Box-Jenkins Research Articles

TIME SERIES FORECASTING FOR TOURISM INDUSTRY IN MALAYSIA

This study is conducted to forecast the future tourism demand in Malaysia by applying Box-Jenkins modelling. The time series data of tourist arrivals volume in Malaysia before MCO retrieved from MOTAC Malaysia database is implemented in this study. The forecast evaluation methods used to validate the best Box-Jenkins model before proceeding to forecasting stage are MAPE and RMSE, and the analysis was performed by using Python. The findings show that SARIMA was considered as highly accurate forecasting model based on its least error produced.

Prospects for the economy and foreign trade of Ethiopia: fore cast estimates based on mathematical modeling

The article presents a forecast assessment of the development of the national economy and foreign trade of Ethiopia until 2030, calculated by the authors on the basis of a mathematical and systematic analysis of macroeconomic processes and methods of modeling these processes usingthe Box-Jenkins model (ARIMA). Estimation method: the exact maximum likelihood method. The topic seems relevant due to Ethiopia's accession to the BRICS alliance from 2024. Therefore, it is important to predict possible trends in Ethiopia's economic and foreign trade development, their significance for the country itself, as well as their impact on the economic dynamics of other BRICS countries and the association as a whole. The study is updated taking into account the continued increase in global volatility and the emergence of new threats, including in Africa. The subject is trends in the economic development of Ethiopia as a member of the BRICS. The purpose of the article is to develop a scientifically based forecast of the development of the economy and foreign trade of Ethiopia in the medium term. The article identifies potential threats to the development of the Ethiopian economy. The statistical basis of the study was mainly data from the United Nations Conference on Trade and Development (UNCTAD). The results of the study can be used to understand the trends of socio-economic and foreign trade development of the country, adjust this development, as well as expand and enrich the theoretical concepts of the world economy. The research and author's calculations are based solely on the current circumstances and current realities, without taking into account any fundamental changes in the politics and economy of the country under study. The author's calculations and conclusions can be useful to economists, entrepreneurs, and researchers as a basis for their further practical and scientific activities.

Empirical Comparison of Forecasting Methods for Air Travel and Export Data in Thailand

Time series forecasting plays a critical role in business planning by offering insights for a competitive advantage. This study compared three forecasting methods: the Holt–Winters, Bagging Holt–Winters, and Box–Jenkins methods. Ten datasets exhibiting linear and non-linear trends and clear and ambiguous seasonal patterns were selected for analysis. The Holt–Winters method was tested using seven initial configurations, while the Bagging Holt–Winters and Box–Jenkins methods were also evaluated. The model performance was assessed using the Root-Mean-Square Error (RMSE) to identify the most effective model, with the Mean Absolute Percentage Error (MAPE) used to gauge the accuracy. Findings indicate that the Bagging Holt–Winters method consistently outperformed the other methods across all the datasets. It effectively handles linear and non-linear trends and clear and ambiguous seasonal patterns. Moreover, the seventh initial configurationdelivered the most accurate forecasts for the Holt–Winters method and is recommended as the optimal starting point.

Estimating and forecasting bitcoin daily prices using ARIMA-GARCH models

PurposeThe goal of the study is to offer important insights into the dynamics of the cryptocurrency market by analyzing pricing data for Bitcoin. Using quantitative analytic methods, the study makes use of a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and an Autoregressive Integrated Moving Average (ARIMA). The study looks at how predictable Bitcoin price swings and market volatility will be between 2021 and 2023.Design/methodology/approachThe data used in this study are the daily closing prices of Bitcoin from Jan 17th, 2021 to Dec 17th, 2023, which corresponds to a total of 1065 observations. The estimation process is run using 3 years of data (2021–2023), while the remaining (Jan 1st 2024 to Jan 17th 2024) is used for forecasting. The ARIMA-GARCH method is a robust framework for forecasting time series data with non-seasonal components. The model was selected based on the Akaike Information Criteria corrected (AICc) minimum values and maximum log-likelihood. Model adequacy was checked using plots of residuals and the Ljung–Box test.FindingsUsing the Box–Jenkins method, various AR and MA lags were tested to determine the most optimal lags. ARIMA (12,1,12) is the most appropriate model obtained from the various models using AIC. As financial time series, such as Bitcoin returns, can be volatile, an attempt is made to model this volatility using GARCH (1,1).Originality/valueThe study used partially processed secondary data to fit for time series analysis using the ARIMA (12,1,12)-GARCH(1,1) model and hence reliable and conclusive results.

