scholarly journals Forecasting the Existence of Chocolate with Variation and Seasonal Calendar Effects Using the Classic Time Series Approach

2022 ◽  
Vol 18 (2) ◽  
pp. 237-250
Author(s):  
I Gusti Bagus Ngurah Diksa
Keyword(s):  
United States ◽  
Time Series ◽  
The United States ◽  
Raw Material ◽  
Time Series Regression ◽  
Calendar Effects ◽  
Double Exponential ◽  
Time Series Approach ◽  
Double Exponential Smoothing ◽  
Chocolate Cake

Chocolate is the raw material for making cakes, so consumption of chocolate also increases on Eid al-Fitr. However, this is different in the United States where the tradition of sharing chocolate cake is carried out on Christmas. To monitor the existence of this chocolate can be through the movement of data on Google Trends. This study aims to predict the existence of chocolate from the Google trend where the use of chocolate by the community fluctuates according to the calendar variance and seasonal rhythm. The method used is classic time series, namely nave, double exponential smoothing, multiplicative decomposition, addictive decomposition, holt winter multiplicative, holt winter addictive, time series regression, hybrid time series, ARIMA, and ARIMAX. Based on MAPE in sample, the best time series model to model the existence of chocolate in Indonesia is ARIMAX (1,0,0) while for the United States it is Hybrid Time Series Regression-ARIMA(2,1,[10]). For forecasting the existence of chocolate in Indonesia, the best models in forecasting are ARIMA (([11],[12]),1,1) and Naïve Seasonal. In contrast to the best forecasting model for the existence of chocolate in the United States, namely Hybrid Naïve Seasonal-SARIMA (2,1,0)(0,0,1)12 Hybrid Time Series Regression- ARIMA(2,1,[10]), Time Series Regression, Winter Multiplicative, ARIMAX([3],0,0).  

scholarly journals Application of the Autoregressive Integrated Moving Average Exogenous (ARIMAX) with Calendar Variation Effect Method for Forecasting Chocolate Data in Indonesia and the United States

2022 ◽  
Vol 18 (2) ◽  
pp. 224-236
Author(s):  
Andy Rezky Pratama Syam
Keyword(s):  
United States ◽  
Time Series ◽  
Moving Average ◽  
The United States ◽  
Time Series Regression ◽  
Autoregressive Integrated Moving Average ◽  
Good Ability ◽  
Modeling And Forecasting ◽  
Time Series Method ◽  
Double Exponential Smoothing

Forecasting chocolate consumption is required by producers in preparing the amount of production each month. The tradition of Valentine, Christmas and Eid al-Fitr which are closely related to chocolate makes it impossible to predict chocolate by using the Classical Time Series method. Especially for Eid al-Fitr, the determination follows the Hijri calendar and each year advances 10 days on the Masehi calendar, so that every three years Eid al-Fitr will occur in a different month. Based on this, the chocolate forecasting will show a variation calendar effect. The method used in modeling and forecasting chocolate in Indonesia and the United States is the ARIMAX (Autoregressive Integrated Moving Average Exogenous) method with Calendar Variation effect. As a comparison, modeling and forecasting are also carried out using the Naïve Trend Linear, Naïve Trend Exponential, Double Exponential Smoothing, Time Series Regression, and ARIMA methods. The ARIMAX method with Calendar Variation Effect produces a very precise MAPE value in predicting chocolate data in Indonesia and the United States. The resulting MAPE value is below 10 percent, so it can be concluded that this method has a very good ability in forecasting.

scholarly journals Impact of economic conditions on (restricted) immigration to the United States: The Polish case

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Michał Schwabe
Keyword(s):  
United States ◽  
Time Series ◽  
Unemployment Rate ◽  
Ethnic Diversity ◽  
The United States ◽  
Time Series Regression ◽  
Polish Migration ◽  
Macroeconomic Indicators ◽  
Migration Flows ◽  
The U.S

Abstract Throughout the twentieth century, United States has been the most desirable destination for international migrants, primarily due to its economic performance and also to American values – work ethics and tolerance of ethnic diversity. This paper aims to test if selected economic indicators might influence international migration. To this end a time series analysis was performed with time series regression model, where lagged values of various macroeconomic indicators were tested for a significant impact on migration flows. This paper also cast a light on U.S. labour migration's legislation and history, as well as current migrant stock characteristics. It gives specific attention to Polish migrant population, as Polish Americans constitute the largest Polish diaspora worldwide. The results of the analysis show that U.S. immigration volumes are sensitive to American unemployment rate and American GDP growth (pull factors). However, analysing Polish migration volumes to the U.S. a significant correlation with selected American indicators was not revealed. On the contrary, Polish migration flows to the U.S. were correlated with Polish economic growth and the Polish unemployment rate fluctuations (push factors).

scholarly journals Forecasting COVID-19 Cases in Indonesia Using Hybrid Double Exponential Smoothing

2021 ◽  
Vol 1 (2) ◽  
pp. 53-57
Author(s):  
Mujiati Dwi Kartikasari
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Upward Trend ◽  
Double Exponential ◽  
The World ◽  
Trend Data ◽  
Time Series Approach ◽  
Double Exponential Smoothing

The COVID-19 epidemic has spread throughout countries around the world. In Indonesia, this case was detected in early March 2020, and until now, there is still an increase in positive cases of COVID-19. The purpose of this paper is to predict COVID-19 cases in Indonesia using a time series approach. The method used is H-WEMA method because this method can capture trend data patterns following the conditions of COVID-19 cases in Indonesia. Based on the analysis results, H-WEMA can predict COVID-19 cases very well. The forecasted results of the COVID-19 cases in Indonesia still have an upward trend, so it needs the cooperation of all elements of community to reduce the spread of COVID-19. Received September 8, 2021Revised October 15, 2021Accepted November 3, 2021  

American And British Political Business Cycles: A Time Series Approach

2011 ◽  
Vol 8 (2) ◽  
pp. 36
Author(s):  
Ronald S. Koot ◽  
J. Keith Ord ◽  
Peg Young
Keyword(s):  
United States ◽  
Time Series ◽  
Business Cycles ◽  
The United States ◽  
The Political ◽  
Intervention Analysis ◽  
Political Business Cycles ◽  
The United Kingdom ◽  
Time Series Approach ◽  
Political Intervention

Intervention analysis was employed to determine the existence of political business cycles in the United States and the United Kingdom; the two economic variables tested were unemployment and disposable personal income. The political intervention variables were the party in power, the timing of the elections, the incumbent running for re-election, and the existence of a state of war. The technique proved successful at showing significant interventions.

scholarly journals Time-series analysis for porcine reproductive and respiratory syndrome in the United States

PLoS ONE ◽  
2018 ◽  
Vol 13 (4) ◽  
pp. e0195282 ◽  
Author(s):  
Andréia Gonçalves Arruda ◽  
Carles Vilalta ◽  
Pere Puig ◽  
Andres Perez ◽  
Anna Alba
Keyword(s):  
United States ◽  
Time Series ◽  
Time Series Analysis ◽  
The United States ◽  
Series Analysis
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