Forecasting with X-12-ARIMA: International tourist arrivals to India and Thailand

Balogh, Péter; Kovács, Sándor; Chaiboonsri, Chukiat; Chaitip, Prasert

doi:10.19041/apstract/2009/1-2/5

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…(Upadhyay, 2013) In this study used ARIMA model using time series data for 1996/97 -2011/2012 with the aid of Box and Jenkins method; and Bayesian information criterion (BIC) and mean absolute percentage error (MAPE) were used to obtain suitable models and forecast the export and import of wood based panels in India. (Balogh et al, 2009) In this study, international tourist arrivals in India and Thailand for the years 2007-2010 were forecast with the help of ARIMA model,, and the results of the study revealed that tourism trends will increase in both the countries. (Celik et al, 2017) An attempt was made to forecast peanut production volume between the years 2016 and 2030 with the help of Autoregressive Integrated Moving Average (ARIMA) model using data from 1950-2015 period in Turkey.…”

Section: Literature Reviewmentioning

confidence: 89%

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Econometric Modeling for High Impact Sustainable Organic Tea Production: The Box-Jenkins Approach

Chandra

2023

AJEBA

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India is world’s second leading tea producer, and biggest consumers in the worldwide. The main objective of this investigation is to identify the Box-Jenkins method an Autoregressive integrated moving average (ARIMA) models that can be used to make predictions the production and yield of tea in India. In this study considered the published yearly secondary data of tea production and yield in India period of 1970-71 to 2021-22. In accordance to the Sigma square, RMSE, MAPE, MAE, AIC and SIC the most appropriate models for prediction the tea production, and yield in India are ARIMA models (1, 1, 1) and (0, 1, 1). Acceptability of the selected models has been verified with the Ljung-Box test, ACF, PACF criterion, and white noise followed by residual diagnostics. Comparison between the original data series and the estimated data series in the similar manner indications that the fitted model performs good statistically and is suitable for forecasting 19 year (period of 2022-23 to 2040-41) the tea production and yield in India i.e., the models are good prediction during and beyond the forecasting period; and we have estimated that the annual amount of tea production and yield achieved in the year 2022-23 is 13500.17 lakh tonnes and 2119.73 kg/ha respectively, which will accelerate to reach 16728.34 lakh tonnes and 2444.34 kg/ha in the year 2041-41 respectively.

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Section: Literature Reviewmentioning

confidence: 89%

“…The ARIMA model is mainly expressed by the following 3 terms: p, d, q. Box and Jenkins, [31,[32][33][34][35][36][37][38][39][40][41][42][43]; Gujarati et al, [27].…”

Section: An Autoregressive Integrated Moving Average (Arima) Processmentioning

confidence: 99%

Econometric Modeling for High Impact Sustainable Organic Tea Production: The Box-Jenkins Approach

Chandra

2023

AJEBA

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“…The reg-ARIMA model (3) can be thought of either as generalizing the pure ARIMA model (1) to allow a regression mean function 𝛴Ó 𝛽 𝑥 , ), or as generalizing the regression model (2) to allow the errors 𝑍 to follow the ARIMA model (1) (Balogh, 2007;Adams., Zubair & Aiyedun-Olatunde 2022). In any case, the reg-ARIMA model implies that first the regression effect is subtracted from 𝑌 to get the zero mean series 𝑍 , then the error series 𝑍 is differenced to get a stationary series, say 𝑤 , and 𝑤 is then assumed to follow the stationary ARIMA model,…”

Section: X-12 Arima Model Specificationsmentioning

confidence: 99%

“…Equation (4) emphasizes that the regression variables 𝑥 , in the reg-ARIMA model, as well as the series𝑌 , are differenced by the ARIMA model differencing operator(Ι − Β) (Ι − Β ) . Notice that the reg-ARIMA model as written in (3) assumes that the regression variable 𝑥 , affect the dependent series𝑌 , only at concurrent time points, i.e., model (3) does not explicitly provide for lagged regression effects such a𝛽 𝑥 , lagged effects can be included in the X-12-ARIMA program (Balogh, 2007).…”