Application of the Box and Jenkins Method to the Time Series of Success Rates of State Examinations in the Democratic Republic of Congo from 1967 to 2021

In this article, This is about present an analysis conducted on the time series of success rates in State Examinations in the Democratic Republic of Congo (DRC) since its inception in 1967 until 2021, following the BOX and JENKINS methodology, which allows for obtaining a parsimonious ARIMA(0,1,2) Parcimony, the chosen model due to its familiarity with the presented data, has proved to be the only one to adhere to all the Box-Jenkins method steps for the series of State Exam pass rates in the DRC. At the conclusion of this study, short-term predictions calculated using the Evew12 software with first differencing applied indicate 56.1% for 2022, 61.64% in 2023, 61.61% in 2024, and 61.54% in 2025. These forecasted results show upward trends and almost stability from 2023 to 2025.

A Study of Suitable Forecasting Model for the Export Quantity of Fresh or Chilled Fish in Thailand

The aim of this study was to find a suitable forecasting model for the export quantity of fresh or chilled fish in Thailand using seven statistical methods including Box-Jenkins method, Holt’s exponential smoothing method, Brown’s exponential smoothing method, damped trend exponential smoothing method, simple seasonal exponential smoothing method, Winters’ additive exponential smoothing method, and Winters’ multiplicative exponential smoothing method. The average monthly export quantity from the website of the Office of Agricultural Economics was used for data analysis. The data set used to create the models was the export quantity from January 2011 to December 2020 for 120 months. The data set used to compare the accuracy was the export quantity from January to December 2021 for 12 months using the lowest mean absolute percentage error. The results indicated that the most accurate method was the damped trend exponential smoothing method.

Application of the ARIMA model in house price index in Malaysia

The factors that affecting the escalating price of houses in Malaysia are driven by factors such as population growth, income dynamics, interest rates, and GDP. This phenomenon has notably outpaced the growth of household incomes, thus majorly impacting Malaysians. The study’s primary goal is to forecast the housing price index in Malaysia from the best model obtained using Box-Jenkins method. aligning with the 2018-2025 National Housing Policy objectives, utilizing advanced machine learning and time series modeling. The objectives guide the research: to find the best model for predicting house price index in Malaysia using Box-Jenkins Method. Utilizing secondary data from the National Property Information Centre (NAPIC) spanning from 1988 to 2023, the study employed the analytical method of ARIMA. The results favored the ARIMA (1,1,1) model as the best model in predicting housing price indexes. This offers an excellent forecasting model for residential properties towards gaining better understanding of their pricing dynamics and offers potential solutions to the issue of housing affordability for Malaysians.

Multi-Target In-Silico modeling strategies to discover novel angiotensin converting enzyme and neprilysin dual inhibitors

Cardiovascular diseases, including heart failure, stroke, and hypertension, affect 608 million people worldwide and cause 32% of deaths. Combination therapy is required in 60% of patients, involving concurrent Renin–Angiotensin–Aldosterone-System (RAAS) and Neprilysin inhibition. This study introduces a novel multi-target in-silico modeling technique (mt-QSAR) to evaluate the inhibitory potential against Neprilysin and Angiotensin-converting enzymes. Using both linear (GA-LDA) and non-linear (RF) algorithms, mt-QSAR classification models were developed using 983 chemicals to predict inhibitory effects on Neprilysin and Angiotensin-converting enzymes. The Box-Jenkins method, feature selection method, and machine learning algorithms were employed to obtain the most predictive model with ~ 90% overall accuracy. Additionally, the study employed virtual screening of designed scaffolds (Chalcone and its analogues, 1,3-Thiazole, 1,3,4-Thiadiazole) applying developed mt-QSAR models and molecular docking. The identified virtual hits underwent successive filtration steps, incorporating assessments of drug-likeness, ADMET profiles, and synthetic accessibility tools. Finally, Molecular dynamic simulations were then used to identify and rank the most favourable compounds. The data acquired from this study may provide crucial direction for the identification of new multi-targeted cardiovascular inhibitors.

