Forecasting Tourist Visits Using Seasonal Autoregressive Integrated Moving Average Method

This paper investigates the application of the Autoregressive Integrated Moving Average (ARIMA) model to predict future trends in Chinese housing prices. The Chinese real estate market, characterized by its volatility, especially during the post-COVID-19 period, presents a complex environment for buyers and investors. The paper investigates how the ARIMA model is employed to make informed predictions in this uncertain market. Although it has some limitations, such as a heavy reliance on historical data and insensitivity to unexpected macroeconomic shifts, the ARIMA model offers a structure for understanding and anticipating housing price trends. The paper integrates various data sources, including long-term housing price statistics, to build a comprehensive ARIMA model tailored to the nuances of China’s housing market. This essay demonstrates the ARIMA model's utility in aiding stakeholders to make more confident and informed decisions in an increasingly complex and unpredictable market. The analysis further suggests refinements to the ARIMA model, considering the multifaceted nature of the housing market, influenced by macroeconomic factors and public perception. The final goal is to enhance the model's accuracy and reliability, making it an indispensable tool in economic and policy decision-making in China's evolving real estate landscape.

Stock market volatility is important for investment, option pricing and financial market regulation. In recent years, stock market analysis and prediction have the greatest significance for many professionals in the fields of finance and stock exchange. There are many methods available in the literature to solve the problem of future prediction. The present study provides a detailed comparison of Single Exponential Smoothing (SES) model and Autoregressive Integrated Moving Average (ARIMA) model. Future values are forecasting using SES and ARIMA model. Forecasted values for different iś values are calculated from SES method and ARIMA (0, 2, 3). Also, Mean Square Error (MSE), Mean Absolute Deviation (MAD), Root Mean Square (RMSE) and Mean Absolute Percentage Error (MAPE) are calculated individually for both methods.

Background: This study intends to identify the best model for predicting the incidence of hand, foot and mouth disease (HFMD) in Ningbo by comparing Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM) models combined and uncombined with exogenous meteorological variables. Methods: The data of daily HFMD incidence in Ningbo from January 2014 to November 2017 were set as the training set, and the data of December 2017 were set as the test set. ARIMA and LSTM models combined and uncombined with exogenous meteorological variables were adopted to fit the daily incidence of HFMD by using the data of the training set. The forecasting performances of the four fitted models were verified by using the data of the test set. Root mean square error (RMSE) was selected as the main measure to evaluate the performance of the models. Results: The RMSE for multivariate LSTM, univariate LSTM, ARIMA and ARIMAX (Autoregressive Integrated Moving Average Model with Exogenous Input Variables) was 10.78, 11.20, 12.43 and 14.73, respectively. The LSTM model with exogenous meteorological variables has the best performance among the four models and meteorological variables can increase the prediction accuracy of LSTM model. For the ARIMA model, exogenous meteorological variables did not increase the prediction accuracy but became the interference factor of the model. Conclusions: Multivariate LSTM is the best among the four models to fit the daily incidence of HFMD in Ningbo. It can provide a scientific method to build the HFMD early warning system and the methodology can also be applied to other communicable diseases.

Under the background of mass entrepreneurship and innovation, it is of great practical and theoretical significance to predict the Shenzhen Growth Enterprise Market (GEM) price index. In view of the nonlinearity and the non-stationarity of GEM price index a prediction method based on empirical mode decomposition (EMD) and Autoregressive Integrated Moving Average (ARIMA) model is studied. Firstly, the EMD is used to stabilize GEM price index data to make GEM price index data more regular and improve the non-linear and non-stationary characteristics of GEM price index data. Then, the ARIMA model is used to model and predict the decomposed data. The model accuracy evaluation index results of the EMD-ARIMA model in this paper are lower than that of the ARIMA model and the average error rate of the prediction results is also lower than the ARIMA model. The results show that the method proposed in this paper is more accurate than that of direct prediction by only using ARIMA model, which indicates that the EMD-ARIMA method proposed in this paper has higher generalization ability and prediction accuracy.

