Box-Jenkins Research Articles

Application of Box-Jenkins Models to the Tourist Inflow in Bhutan

Bhutan has now increasingly become a popular destination for many international tourists. Tourism in Bhutan is considered as one of the largest foreign earning industries. The number of tourist inflow in the country is increasing year by year. Forecasting is very necessary for administration and tourist agent for creating awareness and planning for the future development. It can also predict the future trends as accurately as possible and helps in staying one step ahead of the competition. This study aims to apply mathematical model for forecasting monthly tourist inflow from Malaysia, Singapore, China, USA, England, France, Germany, Thailand, Australia and Japan to Bhutan. The Box-Jenkins model is used to identify the parameters of Autoregressive integrated moving average (ARIMA) model of monthly tourist visited data of above mentioned countries in the period 2011-2015 obtained from Tourism Council of Bhutan. An Akaike's Information Criterion, Schwartz's Bayesian Criterion and estimate variance of white noise are used throughout to test for the identification of best fit model. Further, the periodogram analysis was used to confirm the seasonal period of the model. The results showed ARIMA model for Thai, Chinese, Malaysian and Japanese, while seasonal ARIMA for American, Australian, British, French, Singaporean and German. Further, seasonal ARIMA model was obtained as the best fit model for the overall data. These models are illustrated and could possibly forecast the monthly tourist inflow of one year ahead with acceptable accuracy.

Trend analysis of price inflation on maize in Mekelle City, Ethiopia

The major endeavor of this research paper is to demonstrate the time series analysis of price inflation of maize in Mekelle city. The statistical technique that we have applied to investigate the data is time series analysis. Consequently, time series method that would use to test stationarity by using Dickey-Fuller Test and Stationary during differencing (second order differencing). By using R software the comprehension of time series analysis, trend analysis, and Box-Jenkins models are made for this data. A total of 60 months of price inflation of maize was incorporated in this study. From the Box-Jenkins model evaluation, the ARIMA (2,2,1) was found the preeminent model. AIC method of evaluation was applied to select the preeminent model from the alternative and also considering Box-Jenkins methodology monthly retail prices were forecasted for one year. The outcomes be evidence for that the trend of price inflation of maize is fragment like (constant). In conclusion, the studies guide to forecasting the price inflation of maize of year 2019 G.C.

Development of an Information and Analytical System for Predicting Price Fluctuations and Long-Term Trends Using Generative-Adversarial Neural Networks

Deep learning methods (DML) have been widely used in financial fields recently, such as stock market forecasting, balance the portfolio, financial information processing, and transaction execution strategies. Stock market forecasting and effective trading strategy construction, when using deep learning, are the most popular ways of applying DML in the field of finance. Against the background of the general development of the Russian stock market, the study and investigation of its price dynamics is a highly promising direction for analyzing and forecasting the value of financial assets in which it is planned to invest money. In this study, a new architecture of a conditional generative-adversarial neural network (GAN) with a multi-level perceptron (MLP) as a discriminator and a long short-term memory (LSTM) as a generator for determining trends is proposed. The Box-Jenkins method (ARIMA) is used to determine the confidence interval.

Predictive study of tuberculosis incidence by time series method and Elman neural network in Kashgar, China

ObjectivesKashgar, located in Xinjiang, China has a high incidence of tuberculosis (TB) making prevention and control extremely difficult. In addition, there have been very few prediction studies on TB incidence...

High performance approach for water level forecasting in Yom River basin of Thailand

Analyses of average monthly water level (AMWL) time series (April 2007 - March 2020) at four water level measurement stations (Y.31, Y.20, Y.1C, Y.37) for wet and dry seasons in the Yom River basin of Thailand. Using Box-Jenkins method, eight best-fit seasonal ARIMA models for one hydrological year forecasting for wet and dry seasons of AMWL were selected; among from twenty-four possible models, by minimum values of AIC, SBIC RMSE, and MAPE. Besides, The comparisons with two benchmark models, Holt-Winters’ Seasonal additive method, and Seasonal Naïve method were applied. Results indicated that: The four selected SARIMA models for wet seasons of Y.31, Y20, Y1C, and Y.37 are SARIMA(1,1,1)(1,0,0)[6], SARIMA(1,1,1)(1,0,0)[6], SARIMA(1,1,2)(1,0,0)[6], and SARIMA(1,1,1)(1,0,0)[6], respectively. While the models for dry seasons are SARIMA(0,1,1)(2,0,0)[6], SARIMA(1,1,1)(1,0,1)[6], SARIMA(1,1,1)(2,0,0)[6], and SARIMA(1,0,0)(1,0,1)[6]. The forecasting performance are the minimum values of RMSE and MAPE between SARIMA and the benchmark models. The SARIMA model is the best approach for Y.31 Station [Wet Season], Y.31 Station [Dry Season], and Y.37 Station [Dry Season], while the best method for Y.20 Station [Wet Season], Y.1C Station [Wet Season], Y.37 Station [Wet Season], Y.20 Station [Dry Season], and Y.1C Station [Dry Season] is Holt-Winters’ seasonal additive method. The upstream station (Y.31 station) has higher accuracy than the downstream station (Y.37 station) due to human activities that disturb hydrological changes. Furthermore, the dry season forecasting is more accurate than the wet season.