A Hybrid Approach for Hierarchical Forecasting of Industrial Electricity Consumption in Brazil

The Brazilian industrial sector is the largest electricity consumer in the power system. Energy planning in this sector is important mainly due to its economic, social, and environmental impact. In this context, electricity consumption analysis and projections are highly relevant for the decision-making of the industrial sectorand organizations operating in the energy system. The electricity consumption data from the Brazilian industrial sector can be organized into a hierarchical structure composed of each geographic region (South, Southeast, Midwest, Northeast, and North) and their respective states. This work proposes a hybrid approach that incorporates the projections obtained by the exponential smoothing and Box–Jenkins models to obtain the hierarchical forecasting of electricity consumption in the Brazilian industrial sector. The proposed approach was compared with the bottom-up, top-down, and optimal combination approaches, which are widely used for time series hierarchical forecasting. The performance of the models was evaluated using the mean absolute percentage error (MAPE) and root mean squared error (RMSE) precision measures. The results indicate that the proposed hybrid approach can contribute to the projection and analysis of industrial sector electricity consumption in Brazil.

Scarring In Utero: An Attempt to Validate With Data Unconfounded by Migration and Medical Care.

"Scarring in utero" posits that populations exposed to injurious stressors yield birth cohorts that live shorter lives than expected from history. This argument implies a positive historical association between period life expectancy (i.e., average age at death in year t) and cohort life expectancy (i.e., average lifespan of persons born in year t). Tests of the argument have not produced consistent results and appear confounded by autocorrelation, migration, and access to medical care. Here we test whether, as predicted by scarring in utero, sex-specific period and cohort life expectancy appear positively related among Swedes born from 1751 through 1800. If scarring has ever influenced longevity, we should detect signals of its effects in these cohorts because, unlike other populations with known life span, they aged in place and unlikely benefitted from increased access to efficacious medical care. We use Box-Jenkins methods to control autocorrelation and measure associations. Contrary to the scarring hypothesis, we find an inverse association between period and cohort life expectancy. Our findings imply that, among males, variation in injurious stress on the population predicted changes in cohort life span ranging from a gain of approximately 67 weeks to a loss of about 45 weeks of life and among females from a gain of approximately 68 weeks to a loss of about 38 weeks of life. Epidemiologists trying to understand and explain temporal variation in cohort life expectancy should view the scarring argument with greater skepticism than currently found in the literature.

An inventory-routing optimization model for pharmaceutical supply chain with time-dependent travel time on a multi-graph network: a case study

PurposeSpurred by the high turnover in the pharmaceutical industry, locating pharmacies inside urban areas along with the high product perishability in this industry, the pharmaceutical supply chain management has recently gained increasing attention. Accordingly, this paper unveils an inventory-routing problem for designing a pharmaceutical supply chain with perishable products and time-dependent travel time in an uncertain environment.Design/methodology/approachIn this study, mathematical programming is employed to formulate a multi-graph network affected by the traffic volume in order to adapt to real-world situations. Likewise, by transforming the travel speed function to the travel time function using a step-by-step algorithm, the first-in-first-out property is warranted. Moreover, the Box–Jenkins forecasting method is employed to diminish the demand uncertainty.FindingsAn appealing result is that the delivery horizon constraint in the under-study multi-graph network may eventuate in selecting a longer path. Our analysis also indicates that the customers located in the busy places in the city are not predominantly visited in the initial and last delivery horizon, which are the rush times. Moreover, it is concluded that integrating disruption management, routing planning and inventory management in the studied network leads to a reduction of costs in the long term.Originality/valueApplying the time-dependent travel time with a heterogeneous fleet of vehicles on the multi-graph network, considering perishability in the products for reducing inventory costs, considering multiple trips of transfer fleet, considering disruption impacts on supply chain components and utilizing the Box–Jenkins method to reduce uncertainty are the contributions of the present study.

