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 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 ARIMA Time Series Models for Full Truckload Transportation Prices

Forecasting ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 121-134 ◽  
Author(s):  
Jason W. Miller
Keyword(s):  
Time Series ◽  
Moving Average ◽  
The United States ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Estimated Parameters ◽  
Coefficients Of Variation ◽  
Labor Statistics ◽  
Bureau Of Labor Statistics ◽  
Practical Implications

The trucking sector in the United States is a $700 billion plus a year industry and represents a large percentage of many firms’ logistics spend. Consequently, there is interest in accurately forecasting prices for truck transportation. This manuscript utilizes the autoregressive integrated moving average (ARIMA) methodology to develop forecasts for three time series of monthly archival trucking prices obtained from two public sources—the Bureau of Labor Statistics (BLS) and Truckstop.com. BLS data cover January 2005 through August 2018; Truckstop.com data cover January 2015 through August 2018. Different ARIMA models closely approximate the observed data, with coefficients of variation of the root mean-square deviations being 0.007, 0.040, and 0.048. Furthermore, the estimated parameters map well onto dynamics known to operate in the industry, especially for data collected by the BLS. Theoretical and practical implications of these findings are discussed.

scholarly journals COVID-19 pandemic control using restrictions and vaccination

2021 ◽  
Vol 19 (2) ◽  
pp. 1355-1372
Author(s):  
Vinicius Piccirillo ◽  
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Real Data ◽  
The United States ◽  
Prevention Policy ◽  
Autoregressive Integrated Moving Average ◽  
Infected People ◽  
Marquardt Algorithm ◽  
The Impact ◽  
Methods Numerical

<abstract><p>This work deals with the impact of the vaccination in combination with a restriction parameter that represents non-pharmaceutical interventions measures applied to the compartmental SEIR model in order to control the COVID-19 epidemic. This restriction parameter is used as a control parameter, and the univariate autoregressive integrated moving average (ARIMA) is used to forecast the time series of vaccination of all individuals of a specific country. Having in hand the time series of the population fully vaccinated (real data + forecast), the Levenberg–Marquardt algorithm is used to fit an analytic function that models this evolution over time. Here, it is used two time series of real data that refer to a slow vaccination obtained from India and Brazil, and two faster vaccination as observed in Israel and the United States of America. Together with vaccination, two different control approaches are presented in this paper, which enable reduces the infected people successfully: namely, the feedback and nonfeedback control methods. Numerical results predict that vaccination can reduce the peaks of infections and the duration of the pandemic, however, a better result is achieved when the vaccination is combined with any restrictions or prevention policy.</p></abstract>

scholarly journals Peramalan Banyak Pengunjung Pantai Pandasimo Bantul Menggunakan Regresi Runtun Waktu dan Seasonal Autoregressive Integrated Moving Average Exogenous

2021 ◽  
Vol 4 (1) ◽  
pp. 57
Author(s):  
Tito Tatag Prakoso ◽  
Etik Zukhronah ◽  
Hasih Pratiwi
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Seasonal Pattern ◽  
Series Data ◽  
Gregorian Calendar ◽  
Time Series Regression ◽  
Autoregressive Integrated Moving Average ◽  
Past Data

<p>Forecasting is a ways to predict what will happen in the future based on the data in the past. Data on the number of visitors in Pandansimo beach are time series data. The pattern of the number of visitors in Pandansimo beach is influenced by holidays, so it looks like having a seasonal pattern. The majority of Indonesian citizens are Muslim who celebrate Eid Al-Fitr in every year. The determination of Eid Al-Fitr does not follow the Gregorian calendar, but based on the Lunar calendar. The variation of the calendar is about the determination of Eid Al-Fitr which usually changed in the Gregorian calendar, because in the Gregorian calendar, Eid Al-Fitr day will advance one month in every three years. Data that contain seasonal and calendar variations can be analyzed using time series regression and Seasonal Autoregressive Integrated Moving Average Exogenous  (SARIMAX) models. The aims of this study are to obtain a better model between time series regression and SARIMAX and to forecast the number of Pandansimo beach visitors using a better model. The result of this study indicates that the time series regression model is a better model. The forecasting from January to December 2018 in succession are 13255, 6674, 8643, 7639, 13255, 8713, 22635, 13255, 13255, 9590, 8549, 13255 visitors.</p><strong>Keywords: </strong>time series regression, seasonal, calendar variations, SARIMAX, forecasting