Batam City is one of the tourist attractions in Indonesia with the number of foreign tourist arrivals increasing every year. As one of the impacts of increasing the number of foreign tourist visits, the provincial government must improve the existing facilities in the tourism area, both in quality and quantity. In order for these facilities to be adequate to serve foreign tourists visiting Batam City in the future, it is estimated that the number of tourist visits to Batam City in the future is expected. This study aims to model foreign tourist arrivals using the SARIMA method and Neural Networks and compare the accuracy of the two methods with Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The best SARIMA model for data on the number of foreign tourist arrivals to Batam City is SARIMA (2, 1, 0)(1, 1, 0)12 with MSE = 2,672,774,359 and MAPE = 21,4487%. The Neural Network Model is ˆy = max(0, 0.03208266 + 0.48310924V1 +...+ 0.46732363V8) with MSE = 171.279.990 and MAPE = 7.1404%. Thus, modeling with Artificial Neural Networks in these cases provides a better model than SARIMA in modeling data on the number of tourist visits to Batam City.

Dengue hemorrhagic fever (DHF) is one of the most widespread and deadly diseases in several parts of Indonesia. An accurate forecast-based model is required to reduce the incidence rate of this disease. Time-series methods such as autoregressive integrated moving average (ARIMA) models are used in epidemiology as statistical tools to study and forecast DHF and other infectious diseases. The present study attempted to forecast the monthly confirmed DHF cases via a time-series approach. The ARIMA, seasonal ARIMA (SARIMA), and long short-term memory (LSTM) models were compared to select the most accurate forecasting method for the deadly disease. The data were obtained from the Surabaya Health Office covering January 2014 to December 2016. The data were partitioned into the training and testing sets. The best forecasting model was selected based on the lowest values of accuracy metrics such as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The findings demonstrated that the SARIMA (2,1,1) (1,0,0) model was able to forecast the DHF outbreaks in Surabaya City compared to the ARIMA (2,1,1) and LSTM models. We further forecasted the DHF cases for 12 month horizons starting from January 2017 to December 2017 using the SARIMA (2,1,1) (1,0,0), ARIMA (2,1,1), and LSTM models. The results revealed that the SARIMA (2,1,1) (1,0,0) model outperformed the ARIMA (2,1,1) and LSTM models based on the goodness-of-fit measure. The results showed significant seasonal outbreaks of DHF, particularly from March to September. The highest cases observed in May suggested a significant seasonal correlation between DHF and air temperature. This research is the first attempt to analyze the time-series model for DHF cases in Surabaya City and forecast future outbreaks. The findings could help policymakers and public health specialists develop efficient public health strategies to detect and control the disease, especially in the early phases of outbreaks.

A single ARIMA model cannot meet higher standards of prediction accuracy. Moreover, it can only deal with small prediction periods in the forecasting work. For the sake of prediction accuracy, we combined an ARIMA model with BP neural network. Firstly, an ARIMA forecasting model is established. Secondly the BP neural network is used to improve the single ARIMA model. The residual of ARIMA model is trained and fitted by BP neural network. Finally, more accurate results are given through combination with the forecast results of ARIMA model. The practice turns out that, compared with single ARIMA model, the prediction accuracy of new ARIMA model improved by BP neural networks is obviously enhanced, with an average error of forecast decreasing 10.4% by a large margin. Thus, the combined model proposed by this paper can be used in future prediction researches and industrial data analysis.

The ever-increasing amount of network traffic generated by various devices and applications has made it crucial to have efficient methods for analyzing and managing network traffic. Traditional approaches, such as statistical modeling, have yet to be proven enough due to network traffic's complex nature and dynamic characteristics. Recent research has shown the effectiveness of complex network analysis techniques for understanding network traffic patterns. This paper proposes multilayer seasonal autoregressive integrated moving average models for analyzing and predicting network traffic. This approach considers the seasonal patterns and interdependencies between different layers of network traffic, allowing for a more accurate and comprehensive representation of the data. The Multilayer Seasonal Autoregressive Integrated Moving Average (MSARIMA) model consists of multiple layers, each representing a different aspect of network traffic, such as time of day, day of week, or type of traffic. Each layer is modeled separately using SARIMA, a popular time series forecasting technique. The models for different layers are combined to capture the overall behavior of network traffic. The proposed approach has several benefits over traditional statistical approaches. It can capture network traffic's complex and dynamic nature, including short-term and long-term seasonal patterns. It also allows for the detection of anomalies and the prediction of future traffic patterns with high accuracy.