Monitoring and forecasting the COVID-19 epidemic in the UK

This paper shows how existing methods of time series analysis and modeling can be exploited in novel ways to monitor and forecast the COVID-19 epidemic. In the past, epidemics have been monitored by various statistical and model metrics, such as evaluation of the effective reproduction number, R(t). However, R(t) can be difficult and time consuming to compute. This paper suggests two relatively simple data-based metrics that could be used in conjunction with R(t) estimation and provide rapid indicators of how the epidemic’s dynamic behavior is progressing. The new metrics are the epidemic rate of change (RC) and a related state-dependent response rate parameter (RP), recursive estimates of which are obtained from dynamic harmonic and dynamic linear regression (DHR and DLR) algorithms. Their effectiveness is illustrated by the analysis of COVID-19 data in the UK and Italy. The paper also shows how similar methodology, combined with the refined instrumental variable method for estimating hybrid Box–Jenkins models of linear dynamic systems (RIVC), can be used to relate the daily death numbers in the Italian and UK epidemics and then provide 15-day-ahead forecasts of the UK daily death numbers. The same approach can be used to model and forecast the UK epidemic based on the daily number of COVID-19 patients in UK hospitals. Finally, the paper speculates on how the state-dependent parameter (SDP) modeling procedures may provide data-based insight into a nonlinear differential equation model for epidemics such as COVID-19.

The Autoregressive Moving Average with Exogenous Excitation Model for Acoustic Scattering from Underwater Objects

This study aims to identify and predict objects underwater using the autoregressive moving average with exogenous excitation (ARMX) model in such a way that the outcome of the model is similar to actual measurements. It is used for parameter estimation. This model is validated by comparing results in actual model with ARMX model, autoregressive with an exogenous variables, and Box Jenkins (BJ) model. The results are analyzed in frequency and time domain by using mean square error criterion. Initial results show that ARMX predicts the acoustic scattering response with an accuracy of 96%, while ARX provides an accuracy of 78%, and BJ model poorly estimates the signal with an accuracy of 35%. ARMX also provides higher accuracy of detection by 7-8% as compared to the existing techniques.

Forecasting leptospirosis incidence in the Philippines using the Box–Jenkins method

This study aims to investigate and find a suitable model for forecasting the leptospirosis incidence in the Philippines. The Box-Jenkins approach was utilized in the development of an appropriate model. The dataset was retrieved from the Epidemiology Bureau of the Department of Health containing the weekly number of leptospirosis cases in the Philippines from 2016 to 2018. This dataset was analyzed using the R software. The original series is nonstationary with indications of non-constant variance. Box-Cox transformation and ordinary differencing were performed on the series. The transformed series was analyzed and the results show that ARIMA(0,1,0) or the random walk model is the most appropriate model for forecasting leptospirosis incidence. The residuals and forecast errors of the fitted model behave like a white noise process. The fitted model may be used for forecasting the future number of leptospirosis cases in the Philippines.

Trend Analysis and Comparison of Forecast Models for Production of Turkish Crayfish (Pontastacus leptodactylus Eschscholtz, 1823) in Turkey

This paper aimed to develop forecasting models and to assess the trends in the production of freshwater crayfish in Turkey. Different trend analysis methods (Box-Jenkins method, Şen’s innovative trend analysis, Mann-Kendall test) were compared, and different autoregressive integrated moving average (ARIMA) models were generated to forecast the future trend. The results of the innovative trend analysis methodology and ARIMA models revealed that freshwater crayfish production has a decreasing trend during the study period, although there are some fluctuations. ARIMA models predicted that the production of freshwater crayfish would continue to decrease in the future period. The present paper is also the most temporally rich assessment of the crayfish production in Turkey encompassing 100+ years from 1909 to 2018. Several factors such as climate change, overexploitation, diseases, legal regulations, and fisheries management policies might affect the production amount. Therefore, appropriate policies for fisheries management and legal regulations should be planned and implemented to improve the production.