Suicide mortality in the United States following the suicides of Kate Spade and Anthony Bourdain

2020 ◽  
pp. 000486742097684
Author(s):  
Mark Sinyor ◽  
Ulrich S Tran ◽  
David Garcia ◽  
Benedikt Till ◽  
Martin Voracek ◽  
...  
Keyword(s):  
United States ◽  
Confidence Interval ◽  
Natural Experiment ◽  
Moving Average ◽  
The United States ◽  
Suicide Mortality ◽  
Autoregressive Integrated Moving Average ◽  
Public Health Emergencies ◽  
Moving Average Models ◽  
Celebrity Suicide

Objective: The suicides of Kate Spade and Anthony Bourdain, two major American icons, in a span of days in June 2018 represent a unique and tragic natural experiment to characterize associations with actual suicides in the aftermath of celebrity suicides. The aim of this study was to identify changes in suicide counts after their deaths. Methods: Suicide data were obtained from the United States’ Centers for Disease Control and Prevention’s public-use mortality file. A time-series analysis was performed, examining monthly suicide data by age group (⩽19, 20–44, 45–64 and ⩾65 years), for both men and women, for all suicide methods and for hanging versus non-hanging methods, from January 1999 to December 2018. Seasonal autoregressive integrated moving-average models were fitted to the pre-June 2018 period, estimating suicides in subsequent months and identifying deviations from expected values. The volume of Twitter posts about Kate Spade and Anthony Bourdain was used as a proxy of societal attention. Results: Tweets about the celebrities were mainly concentrated in June 2018 and faded quickly in July. Total suicides exceeded the 95% confidence interval for June and approximated the upper limit of the 95% confidence interval in July. Over this 2-month span, there were 418 (95% confidence interval = [184, 652]) more suicides than expected, including 275 (95% confidence interval = [79, 471]) excess suicides in men and 182 (95% confidence interval = [93, 271]) in women. These equate to 4.8%, 4.1% and 9.1% increases above expected counts. There were 392 (95% confidence interval = [271, 514]) excess suicides by hanging, a 14.5% increase, with no significant increase in all other methods combined. Conclusion and Relevance: These findings demonstrate that mortality following celebrity suicides can occur at a similar magnitude to that observed for other public health emergencies. They underscore the urgency for interventions to mitigate imitation effects after celebrity suicide reporting.

scholarly journals PERAMALAN PERSEDIAAN INFUS MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) PADA RUMAH SAKIT UMUM PUSAT SANGLAH

2018 ◽  
Vol 7 (2) ◽  
pp. 129
Author(s):  
I PUTU YUDI PRABHADIKA ◽  
NI KETUT TARI TASTRAWATI ◽  
LUH PUTU IDA HARINI
Keyword(s):  
Time Series ◽  
General Hospital ◽  
Moving Average ◽  
Glucose Infusion ◽  
Series Method ◽  
Autoregressive Integrated Moving Average ◽  
Important Thing ◽  
Forecasting Method ◽  
Time Series Method ◽  
The Many

Infusion supplies are an important thing that must be considered by the hospital in meeting the needs of patients. This study aims to predict the need for infusion of 0.9% 500 ml of NaCl and 5% 500 ml glucose infusion at Sanglah General Hospital (RSUP) Sanglah so that the hospital can estimate the many infusions needed for the next six months. The forecasting method used in this research is the autoregressive integrated moving average (ARIMA) time series method. The results of this study indicate the need for infusion at Sanglah Hospital as many as 154,831 units for infusion of 0.9% NaCl 500 ml and 8,249 units for 5% 500 ml Glucose infusion.