A hybrid methodology is presented to predict equivalent circulating density (ECD), which combines autoregressive integrated moving average (ARIMA) and back propagation neural network (BPNN) models. Research results are compared to previously published ECD prediction method that is based on theoretical calculation of hydraulic parameters. The hybrid methodology is based on data analysis theory. It uses ARIMA model to capture the linear trends of ECD, and then the BP neural network is used to predict the nonlinear and stochastic change law of ECD. Finally BP neural network prediction results are used to correct the prediction error of ARIMA to get the ECD prediction results. With a deepwater well in the South China Sea, a simulation experiment is carried out to verify the comprehensiveness and accuracy of the method presented in this paper. The prediction results are similar to those of the traditional hydraulics model, which considers the effect of temperature and pressure on drilling fluid density and rheological parameters. Comparisons are also provided for three classical time series prediction models, including the support vector machine, multiple linear regression and grey prediction. The root mean square error (RMSE) and the mean absolute deviation (MAD) were selected as evaluation indicators. The comparison results show that the hybrid model can reflect the variation law of ECD more accurately. Therefore, relative to the traditional methods of prediction, the hybrid methodology has the advantages of advanced modeling thought and simple operation, and it can be selected for prediction of ECD. Because of the effects of high temperature, high pressure and uncertain factors, the accurate prediction of ECD is very difficult. This paper provides a novel idea for accurately predicting ECD by analyzing the implicit relationship of ECD series data through data mining.

Onions, a significant spice crop in Bangladesh, frequently experience price surges due to an imbalance between demand and supply, compelling the government to import onions. The volatility of the onion market in Bangladesh and the measures needed to stabilize the situation require intensive policy attention. Therefore, this study was carried out to forecast the onion production over time and to evaluate the efficacy of various trend models. The secondary data was collected from the Food and Agriculture Organization's Statistical Database (FAOSTAT) from 1972 to 2022. The autoregressive integrated moving average (ARIMA) model was used to predict the onion production over time. Additionally, metrics such as modified and the coefficient of determination were used to evaluate the effectiveness of eight trend models comprehensively. The findings indicate that the cubic model is superior to others since it has larger and adjusted values. The models' accuracy is also determined utilizing mean absolute percentage error (MAPE) and mean absolute deviation (MAD) metrics. The cubic trend model exhibits lower MAPE and MAD values of 29.43% and 80638.67, respectively. Therefore, it is concluded that the ARIMA (2, 2, 2) model is the best model for forecasting the annual production of onion in Bangladesh. A five-year trend analysis spanning from 2023-2027 indicates an upward trend. Such a result offers a critical insight for stakeholders and policymakers in formulating sustainable onion production strategies. J Bangladesh Agril Univ 22(3): 386-395, 2024

Hotspots are indicators of forest fires that detect a location that has a relatively higher temperature than the surrounding temperature. Hotspots are usually used as indicators or forest and land fires in an area, so the more hotspots, the more potential land fires occur in an area. Although not always more and more hotspots in an area the more the potential for fires. But the hotspot point can indeed be used to identify the initial occurrence of forest and land fires. Pekanbaru Meteorological Station owned by the Meteorology, Climatology and Geophysics Agency (BMKG) via NASA's Terra and Aqua remote sensing satellite detected 103 hotspots indicating forest and land fires in Riau Province, 97 of which are located in Rokan Hilir District. Therefore, based on these problems, the hotspots forecasting model research will be conducted to prevent the expansion of forest and land fires. Forecasting time series uses historical data to predict future data. The time series forecasting model used in this study applies the Box-Jenkins procedure to build ARIMA models that are compatible with the data and applies the ARIMA models obtained to make predictions of the occurrence of hotspots in July - December 2019. Based on the research results, the best model is obtained. ARIMA (2,0,2) with non-zero mean with AICc value of 408.62. Model evaluation is used to determine the accuracy of the ARIMA model (2,0,2) in forecasting by looking at the error value. Obtained the Mean Absolute Error of 3.766891 from the test results, so that the accuracy of the model in forecasting is 96.23%.