Modelling and Forecasting Inbound Tourism Demand to Croatia using Artificial Neural Networks: A Comparative Study

Tourism demand is the basis on which all commercial decisions concerning tourism ultimately depend. Accurate estimation of tourism demand is essential for the tourism industry because it can help reduce risk and uncertainty as well as effectively provide basic information for better tourism planning. The purpose of this study is to develop the optimal forecasting model that yields the highest accuracy when compared to the forecast performances of three different methods, namely Artificial Neural Network (ANN), Exponential Smoothing, and Box-Jenkins methods for forecasting monthly inbound tourist flows to Croatia. Prior studies have been applied to forecast tourism demand to Croatia based on time series models and casual methods. However, the monthly and comparative tourism demand forecasting studies using ANNs are still limited, and this paper aims to fill this gap. The number of monthly foreign tourist arrivals to Croatia covers the period between January 2005-December 2019 data were used to build optimal forecasting models. Forecasting performances of the models were measured by Mean Absolute Percentage Error (MAPE) statistics. As a result of the experiments carried out, when compared to the forecasting performances of various models, 12 lagged ANN models, which have [4-3-1] architecture, were seen to perform best among all models applied in this study. Considering both the empirical findings obtained from this study and previous studies on tourism forecasting, it can be seen that ANN models that do not have any negativities (such as over-training, faulty architecture, etc.) produce successful forecasting results when compared with results generated by conventional statistical methods.

Time Series Analyses and Modeling of Environmental Systems: Case Studies in Kuwait

Background: Time series is a sequence of measurements made at regular time intervals. Tremendous work has been done on pipe break investigation and maintenance, using different models. However, the authors have not found any research work on pipe failures in water distribution networks using time series. Objective: The principal objective of this paper was to investigate the applications of time series analysis in modeling man-made (pipe breaks) and natural (rainfall) environmental systems. Methods: This objective was satisfied by employing the Time-Domain (Box-Jenkins) approach to assess two case studies in Kuwait of a water distribution network (pipe failures), and from rainfall rates measurement of a solid waste disposal site, respectively. The statistical analysis of these data was conducted with the aid of the BMDP and SAS computer software packages and by the use of the P2T programming procedure for the Box-Jenkins method. Results: The results showed a significant correlation between the data and the time series models provides good estimates of pipe reliability and rainfall measurements. Conclusion: Based on the significant results, time series analyses could and should be useful by water authorities in planning pipe maintenance and inspection. Moreover, it is recommended and it would be very useful to conduct similar studies on pipes carrying other fluids such as gas, air and oil.

ANALISIS TEKNIKAL HARGA SAHAM PT. ADIRA DINAMIKA MULTI FINANCE Tbk. DI MASA COVID 19 DENGAN METODE ARIMA

This study aims to analyze and predict stock prices using technical analysis at PT. Adira Dinamika Multi Finance, Tbk by ARIMA method. Data collection technique used is archival data collection which comes from secondary data. Secondary data in this study’s from daily data on ADMF shares in 2nd January 2020-26th October 2020 which is sourced from finance.yahoo.com. The analysis technique used in this research is the Box-Jenkins method with three stages, i.e. the first stage of identification and specification of ARIMA model (p, d, q), the second stage is prediction stage, the third stage is diagnosis model. This study results show that the MA Model (2) is the best model whose data describes ADMF stock price movements during the pandemic period and produces a difference between the real data and forecast is not too different. Where on average there’s a difference about Rp. 43.5 between the real stock price data with forecast data. Suggestions for future researchers to use fundamental analysis to support investment decisions.

Modelling of Hazelnut Export in Turkey by Using Box-Jenkins Method and Export Forecast

Ülkemiz fındık üretim ve ihracatında Dünya’da ilk sırada yer alarak fındık pazarında söz sahibi ülke durumundadır. Bu nedenle fındık piyasasının şekillenmesinde önemli rolü vardır. Bu çalışmanın amacı Türkiye’de 1961-2018 yıllarına ait fındık ihracatının zaman serileri ile modellenerek 2023 yılına kadar olan fındık ihracat öngörüsünü yapmaktır. Fındık ihracatı veri tabanına FAO’dan ulaşılmıştır ve 2019-2023 yıllarını tahminlemek için ARIMA modeli kullanılmıştır. Yapılan analizler sonucu en uygun modelin ARIMA(1,1,11) olduğu tespit edilmiştir. Sonuçlara göre 2019 yılında Türkiye’nin fındık ihracatının 162 bin ton, 2023 yılına gelindiğinde ise 176 bin ton olacağı tahmin edilmiştir.