Identification of heat exchanger process through Hammerstein and polynomial models

Process industries extensively use heat exchangers in their operations, playing a crucial role in various sectors by facilitating efficient heat transfer, conserving energy, and reducing operational costs. This paper focuses on identifying and validating the system model, with the primary task of designing the controller involving the determination of the mathematical representation of the system. The First Order Plus Dead Time (FODT) model, derived from basic principles, is used to represent the system. To analyze the system’s behavior and construct a suitable controller, model development is essential, achieved by constructing principles models using energy balance equations to identify the heat exchanger process. Data collected from the model are utilized in the identification process, with the temperature at the outlet of the hot air blower being the controlled variable in this investigation. The research aims to determine the mathematical model based on time versus temperature data acquired from the Heat Exchanger. Various system identification methods, such as Hammerstein Wiener (HW), Auto Regressive with Exogenous Input (ARX), Box-Jenkins (BJ), Output-Error (OE), and Auto Regressive Moving Average with Exogenous Input (ARMAX) models, are implemented for the heat exchanger. The models obtained undergo validation, and the best-fit model closest to the physical system is considered for controller design. After conducting the analysis, it was found that the Output-Error (OE) model outperforms other models in terms of achieving the best fit.

Forecasting Mango Production and Area in Punjab, Pakistan for 2021-2029

Production of Mango in Pakistan has increased due to use of improved farm inputs and better management practices. Despite an increased production and rising demand in the export market, the potential of mango export has, however, not been fully achieved. Pakistan has comparative advantage in the production of Mango and enormous potential exists for its export in the vast Middle East market. The Punjab province of Pakistan which account for 63% of the country's total mango output. The objective of this study is to predict the area and output of mangoes of Pakistan's Punjab province for the year 2029. For this purpose, the 63-year data from 1957-2019 has been collected from Crop Reporting Service Punjab. Box-Jenkins method has been used to build an appropriate Univaraite ARIMA model. The best model was chosen after a comparison of the models' mean absolute percentage error, root mean squared error, stationary R-square, and other properties. The area's acceptable model was ARIMA (1,1,0) and production’s acceptable model was ARIMA (1,1,1). Additionally, this estimate predicts that by 2029, the area will be (246.46-279.63) hectors and output will be 1358.10-1538.47) million tons. The results of this study will be useful to government planners, marketers, businesses and purchasing nations.

Forecasting of area, production, and yield of jute in Bangladesh using Box-Jenkins ARIMA model

Jute holds significant importance as a cash crop in Bangladesh and was once renowned as the country's "golden fiber." Before embarking on a comprehensive strategy, it is crucial to gain insight into the future scenarios of jute in Bangladesh. This study's main objective is to employ the Box-Jenkins method to choose the most suitable Autoregressive Integrated Moving Average (ARIMA) models for forecasting area, production, and yield of jute in Bangladesh for the period from 2023 to 2030. This research relies on historical time series data spanning from 1970 to 2022. After thorough analysis, the most appropriate models were determined: ARIMA (2,0,3) for area, ARIMA (1,0,2) for production, and ARIMA (1,0,3) for yield. These selections were made based on model selection criteria, normalized BIC values, examination of ACF and PACF plots, and confirmation that residuals from these chosen ARIMA models exhibit no serial correlation, as indicated by Ljung-Box Q statistics. The ARIMA model predicts a continuous upward trajectory for jute cultivation, projecting that the jute cultivation area will expand to 7.54 lac hectares by 2023 and 8.30 lac hectares by 2030. This represents an estimated increase of approximately 13.85 % compared to the jute area in 2022. However, projections indicate a decline in both jute production and yield. By 2023, production is forecasted to be 80.48 lac bales with a yield of 11.47 bales per hectare, while by 2030, production is expected to decrease to 74.34 lac bales with a yield of 11.22 bales per hectare, down from 84.58 lac bales and 11.59 bales per hectare respectively in 2022.These findings are valuable for researchers and policymakers as they provide new insights into the future dynamics of jute cultivation in Bangladesh. This information can guide the development of initiatives and plans aimed at enhancing jute area, production, and yield in the country.

