Analysis and Forecast of Car Sales Based on R Language Time Series

2020 ◽  
pp. 197-207
Author(s):  
Yuxin Zhao
Keyword(s):  
Time Series ◽  
R Language ◽  
Car Sales

Time Series Analysis of New Cars Sale in Bosnia and Herzegovina

2015 ◽  
Vol 806 ◽  
pp. 287-293
Author(s):  
Esad Jakupović ◽  
Vladimir Stojanović ◽  
Sanel Jakupović ◽  
Dragana Trnavac
Keyword(s):  
Time Series ◽  
Bosnia And Herzegovina ◽  
Polynomial Regression ◽  
Growth Index ◽  
Coefficient Of Determination ◽  
Chain Growth ◽  
Degree Polynomial ◽  
Car Sales ◽  
The Given ◽  
Polynomial Regression Model

The paper provides analysis of new car sales in Bosnia and Herzegovina for the period 2007-2014. For the examined period new car sales in B&H was reduced by 53.67%, from 12449 in 2007 to 6682 in 2014. The trend can be approximated using 3rd degree polynomial regression model with coefficient of determination R2=0,845. Most new cars sold were by Skoda and least was sold by Porsche. Total number of sold vehicles for this period was 73152. We also present annual growth, chain growth and cumulative growth index for the given period.

scholarly journals R Language: Statistical Computing and Graphics for Modeling Hydrologic Time Series

2014 ◽  
Vol 10 (4) ◽  
pp. 9-18
Author(s):  
Gabriela-Roxana Dobre
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Statistical Model ◽  
Graphical Representation ◽  
Statistical Computing ◽  
R Language ◽  
Hydrologic Time Series ◽  
Time Period ◽  
Precipitation And Temperature ◽  
Representation Of Time

Abstract The analysis and management of Hydrology time series is used for the development of models that allow predictions on future evolutions. After identifying the trends and the seasonal components, a residual analysis can be done to correlate them and make a prediction based on a statistical model. Programming language R contains multiple packages for time series analysis: ‘hydroTSM’ package is adapted to the time series used in Hydrology, package ‘TSA’ is used for general interpolation and statistical analysis, while the ‘forecast’ package includes exponential smoothing, all having outstanding capabilities in the graphical representation of time series. The purpose of this paper is to present some applications in which we use time series of precipitation and temperature from Fagaras in the time period 1966-1982. The data was analyzed and modeled by using the R language.

scholarly journals PREDIKSI RETAIL SALES MOBIL TOYOTA MENGGUNAKAN METODE ARIMA (AUTOREGRESSIVE INTEGRATED MOVING AVERAGE)

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Adri Arisena
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Moving Average ◽  
Retail Sales ◽  
Autoregressive Integrated Moving Average ◽  
Automobile Manufacturing ◽  
Automobile Factory ◽  
Sales Prediction ◽  
Car Sales

AbstrakPenjualan mobil di Indonesia saat ini terus meningkat. Untuk melakukan perencanaan yang baik, perusahaan membutuhkan sebuah nilai prediksi penjualan agar dapat menentukan target penjualan mobil. Salah satu model prediksi yang sering digunakan untuk memprediksi sebuah data yaitu metode ARIMA (Autoregressive Integrated Moving Average). Dalam penelitian ini data yang digunakan yaitu data Retail Sales mobil Toyota dari bulan Januari 2017 sampai dengan Maret 2020. Retail Sales merupakan penjualan mobil dari dealer kepada konsumen sehingga diharapkan pabrik mobil Toyota memiliki prencanaan terhadap produksi mobil sehingga menjadi efektif dan efisien.Kata Kunci : ARIMA, time series, Toyota, MobilAbstrackCar sales in Indonesia are growing. To do good planning, the company needs a sales prediction value in order to determine the car sales target. One prediction model that is often used to predict a data is the method of ARIMA (Autoregressive Integrated Moving Average). In this study the data used is the Toyota Sales Retail data from January 2017 to March 2020. Retail Sales is a car sales from dealers to consumers, so it is hoped that the Toyota automobile factory has a preproduction to the automobile manufacturing so that it becomes effective and efficient.Keywords : ARIMA, time series, Toyota, Car

scholarly journals Multistep Forecasting for Highly Volatile Data using A New Box-Jenkins and GARCH Procedure

2020 ◽  
pp. 1-6
Author(s):  
Siti Roslindar Yaziz ◽  
Roslinazairimah Zakaria ◽  
John Boland
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Gold Price ◽  
Series Data ◽  
R Language ◽  
Price Series ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
One Step ◽  
Multiple Step

The study of the multistep ahead forecast is significant for practical application purposes using the proposed statistical model. This study proposes a new procedure of Box-Jenkins and GARCH (or BJ-G) in evaluating the multistep forecasting performance for a highly volatile time series data. The promising results from one-step ahead out-of-sample forecast series using the BJ-G model has motivated the extension to multiple step ahead forecast. In order to achieve the objective, the procedure of multistep ahead forecast for BJ-G model is proposed using R language. In evaluating the performance of the multistep ahead forecast, the proposed procedure is employed to daily world gold price series of 5-year data. Based on the empirical results, the proposed procedure of multistep ahead forecast enhances the existing procedure of BJ-G which is able to provide a promising procedure to assess the performance of the BJ-G model in forecasting a highly volatile time series data. The procedure adds the value of BJ-G model since it allows the model to describe efficiently the characteristics of the volatile series up to n-step ahead forecast. Keywords: Box-Jenkins, GARCH, highly volatile data, multistep forecast; gold price

The LDE-Type Flare Occurrence (1969-1993)

1994 ◽  
Vol 144 ◽  
pp. 279-282
Author(s):  
A. Antalová
Keyword(s):  
Time Series ◽  
Fourier Analysis ◽  
Sunspot Cycle ◽  
List Type ◽  
Type Number ◽  
Solar Cycles ◽  
Type Iv ◽  
Long Duration ◽  
Cycle Maximum ◽  
Flare Index

AbstractThe occurrence of LDE-type flares in the last three cycles has been investigated. The Fourier analysis spectrum was calculated for the time series of the LDE-type flare occurrence during the 20-th, the 21-st and the rising part of the 22-nd cycle. LDE-type flares (Long Duration Events in SXR) are associated with the interplanetary protons (SEP and STIP as well), energized coronal archs and radio type IV emission. Generally, in all the cycles considered, LDE-type flares mainly originated during a 6-year interval of the respective cycle (2 years before and 4 years after the sunspot cycle maximum). The following significant periodicities were found:• in the 20-th cycle: 1.4, 2.1, 2.9, 4.0, 10.7 and 54.2 of month,• in the 21-st cycle: 1.2, 1.6, 2.8, 4.9, 7.8 and 44.5 of month,• in the 22-nd cycle, till March 1992: 1.4, 1.8, 2.4, 7.2, 8.7, 11.8 and 29.1 of month,• in all interval (1969-1992):a)the longer periodicities: 232.1, 121.1 (the dominant at 10.1 of year), 80.7, 61.9 and 25.6 of month,b)the shorter periodicities: 4.7, 5.0, 6.8, 7.9, 9.1, 15.8 and 20.4 of month.Fourier analysis of the LDE-type flare index (FI) yields significant peaks at 2.3 - 2.9 months and 4.2 - 4.9 months. These short periodicities correspond remarkably in the all three last solar cycles. The larger periodicities are different in respective cycles.

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