Domestic Tractor Market Share Estimation by Time Series Analysis Method

This study is aimed to examine the domestic tractor market share with time series analysis to forecast the future probabilities with the help of the models which are created according to the Box-Jenkins method. The study was planned for year 2010, based on 5 different domestic tractor brands which are separately estimated for the domestic tractor market and included the total tractor numbers. It has been observed that the projected value of tractor companies whose market shares are estimated by time series analysis and the total tractor number are very close to their actual values.

Dynamics of Price Growth of Curly Red Chili and Red Chili in Kolaka District

This study aims to: 1) Identify the price data pattern of Curly Red Chili and red chilies in Kolaka Regency; 2) Obtain the best time series forecasting method for forecasting the price of Curly Red Chili and ordinary red chilies in Kolaka Regency; 3) Get a forecast for the price of Curly Red Chili and regular red chilies for the coming year. This type of research is descriptive quantitative, the source of the data in this study is secondary data obtained from the office of industry and trade, Kolaka district. The data analysis method used is the time series forecasting method.The results showed that: 1) The results of the identification of data patterns on the plot of price data for Curly Red Chili and common red chilies in Kolaka Regency in 2015-2019 obtained a seasonal pattern. 2) The results of the application of the time series forecasting method for the price of curly red chilies, obtained the best time series forecasting method, namely the box-jenkins method of the SARIMA (1,0,0) (2,0,0) 12 model with the smallest MSE value, namely MSE = 41,534 590 While for the price of ordinary red chilies, the best time series forecasting method is the box-jenkins method of the SARIMA (1,0,0) (1,0,0) 12 model with the smallest MSE value, namely MSE = 44,764,512. 3) The results of forecasting the price of Curly Red Chili using the box-jenkins method SARIMA (1,0,0) (2,0,0) 12 with the help of the MINITAB 19 program is the highest price of IDR 47,355.4 / Kg which occurred in August 2020 and the lowest price is IDR 23,169 / Kg which occurred in January 2020, while the average price of Curly Red Chili in 2020 is IDR 32,487.3 / Kg. While the results of forecasting the price of ordinary red chilies using the box-jenkins method with the SARIMA (1,0,0) (1,0,0) 12 model with the help of the MINITAB 19 program is the highest price of Rp. 32,967.8 / Kg which occurred in August. 2020 and the lowest price is IDR 16,756.4 / Kg which occurred in December 2020, while the average price of ordinary red chili in 2020 is IDR 23,346.27 / Kg.

GRİ TAHMİN VE BOX-JENKINS YÖNTEMLERİ İLE ANTALYA LİMANI İÇİN AYLIK KONTEYNER TALEP TAHMİNİ

Konteyner taşımacılığının, denizyolu ticaretindeki önemi her geçen gün artmaktadır. Konteyner hacminin etkili tahmini ise liman planlaması ve işletimi için bir karar desteği sağlamaktadır. Bu nedenle liman yönetimlerinin geleceğe yönelik planları açısından tahminleme çalışmaları önemli bir rol oynamaktadır. Bu çalışmada, Antalya’da bulunan Port Akdeniz Limanı için yapılan tahmin modellerinde Ocak 2008-Aralık 2017 (120 ay) dönemi konteyner istatistikleri veri seti olarak kullanılmıştır. Liman işletmesinin yük talep tahmini, konteyner bazında ve mevsimsel farklılıklar dikkate alınarak, Ocak 2018-Aralık 2019 (24 ay) dönemi için yapılmıştır. Gri Tahmin ve Box-Jenkins yöntemlerinin kullanıldığı çalışmada, konteyner tahminleri Gri Model (1,1) ve ARIMA (0,1,0)x(0,1,1)12 modelleri ile analiz edilmiştir. Tahmin sonuçları başarı kriterleri ile değerlendirildiğinde, Gri Model (1,1)’in MAPE ve MAE değerlerinin daha düşük olduğu gözlemlenmiştir. Ancak hem RMSE ve MSE hem de sapma değerleri dikkate alındığında ise; ARIMA (0,1,0)x(0,1,1)12 modelinin daha iyi ve uygun tahmin değerleri verdiği tespit edilmiştir.