The Pattern of Motorcyclists' Death Due to Accidents and a Three-year Forecast in East Azerbaijan Province, Iran: A Time Series Study

Introduction: In low- and middle-income countries, a large proportion of road users include pedestrians, cyclists, and motorcyclists, and nearly half of road traffic fatalities occur among motorcyclists. This study aimed to examine the pattern of motorcyclists' death due to accidents in East Azerbaijan, Iran between 2006 and 2021 and present a forecast. Methods: We used death data due to motorcycle accidents of Legal Medicine Department between 2006 and 2021. For time series analysis, the Box-Jenkins model was used and three stages of identification, estimation, and diagnosis were successively performed and repeated several times to achieve the best prediction model. The Box-cox transformation method was used to stabilize the variance, and the first-order seasonal differential method with a period of 12 was used to control the seasonality. Due to seasonal variations, the Seasonality Auto-Regressive Integrated Moving Average model: SARIMA (p, d, q) (P, D, Q)s was employed and the death trend was predicted for 36 months. The candidate models were compared based on Log-likelihood, AIC, and BIC indices. STATA 17 was used for data analysis. Results: About 18.6% of all accident deaths are attributed to motorcycle accidents. The death rate for all causes of accidents and motorcycle accidents were 23.13 and 4.30 per 100,000 population, respectively. Seven models were considered as candidates. The SARIMA (0, 0, 0) (1, 1, 1)12 model was selected as the best model due to better fit and used to predict the number and trend of motorcycle accident deaths. Motorcycle accident deaths are predicted to decrease gradually in the next 36 months, from June 2021 to May 2024, affected by seasonal changes. Conclusion: The trend of death due to motorcycle accidents from 2006 to 2021 in East Azerbaijan was declining, and it is predicted to decrease slightly in the next three years as well. As this reduction may be attributed to many factors, it is recommended to investigate effective factors in future studies.

Experimental identification of a linear parametric driver steering control model with torque feedback

Existing mathematical driver models with steering torque feedback are reviewed. It is argued that a new linear parametric driver-steering-vehicle model is required to provide greater insight to subjective assessment of steering torque feedback. The proposed new model incorporates: neuromuscular dynamics; proprioceptive (muscle stretch) and visual sensory dynamics; steering torque perception via efference copy; process and measurement noise; state estimator; and an internal predictive model of the plant. A series of path-following experiments with thirteen test subjects is performed on a fixed-base driving simulator. A Box-Jenkins model accounted for 90% of the variance of the measured handwheel angle time series averaged over the test subjects (VAF). The unknown parameter values of the model were identified from the driving simulator data. The prediction accuracy approached the upper bound given by the Box-Jenkins model. Process noise was found to be linearly dependent on muscle activation torque, and this signal-dependent behaviour was also present in the measurement noise. The model was re-identified using signal to noise ratios as parameters. The VAF was 86.6%, close to that of the Box-Jenkins model, and the identified parameter values are physically plausible, giving confidence that the model is suitable for further work on understanding steering torque feedback.

A survey on cryptocurrency price prediction

A type of digital currency known as a cryptocurrency allows all transactions to be completed online. There is no hard cash version of this soft currency. We highlight that a decentralized currency differs from a centralized currency in the any user of a virtual currency can purchase services without the need for third parties to get involved. Due to its extreme price volatility, using these cryptocurrencies has an impact on trade and international relations. Moreover, the constantly fluctuating oscillations indicate the urgent need for a more precise method of predicting this price. Deep learning techniques that use effective learning models for training data, including the LSTM, GRU, and Feedback Neural Network, can be used to do this. Benchmark datasets are used to test the suggested strategy. That brings us to the neural network, one of the clever data mining technologies that researchers in many domains have been using for the past ten years. In the current economy, stock market data is essential. There are two types of forecasting methodologies: nonlinear models (ARCH, GARCH, Neural Network) and linear models (AR, MA, ARIMA, ARMA). To forecast a company's stock price based on past prices, we employed the Box Jenkins Model also known as ARIMA, and Long Short-Term Memory (LSTM), and Feedback Neural Network also known as RNN.