scholarly journals Forecasting Stock Price PT. Telkom Using Hybrid Time Series Regression Linear– Autoregressive Integrated Moving Average Model

2022 ◽  
Vol 18 (2) ◽  
pp. 293-307
Author(s):  
Kartika Ramadani ◽  
Sri Wahyuningsih ◽  
Memi Nor Hayati
Keyword(s):  
Time Series ◽  
Linear Model ◽  
Stock Price ◽  
Moving Average ◽  
Arima Model ◽  
Residual Value ◽  
Time Series Regression ◽  
Autoregressive Integrated Moving Average ◽  
Trend Pattern ◽  
Closing Price

The hybrid method is a method of combining two forecasting models. Hybrid method is used to improve forecasting accuracy. In this study, the Time Series Regression (TSR) linear model will be combined with the Autoregressive Integrated Moving Average (ARIMA) model. The TSR linear model is used to obtain the model and residual value, then the residual value of the TSR linear model will be modeled by the ARIMA model. This combination method will produce a hybrid TSR linear-ARIMA model. The case study in this research is stock closing price (daily) of PT. Telkom Indonesia Tbk. The stock closing price (daily) of PT. Telkom Indonesia Tbk in 2020 showed an decreasing and increasing trend pattern. The results of this study obtained the best model of hybrid TSR linear-ARIMA (2,1,1) with the proportion of data training and testing is 70:30. In the best model, the MAD value is 56.595, the MAPE value is 1.880%, and the RMSE value is 78.663. It is also found that the hybrid TSR linear-ARIMA model has a smaller error value than the TSR linear model. The results of forecasting the stock price of PT. Telkom Indonesia Tbk for the period 02 January 2021 to 29 January 2021 formed a decreasing trend pattern.

scholarly journals Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving Average (ARIMA) Model (Preprint)

2020 ◽  
Author(s):  
Ram Kumar Singh ◽  
Meenu Rani ◽  
Akshaya Srikanth Bhagavathula ◽  
Ranjit Sah ◽  
Alfonso J Rodriguez-Morales ◽  
...  
Keyword(s):  
United States ◽  
United Kingdom ◽  
Death Rate ◽  
Rate Ratio ◽  
Moving Average ◽  
Arima Model ◽  
The United States ◽  
Recovery Ratio ◽  
Autoregressive Integrated Moving Average ◽  
The United Kingdom

BACKGROUND The coronavirus disease (COVID-19) pandemic has affected more than 200 countries and has infected more than 2,800,000 people as of April 24, 2020. It was first identified in Wuhan City in China in December 2019. OBJECTIVE The aim of this study is to identify the top 15 countries with spatial mapping of the confirmed cases. A comparison was done between the identified top 15 countries for confirmed cases, deaths, and recoveries, and an advanced autoregressive integrated moving average (ARIMA) model was used for predicting the COVID-19 disease spread trajectories for the next 2 months. METHODS The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. The spatial map is useful to identify the intensity of COVID-19 infections in the top 15 countries and the continents. The recent reported data for confirmed cases, deaths, and recoveries for the last 3 months was represented and compared between the top 15 infected countries. The advanced ARIMA model was used for predicting future data based on time series data. The ARIMA model provides a weight to past values and error values to correct the model prediction, so it is better than other basic regression and exponential methods. The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. RESULTS The top 15 countries with a high number of confirmed cases were stratified to include the data in a mathematical model. The identified top 15 countries with cumulative cases, deaths, and recoveries from COVID-19 were compared. The United States, the United Kingdom, Turkey, China, and Russia saw a relatively fast spread of the disease. There was a fast recovery ratio in China, Switzerland, Germany, Iran, and Brazil, and a slow recovery ratio in the United States, the United Kingdom, the Netherlands, Russia, and Italy. There was a high death rate ratio in Italy and the United Kingdom and a lower death rate ratio in Russia, Turkey, China, and the United States. The ARIMA model was used to predict estimated confirmed cases, deaths, and recoveries for the top 15 countries from April 24 to July 7, 2020. Its value is represented with 95%, 80%, and 70% confidence interval values. The validation of the ARIMA model was done using the Akaike information criterion value; its values were about 20, 14, and 16 for cumulative confirmed cases, deaths, and recoveries of COVID-19, respectively, which represents acceptable results. CONCLUSIONS The observed predicted values showed that the confirmed cases, deaths, and recoveries will double in all the observed countries except China, Switzerland, and Germany. It was also observed that the death and recovery rates were rose faster when compared to confirmed cases over the next 2 months. The associated mortality rate will be much higher in the United States, Spain, and Italy followed by France, Germany, and the United Kingdom. The forecast analysis of the COVID-19 dynamics showed a different angle for the whole world, and it looks scarier than imagined, but recovery numbers start looking promising by July 7, 2020.