Sugarcane is a vital commercial crop in Eastern Uttar Pradesh, contributing significantly to both the agricultural and industrial sectors. This study utilizes the ARIMA model to forecast the area, production, and yield of sugarcane in the region, aiming to address the challenges posed by yield variability due to factors such as climate change and market fluctuations. Historical data from 1960 to 2022, covering 15 districts, was analyzed using time series methods. The ARIMA (p,d,q) model, with its autoregressive and moving average components, was employed to project trends up to 2035. The average area under sugarcane cultivation over this period was 343.46 thousand hectares, with a compound annual growth rate (CAGR) of 0.69%. The production reached an average of 12,633.05 thousand tonnes, while the yield averaged 44.81 tonnes per hectare. The ARIMA model demonstrated strong predictive capability, as evidenced by performance metrics such as RMSE and R2, validating its suitability for agricultural forecasting. The findings highlight the need for data-driven planning to enhance sugarcane productivity and economic sustainability, providing valuable insights for policymakers, farmers, and industry stakeholders in mitigating risks and optimizing resources.

Governments across the world rely on their Customs Administration to provide functions that include border security, intellectual property rights protection, environmental protection, and revenue mobilisation amongst others. Analyzing the trends in revenue being collected from Customs is necessary to direct government policies and decisions. Models that can capture the trends being purported from the nominal (nonreal) tax values with respect to the trade volumes (value) over the period are indispensable. Predominant amongst the existing models are the econometric models (the GDP-based model, the monthly receipts model, and the microsimulation model), which are laborious and sometimes unreliable when studying trends in time series data. In this study, we modelled monthly revenue data obtained from the Ghana Revenue Authority-Customs Division (GRA-CD) for the period January 2010 to December 2019 using two traditional time series models, ARIMA model and ARIMA Error Regression Model (ARIMAX), and two machine learning time series models, Bayesian Structural Time Series (BSTS) model and a Neural Network Autoregression model. The Neural Network Autoregression model of the form NNAR (1, 3) provided the best forecasts with the least Mean Squared Error (MSE) of 53.87 and relatively lower Mean Absolute Percentage Error (MAPE) of 0.08. Generally, the machine learning models (NNAR (1, 3) and BSTS) outperformed the traditional time series models (ARIMA and ARIMAX models). The forecast values from the NNAR (1, 3) indicated a potential decline in revenue and this emphasizes the need for relevant authorities to institute measures to improve revenue generation in the immediate future.

Planning the future blood collection efforts must be based on adequate forecasts of transfusion demand. In this study, univariate time-series methods were investigated for their performance in forecasting the monthly demand for RBCs at one tertiary-care, university hospital. Three time-series methods were investigated: autoregressive integrated moving average (ARIMA), the Holt-Winters family of exponential smoothing models, and one neural-network-based method. The time series consisted of the monthly demand for RBCs from January 1988 to December 2002 and was divided into two segments: the older one was used to fit or train the models, and the younger to test for the accuracy of predictions. Performance was compared across forecasting methods by calculating goodness-of-fit statistics, the percentage of months in which forecast-based supply would have met the RBC demand (coverage rate), and the outdate rate. The RBC transfusion series was best fitted by a seasonal ARIMA(0,1,1)(0,1,1)(12) model. Over 1-year time horizons, forecasts generated by ARIMA or exponential smoothing laid within the +/- 10 percent interval of the real RBC demand in 79 percent of months (62% in the case of neural networks). The coverage rate for the three methods was 89, 91, and 86 percent, respectively. Over 2-year time horizons, exponential smoothing largely outperformed the other methods. Predictions by exponential smoothing laid within the +/- 10 percent interval of real values in 75 percent of the 24 forecasted months, and the coverage rate was 87 percent. Over 1-year time horizons, predictions of RBC demand generated by ARIMA or exponential smoothing are accurate enough to be of help in the planning of blood collection efforts. For longer time horizons, exponential smoothing outperforms the other forecasting methods.

Gastronomy as an element of attraction in a tourist destination: the case of Lima, Peru