Section: ø(𝐵)𝛷(𝐵 )𝑤 = 𝛩(𝐵)𝜌(𝐵 )𝑎mentioning

confidence: 99%

Predictive Modeling of Nigeria’s Currency in Circulation Using X-12 Autoregressive Integrated Moving Average Method

Bamanga

Adams

2023

DUTCH J FINANCE MANA

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In Nigeria, the average monthly quantity of currency in circulation (CIC) has increased by 269 billion nairas, reaching 2.13 trillion as of 2019 and 2.41 trillion as of 2020. The current value of currency in circulation is expected to be 2.88 trillion naira. The economy of Nigeria is impacted by the seasonal fluctuations in its currency, and it is unavoidable that the economy would need to be adjusted. The purpose of this study was to adjust the seasonal effect of eight days to Easter and Muslim holidays on CIC, model and predict the CIC in Nigeria using the United State Census Bureau's X-12 ARIMA Seasonal adjustment software. The data utilized in the study was the monthly amount of money in circulation that was taken from the Central Bank of Nigeria (CBN) Bulletin between January 2012 and March 2022. Natural logarithm was used to standardize the data, and series seasonality was removed using seasonal differencing. Based on these data, it is clear that X-12-ARIMA (2 1 1)(0 1 1) is the most accurate forecasting approach for Nigeria's CIC. The money in circulation in Nigeria from April 2022 through December 2022 will rise at a positive rate of 2.8% growth rate each month, with a predicted monthly mean CIC of 3.40 trillion by the end of the year 2022, according to this method's predictions. This is the first study on modeling and forecast of CIC in Nigeria that have utilize the United State Census Bureau X-12-ARIMA software, the findings can be extrapolated to the coming year, Nigerians may want to get ready for an increase in the amount of money in circulation during this time.

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“…The number of tourists' arrivals can be considered a time series process due to the consistent change over time and therefore, the prediction model may be applied. Tourists' arrival data get more attention in several studies (see, [38][39][40]). In this study, the following three tourists' arrival datasets are considered to implement and justify the proposed hybrid model's competency over the other models.…”

Section: Figure 1: Hybrid Sarfima-ann Model 6 Application and Empiric...mentioning

confidence: 99%

A New Hybrid SARFIMA-ANN Model for Tourism Forecasting

Saba¹,

Shahzad²,

Iqbal³

et al. 2022

Computers, Materials &Amp; Continua

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Many countries developed and increased greenery in their country sights to attract international tourists. This planning is now significantly contributing to their economy. The next task is to facilitate the tourists by sufficient arrangements and providing a green and clean environment; it is only possible if an upcoming number of tourists' arrivals are accurately predicted. But accurate prediction is not easy as empirical evidence shows that the tourists' arrival data often contains linear, nonlinear, and seasonal patterns. The traditional model, like the seasonal autoregressive fractional integrated moving average (SARFIMA), handles seasonal trends with seasonality. In contrast, the artificial neural network (ANN) model deals better with nonlinear time series. To get a better forecasting result, this study combines the merits of the SARFIMA and the ANN models and the purpose of the hybrid SARFIMA-ANN model. Then, we have used the proposed model to predict the tourists' arrival in New Zealand, Australia, and London. Empirical results showed that the proposed hybrid model outperforms in predicting tourists' arrival compared to the traditional SARFIMA and ANN models. Moreover, these results can be generalized to predict tourists' arrival in any country or region with a complicated data pattern.

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Forecasting with X-12-ARIMA: International tourist arrivals to India and Thailand

Cited by 5 publications

References 9 publications

Econometric Modeling for High Impact Sustainable Organic Tea Production: The Box-Jenkins Approach

Econometric Modeling for High Impact Sustainable Organic Tea Production: The Box-Jenkins Approach

Predictive Modeling of Nigeria’s Currency in Circulation Using X-12 Autoregressive Integrated Moving Average Method

A New Hybrid SARFIMA-ANN Model for Tourism Forecasting

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