scholarly journals Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving Average (ARIMA) Model

10.2196/19115 ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e19115 ◽  
Author(s):  
Ram Kumar Singh ◽  
Meenu Rani ◽  
Akshaya Srikanth Bhagavathula ◽  
Ranjit Sah ◽  
Alfonso J Rodriguez-Morales ◽  
...  
Keyword(s):  
United States ◽  
United Kingdom ◽  
Death Rate ◽  
Rate Ratio ◽  
Moving Average ◽  
Arima Model ◽  
The United States ◽  
Recovery Ratio ◽  
Autoregressive Integrated Moving Average ◽  
The United Kingdom

Background The coronavirus disease (COVID-19) pandemic has affected more than 200 countries and has infected more than 2,800,000 people as of April 24, 2020. It was first identified in Wuhan City in China in December 2019. Objective The aim of this study is to identify the top 15 countries with spatial mapping of the confirmed cases. A comparison was done between the identified top 15 countries for confirmed cases, deaths, and recoveries, and an advanced autoregressive integrated moving average (ARIMA) model was used for predicting the COVID-19 disease spread trajectories for the next 2 months. Methods The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. The spatial map is useful to identify the intensity of COVID-19 infections in the top 15 countries and the continents. The recent reported data for confirmed cases, deaths, and recoveries for the last 3 months was represented and compared between the top 15 infected countries. The advanced ARIMA model was used for predicting future data based on time series data. The ARIMA model provides a weight to past values and error values to correct the model prediction, so it is better than other basic regression and exponential methods. The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. Results The top 15 countries with a high number of confirmed cases were stratified to include the data in a mathematical model. The identified top 15 countries with cumulative cases, deaths, and recoveries from COVID-19 were compared. The United States, the United Kingdom, Turkey, China, and Russia saw a relatively fast spread of the disease. There was a fast recovery ratio in China, Switzerland, Germany, Iran, and Brazil, and a slow recovery ratio in the United States, the United Kingdom, the Netherlands, Russia, and Italy. There was a high death rate ratio in Italy and the United Kingdom and a lower death rate ratio in Russia, Turkey, China, and the United States. The ARIMA model was used to predict estimated confirmed cases, deaths, and recoveries for the top 15 countries from April 24 to July 7, 2020. Its value is represented with 95%, 80%, and 70% confidence interval values. The validation of the ARIMA model was done using the Akaike information criterion value; its values were about 20, 14, and 16 for cumulative confirmed cases, deaths, and recoveries of COVID-19, respectively, which represents acceptable results. Conclusions The observed predicted values showed that the confirmed cases, deaths, and recoveries will double in all the observed countries except China, Switzerland, and Germany. It was also observed that the death and recovery rates were rose faster when compared to confirmed cases over the next 2 months. The associated mortality rate will be much higher in the United States, Spain, and Italy followed by France, Germany, and the United Kingdom. The forecast analysis of the COVID-19 dynamics showed a different angle for the whole world, and it looks scarier than imagined, but recovery numbers start looking promising by July 7, 2020.

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