Recent trends for the management of diabetes for older adults in the context of universal coverage and COVID-19: evidence from Mexico.

BackgroundTo identify trends in the epidemiological and economic burden of diabetes in the elderly.MethodsUsing the Box–Jenkins method to estimate expected cases for the period 2020–2022, costs were determined with the instrumentation technique. The population base was 4 032 189 older adults diagnosed with diabetes in 2019.ResultsRegarding the epidemiological burden, there is an increase of 11–15% (p<0.001). Comparing the economic burden for 2020 vs 2022, the increase is estimated as 29% (p<0.05).ConclusionIn the framework of the coronavirus disease 2019 pandemic, the increase in diabetes cases and costs in older adults substantially complicates the scope of universal coverage for patients with diabetes.

Analysis of the Strategic Emission-Based Energy Policies of Developing and Developed Economies with Twin Prediction Model

Upholding sustainability in the use of energies for the increasing global industrial activity has been one of the priority agendas of the global leaders of the West and East. The projection of different GHGs has thus been the important policy agenda of the economies to justify the positions of their own as well as of others. Methane is one of the important components of GHGs, and its main sources of generation are the agriculture and livestock activities. Global diplomacy regarding the curtailment of the GHGs has set the target of reducing the levels of GHGs time to time, but the ground reality regarding the reduction is far away from the targets. Sometimes, the targets are fixed without the application of scientific methods. The aim of the present study is to examine sustainability of energy systems through the forecasting of the methane emission and agricultural output of the world’s different income groups up to 2030 using the data for the period 1981–2012. The work is novel in two senses: the existing studies did not use both the Box–Jenkins and artificial neural network methods, and the present study covers all the major economic groups in the world which is unlike to any existing studies. Two methods are used for forecasting of the two. One is the Box–Jenkins method, where linear nature of the two variables is considered and the other is artificial neural network methods where nonlinear nature of the variables is also considered. The results show that, except the OECD group, all the remaining groups display increasing trends of methane emission, but unquestionably, all the groups display increasing trends of agricultural output, where middle- and upper middle-income groups hold the upper berths. The forecasted emission is justified to be sustainable in major groups under both methods of estimations since overall growth of agricultural output is greater than that of methane emission.

Comparison of Box-Jenkins models predicting Iraq’s population growth rate

The prediction of the urban population growth rate it gives estimate the expected change in the rate of growth in the future Based on socio-economic development and geography of the population, The use of time series analysis is one of the most important statistical methods used in the study and analysis of annual, monthly and daily data To prediction the values of future random phenomena based on what happened in the past which helps in making future plans for economic development. The goal of most of the statistical population studies is to provide an approximate forecast future population. In this paper, the Box-Jenkins models were compared to predict the rate of population growth in urban areas until the year (2033) based on the criterion of the mean absolute percentage error (MAPE) to choose the best prediction model.

FORECASTING THE NUMBER OF AIRPLANE PASSENGERS USING BOX-JENKINS AND ARTIFICIAL NEURAL NETWORK IN MALAYSIA

Airline industry is one of the largest industries in the world of transport because it is the most important transport in the global transport system. The airline industry has played a very important role in the economic development in Malaysia. Due to the increase in its operating business, the demand for air travel increases day by day. Hence, this study focused on the number of passengers using air transport in Malaysia. The monthly data from January 2005 to December 2015 were obtained from Malaysia Airport Holdings Berhad (MAHB) in Sepang, Selangor. The data is divided into 2 parts, which are in sample data from January 2005 to December 2014 and out sample data from January 2015 to December 2015. The study was conducted to predict airline passengers in Malaysia using the Box-Jenkins model and Artificial Neural Network (ANN) model. Both models were studied to choose the best model. Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE) were used to measure the performance of both models. SARIMA was selected as the best model for Box-Jenkins with MAPE and MSE were 7.3458388 and 2.67011 respectively while Multilayer Feed Forward Neural Network (MFFNN) with seven input variables, with MAPE and MSE, 7.251 and 0.0006 respectively were selected as the best model for Multilayer Feed Forward Neural Network (FFNN). In conclusion, these studies have proven that the Multilayer Feed Forward Neural Network (FFNN) model is the best model for considering airplanes in Malaysia compared to the SARIMA model.