Hybrid Model of Seasonal ARIMA-ANN to Forecast Tourist Arrivals through Minangkabau International Airport

Yollanda, Mutia; Devianto, Dodi

doi:10.4108/eai.2-8-2019.2290473

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…The Aravalli, Ganges, Brahmaputra, and Indus are four significant river systems in India, each with a substantial catchment area and drainage density. All of these river systems have several tributaries that run the length and width of India, making it more vulnerable to flooding [67]. Consequently, during the years 1987 and 1993, the cycle of floods followed by severe drought and its impact on water shortage in Chennai was at its peak.…”

Section: Discussionmentioning

confidence: 99%

Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study

et al. 2022

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Reservoir water level (RWL) prediction has become a challenging task due to spatio-temporal changes in climatic conditions and complicated physical process. The Red Hills Reservoir (RHR) is an important source of drinking and irrigation water supply in Thiruvallur district, Tamil Nadu, India, also expected to be converted into the other productive services in the future. However, climate change in the region is expected to have consequences over the RHR’s future prospects. As a result, accurate and reliable prediction of the RWL is crucial to develop an appropriate water release mechanism of RHR to satisfy the population’s water demand. In the current study, time series modelling technique was adopted for the RWL prediction in RHR using Box–Jenkins autoregressive seasonal autoregressive integrated moving average (SARIMA) and artificial neural network (ANN) hybrid models. In this research, the SARIMA model was obtained as SARIMA (0, 0, 1) (0, 3, 2)12 but the residual of the SARIMA model could not meet the autocorrelation requirement of the modelling approach. In order to overcome this weakness of the SARIMA model, a new SARIMA–ANN hybrid time series model was developed and demonstrated in this study. The average monthly RWL data from January 2004 to November 2020 was used for developing and testing the models. Several model assessment criteria were used to evaluate the performance of each model. The findings showed that the SARIMA–ANN hybrid model outperformed the remaining models considering all performance criteria for reservoir RWL prediction. Thus, this study conclusively proves that the SARIMA–ANN hybrid model could be a viable option for the accurate prediction of reservoir water level.

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Section: Discussionmentioning

confidence: 99%

Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study

et al. 2022

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“…The results obtained that the accuracy of the SARIMA -SVM hybrid is better than with each method-another study hybrid ARIMA with ANN for forecasting pollution index in cities in Southeast Asia [14]. Furthermore, the study used the same method to predict tourists arriving at Minangkabau international airport [15]. Moreover, for modeling and evaluating the data from a residential district in Berlin [16].…”

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confidence: 99%

The mortality modeling of covid-19 patients using a combined time series model and evolutionary algorithm

Tahyudin

Wahyudi

Maulana

et al. 2022

Int. J. Adv. Intell. Informatics

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COVID-19 pandemics for as long as two years ago since 2019 gives many insights into various aspects, including scientific development. One of them is the fundamental research of computer science. This research aimed to construct the best model of COVID-19 patients’ mortality and obtain less prediction errors. We performed the combination methods of time series, SARIMA, and Evolutionary algorithm, PARCD, to predict male patients who died because of COVID-19 in the USA, containing 1.008 data. So, this research proposed that SARIMA-PARCD has a powerful combination for addressing the complex problem in a dataset. The prediction error of SARIMA-PARCD was compared with other methods, i.e., SARIMA, LSTM, and the combination of SARIMA-LSTM. The result showed that the SARIMA-PARCD has the smallest MSE value of 0.0049. Therefore, the proposed method is competitive to implement in other cases with similar characteristics. This combination is robust for solving linear and non-linear problems.

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“…Neural network models could be a potential alternative to the traditional linear time series models. Recently, many studies have integrated time series analysis and neural network framework together, a combination of Seasonal Autoregressive Integrated Moving Average (SARIMA) and Nonlinear Autoregressive Neural Network (NARNN) model, in medical sciences [3] [4] [5] [6], business [7], tourism [8]. From these studies reported, this hybrid model can explore the reliable model to forecast the time series for a better performance.…”

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confidence: 99%

Time Series Forecasting of Global Price of Soybeans using a Hybrid SARIMA and NARNN Model

Nain

2021

Data Science: J. of Computing and Appl. Informatics

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Global price of soybeans has a big impact because of the trade war between the U.S. and China. Under this circumstance, price forecast is vital to facilitate efficient decisions and will play a major role in coordinating the supply and demand of soybeans globally. Hence, the primary purpose of this study was to demonstrate the role of time series models in predicting process using the time series data of monthly global price of soybeans from January 1990 to January 2021. The SARIMA and NARNN models are good at modelling linear and nonlinear problems for the time series, respectively. However, using the hybrid model, a combination of the SARIMA and NARNN models has both linear and nonlinear modelling capabilities, can be a better choice for modelling the time series. The comparative results revealed that the Hybrid-LM model with 8 neurons in the hidden layer and 3 time delays yielded higher accuracy than the NARNN-LM model with 8 neurons in the hidden layer and 3 time delays, and the SARIMA, ARIMA(0,1,3)(0,0,2)12, model, according to its lowest MSE in this study. Thus, this study may provide an integrated modelling approach as a decision-making supportive method for formulating price forecast of soybeans for the global soybean market.

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Hybrid Model of Seasonal ARIMA-ANN to Forecast Tourist Arrivals through Minangkabau International Airport

Cited by 6 publications

References 20 publications

Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study

Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study

The mortality modeling of covid-19 patients using a combined time series model and evolutionary algorithm

Time Series Forecasting of Global Price of Soybeans using a Hybrid SARIMA and NARNN Model

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