. The inflation forecast was carried out on the basis of the Holt-Winters model, which has a minimum error.

The Salaries of Officials and Officers in the Russian Empire

2021 ◽  
Vol 47 (3) ◽  
pp. 224-237
Author(s):  
Boris N. Mironov ◽  
Jan Surer
Keyword(s):  
Time Series ◽  
Price Index ◽  
Consumer Price Index ◽  
Russian Empire ◽  
Officer Corps ◽  
The Real ◽  
Regulatory Documents ◽  
The Russian Empire

Abstract This article analyzes changes in both the nominal and real salaries of Russian officials and officers. The study draws upon data concerning provincial administrations, which employed a significant portion of officials, and the infantry, in which most of the officer corps served, from the introduction of monetary salaries in 1763 (for officials) and in 1711 (for officers) to 1913. A table of the changes in nominal salaries was compiled from legislative and regulatory documents, and, with the use of a consumer price index constructed by the author, time series of the real salaries of officials and officers of various ranks were obtained by decades over 150 years.

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).

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