Yazar "Danbatta, Salim Jibrin" seçeneğine göre listele

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    ANN-polynomial-Fourier series modeling and Monte Carlo forecasting of tourism data
    (Wiley, 2022) Danbatta, Salim Jibrin; Varol, Asaf
    Modeling and forecasting of tourism data have received attention in the past decades. Turkey is one of the countries that benefit significantly from the tourism industry. Several time-series models have been recommended to best describe tourist arrivals to Turkey. However, in the 21st century, the world experiences great uncertainty in most possible event outcomes. These uncertainties are very difficult to account for. We proposed a hybrid artificial neural network (ANN)-polynomial-Fourier method to model the number of foreign visitors to Turkey from January 2004 to December 2020. The proposed model performance before and during the COVID-19 pandemic is evaluated separately. We evaluate the model performance by comparing with results from Danbatta and Varol (2021, ), Fourier series, and ARIMA models. To account for prediction uncertainties, we ran 300 Monte Carlo simulations within +/- 2 sigma from the model regression curve. According to the result outcomes, the proposed ANN-polynomial-Fourier has proven worthy to be considered a candidate model for the Turkish tourism data. The multistep ahead forecast suggests a 10.22% increase in the monthly foreign visitors' arrivals to Turkey in the year 2021.
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    Forecasting Foreign Visitors Arrivals Using Hybrid Model and Monte Carlo Simulation
    (World Scientific Publ Co Pte Ltd, 2022) Danbatta, Salim Jibrin; Varol, Asaf
    The tourism industry is one of the important revenue sectors in today's world. Millions of visits are made monthly to different countries across the planet. Some countries host more tourists than others, depending on the availability of factors that would fascinate visitors. Tourism demand can be affected by different factors, which may include government policies, insecurity, political motive, etc. Being an important sector, policymakers/governments are keen on models that would provide an insight into the inherent dynamics of tourism in their country. Especially in forecasting future tourist arrivals, as it will greatly assist in decision making. Several tourism demand models have been presented in the literature. The best practice is to have a model that would account for uncertainty in estimations. In this paper, an ANN-Polynomial-Fourier series model is implemented to capture and forecast tourist data for Turkey, Japan, Malaysia, and Singapore. The proposed model is a combination of the artificial neural network (ANN), polynomial fitting (poly), and Fourier series fitting (Fourier). The proposed model is designed to capture the data trend component using the polynomial fitting, the data seasonal component using the Fourier series fitting, and other data anomalies using the artificial neural network. Multistep ahead forecasting is made for each of the studied tourist data, and estimation uncertainties are covered by generating multiple forecast paths (Monte Carlo forecast). According to estimations, Turkey will expect a 10.22% increase in 2021 compared to the tourist arrivals it received in 2020. Japan is expected to have a 92.42% decrease in 2021 compared to the tourist arrivals it received in 2020. Malaysia is also expected to have a 54.81% decrease in 2021 when compared to the number of tourists it received in 2020. Finally, Singapore will expect a 70.55% decrease in 2021 compared to the number of tourists it received in 2020.
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    Forecasting monthly rainfall using hybrid time-series models and Monte Carlo simulation amidst security challenges: a case study of five districts from northern Nigeria
    (Springer, 2024) Danbatta, Salim Jibrin; Muhammad, Ahmad; Varol, Asaf; Abdurrahaman, Daha Tijjani
    Nigeria's agricultural sector relies heavily on rainfall, but insecurity in various regions poses significant challenges. This study aims to address this issue by identifying secure, rain-rich areas in northern Nigeria to support sustainable agriculture. Two models, one integrating classical statistical methods (polynomial and Fourier series fittings) and another using a hybrid approach (artificial neural networks, polynomial, and Fourier series fittings), were employed to analyze historical rainfall data from 1981 to 2021 in the selected districts (Kano, Zaria, Bida, Nguru, and Yelwa) known for their rainfall levels and security stability. The study demonstrates that the machine learning-classical hybrid model outperforms existing models, including the classical-classical hybrid and benchmark models like Iwok's (2016) model, Fourier series, and SARIMA models. Multi-step ahead forecasting with this hybrid model reveals potential changes in rainfall patterns. Notably, Kano, Zaria, Bida, and Yelwa are expected to experience increased rainfall from 2022 to 2026, while Nguru may initially witness decreased rainfall, with improvement in the final year (2026). In conclusion, this study introduces an effective approach for rainfall modeling and forecasting, facilitating the identification of secure agricultural regions in northern Nigeria. These findings carry implications for crop production and agricultural development, contributing to climate resilience efforts and assisting stakeholders in strategic decision-making for regional agricultural investments.
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    Modeling and Forecasting of Tourism Time Series Data using ANN-Fourier Series Model and Monte Carlo Simulation
    (Ieee, 2021) Danbatta, Salim Jibrin; Varol, Asaf
    Tourism is counted as one of the most sensitive sectors to crises such as the COVID-19 pandemic. By the first quarter of 2020, it brought the foreign visitors' travels to a sudden and unexpected halt. This has negatively affected the tourism sector. Due to the perishable nature of the tourism industry products, many researchers are calling for urgent development and implementation of a rescue plan that will help in predicting the future number of foreign visitors. In this paper, we proposed an approach to modeling and forecasting a tourism time-series data that have both trend and seasonality. This approach is a combination of the Fourier series and artificial neural network methods to capture the seasonality and trend components in data. We applied this method to the monthly foreign visitors to Turkey dataset. We studied the data for the periods before, and during the COVID-19 pandemic. To account for uncertainties in the model prediction during the COVID-19 pandemic, we employed the Monte Carlo simulation method. We run 100 Monte Carlo simulations within +/- 2 sigma from the model curve. The mean of these 100 Monte Carlo simulation paths is computed and used for presenting the Monte Carlo forecast result values of the data. To test the feasibility of this approach, we compared the model predictions with some other existing models in the literature. In each case, the model has demonstrated a decent prediction and outperformed the benchmarked models. The proposed model produces a statistically good fit and acceptable result that can be used to forecast other tourism-related attributes.
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    Monte Carlo forecasting of time series data using Polynomial-Fourier series model
    (World Scientific Publ Co Pte Ltd, 2021) Danbatta, Salim Jibrin; Varol, Asaf
    The perishable nature of tourism products and services makes forecasting an important tool for tourism planning, especially in the current COVID-19 pandemic time. The forecast assists tourism organizations in decision-making regarding resource allocations to avoid shortcomings. This study is motivated by the need to model periodic time series with linear and nonlinear trends. A hybrid Polynomial-Fourier series model that uses the combination of polynomial and Fourier fittings to capture and forecast time series data was proposed. The proposed model is applied to monthly foreign visitors to Turkey from January 2014 to August 2020 dataset and diagnostic checks show that the proposed model produces a statistically good fit. To improve the model forecast, a Monte Carlo simulation scheme with 100 simulation paths is applied to the model residue. The mean of the 100 simulation paths within +/- 2 sigma bounds from the model curve was taken and found to give statistically acceptable results.