Determination of heat wave definition temperatures in Spain at an isoclimatic level: time trend of heat wave duration and intensity across the decade 2009–2018

BackgroundIn line with WHO guidelines for the implementation of public health prevention plans targeted at the impacts of high temperatures, a heat wave definition temperature (Tthreshold) was calculated for 182 so-called “isoclimatic zones” (IZ) in Spain. As the dependent variable for determining this Tthreshold, we analysed daily all-cause mortality data (ICD-10: A00-R99) for each IZ across the period 2009–2018. The independent variable used was the mean value of the maximum daily temperature of the summer months recorded at meteorological observatories in each IZ. We used Box–Jenkins models to ascertain mortality anomalies, and scatterplots to link these anomalies to the temperatures at which they occurred, thereby determining the Tthreshold for each IZ. We then calculated how many heat waves had occurred in each IZ, as well as their intensity, and analysed their time trend over this period.ResultsThe results showed that in 52.5% of the IZ, the percentile of the maximum temperatures series of the summer months to which Tthreshold corresponded was below the 95th percentile of the meteorological heat wave definition in Spain: indeed, it only coincided in 30.7% of cases. The geographical distribution of these percentiles displayed great heterogeneity as a consequence of the local factors that influence the temperature–mortality relationship. The trend in the number of heat waves analysed indicated an overall increase in Spain at a rate of 3.9 heat waves per decade, and a similar rise in mean annual intensity of 9.5 °C/decade. These time-trend values were higher than those yielded by analysing the trend in meteorological heat waves based on the 95th percentile.ConclusionsThe results obtained in this study indicate the need to use a heat wave definition based on epidemiological temperature–mortality studies, rather than on values based on meteorological percentiles. This could be minimising estimated health impacts in analyses of future impacts attributable to heat.

AI-Driven Prediction of Average Per Capita GDP: Exploring Linear and Nonlinear Statistical Techniques

Average per capita GDP income is an important economic indicator. Economists use this term to determine the amount of progress or decline in the country's economy. It is also used to determine the order of countries and compare them with each other. Average per capita GDP income was first studied using the Time Series (Box Jenkins method), and the second is linear and non-linear regression; these methods are the most important and most commonly used statistical methods for forecasting because they are flexible and accurate in practice. The comparison is made to determine the best method between the two methods mentioned above using specific statistical criteria. The research found that the best approach is to build a model for predicting Iraq’s average GDP per capita income by relying on the amounts of average GDP per capita income in the past years (1981-2020). The researcher found that in a second way, it became clear that the non-linear regression model of the Asian model was the best model representing (average per capita GDP income) in Iraq, and this model was used to predict the period (20221-2027). When comparing the two methods of projected amounts up to 2027, it was found that the best method was the second based on the indicator mean absolute percentage error (MAPE) because he has the least value.

Generating Synthetic Electricity Load Time Series at District Scale Using Probabilistic Forecasts

Thanks to various European directives, individuals are empowered to share and trade electricity within Renewable Energy Communities, enhancing the operational efficiency of local energy systems. The digital transformation of the energy market enables the integration of decentralized energy resources using cloud computing, the Internet of Things, and artificial intelligence. In order to assess the feasibility of new business models based on data-driven solutions, various electricity consumption time series are necessary at this level of aggregation. Since these are currently not yet available in sufficient quality and quantity, and due to data privacy reasons, synthetic time series are essential in the strategic planning of smart grid energy systems. By enabling the simulation of diverse scenarios, they facilitate the integration of new technologies and the development of effective demand response strategies. Moreover, they provide valuable data for assessing novel load forecasting methodologies that are essential to manage energy efficiently and to ensure grid stability. Therefore, this research proposes a methodology to synthesize electricity consumption time series by applying the Box–Jenkins method, an intelligent sampling technique for data augmentation and a probabilistic forecast model. This novel approach emulates the stochastic nature of electricity consumption time series and synthesizes realistic ones of Renewable Energy Communities concerning seasonal as well as short-term variations and stochasticity. Comparing autocorrelations, distributions of values, and principle components of daily sequences between real and synthetic time series, the results exhibit nearly identical characteristics to the original data and, thus, are usable in designing and studying efficient smart grid systems.