Seasonal Autoregressive Moving Average Research Articles

Statistical Analysis of Overseas Tourist Arrivals to South Africa in Assessing the Impact of COVID-19 on Sustainable Development

The COVID-19 pandemic has harmed the global tourism and hospitality industry, crippling foreign currency earnings and employment in many countries, South Africa (SA) included. This study aims to evaluate the impact of the COVID-19 pandemic on overseas tourist arrivals to SA, and to make an inference on the country’s foreign currency earnings on economic development. The Box–Jenkins methodology is used in fitting non-seasonal integrated autoregressive moving average (ARIMA) and seasonal ARIMA (SARIMA) models to quantify and characterise the number of overseas tourists to SA. The ARIMA 1,0,1(0,1,1)12 model is the best fitting model for the overseas tourist arrivals data to SA, as confirmed by the Akaike Information Criterion (AIC). The model shows good forecasting power in the absence of the COVID-19 pandemic, as evidenced by the validation results. The difference between forecasts and actual values after the validation phase shows the negative impact of the COVID-19 pandemic on overseas tourist arrivals to SA and the challenges it poses to the statistical modelling of tourist arrivals to SA, considering the pandemic was the first of its kind. The COVID-19 pandemic exposed the tourism industry’s vulnerability to economic shocks, showing the need for aggressive marketing strategies that may revamp the tourism sectors to levels previously expected before and or after COVID-19 for sustainable development.

MODEL COMPARISON OF VECTOR AUTOREGRESSIVE RESHAPED AND SARIMA IN SEASONAL DATA (A CASE STUDY OF TEA PRODUCTION IN PT PERKEBUNAN NUSANTARA VIII INDONESIA)

PT Perkebunan Nusantara VIII (PTPN VIII) is a State-Owned Enterprise (BUMN). It operates in the plantation sector. The leading commodity is tea. The demand for tea produced by PTPN VIII is increasing. Thus, planning tea production is necessary. One of the production planning efforts is through forecasting based on previous data. Tea production data is time-series data. It contains seasonal elements and is dependent on other locations. We will analyze data with these criteria using space-time models, one of which is vector autoregressive (VAR). VAR models the relationship between observations on certain variables at one time. It also models the observation of the variable itself at previous times. Additionally, VAR models the relationship between observations and other variables at previous times. This paper explains how to forecast tea production. It uses the reconstituted VAR and Seasonal Autoregressive Moving Average (SARIMA) models. The results showed that the reconstituted VAR model was better than the SARIMA model in predicting tea production. The tea production prediction was at the Sedep and Santosa plantations in Bandung Regency.

A time series analysis of monthly issuance of plant import permits in Nigeria

An import permit is issued by the National Plant Protection Organization of each country, known in Nigeria as the Nigeria Agricultural Quarantine Service (NAQS), to allow the importation of pest-free plants and prevent the introduction of quarantine pests or check the entry of regulated non-quarantine pests of imported plant commodities. Issuance of import permits at the Post-Entry Quarantine, Surveillance and Diagnostic Station (PEQDS), Ibadan is mostly for germplasm materials mainly used for cultivation, which poses a high risk of pest introduction. Thus, this study investigated the statistical components of the issuance of import permits at the PEQDS using the Time Series approach to determine the peak period of collection essential for planning phytosanitary activities to mitigate the introduction of foreign pests. The dataset of the monthly issuance of import permits from January 2012 to July 2022 was analysed. From the preliminary study, we observed that the series exhibit seasonal and stationary components with no definite trend pattern, hence we modelled the series using a Seasonal Autoregressive Moving Average (SARIMA) model. Several models were estimated and the best model (SAR(12)) for forecasting the future import permit collectionwas selected based on the forecast accuracy measures. The forecasted values indicated the highest issuance of import permits in May and September, each year and showed a fluctuating movement over time in the future. To further prevent pest incursion and facilitate trade, NAQS needs to operate round-the-clock surveillance, keep its staff on high alert and adequate facilities operational for more effective activities. Other factors responsible for low and fluctuating issuance of import permits should also be investigated.

Time series analysis and development of simulation model for monthly rainfall using ARIMA model

Rainfall holds critical significance for water resource applications, particularly in rainfed agricultural systems. This study employs the Autoregre ssive Integrated Moving Average (ARIMA) technique, a data mining approach commonly used for time series analysis and future forecasting. Given the increasing importance of climate change forecasting in averting unexpected natural hazards such as floods, frost, forest fires, and droughts, accurate weather data forecasting becomes imperative. The objective of this study was to develop a Seasonal Auto-Regressive Integrative Moving Average (SARIMA) model for forecasting monthly rainfall in Junagadh Station, Gujarat. Utilizing 50 years of historical data (1968 to 2016), the SARIMA model predicts weekly rainfall for the subsequent five years (2018 to 2022). Through comprehensive evaluation using ACF and PACF plots, AIC, SBC, MAPE, and MAE values, the study identifies SARIMA (1,0,0)(3,1,1)12 as the optimal model, offering the most accurate prediction. The robust results affirm that the SARIMA model provides reliable and satisfactory weekly rainfall predictions. This research contributes valuable insights into the precision and efficacy of SARIMA models for rainfall forecasting, aiding in strategic water resource management in the Junagadh region.

Stochastic Time Series Analysis, Modeling, and Forecasting of Weekly Rainfall Using Sarima Model

Rainfall holds critical significance for water resource applications, particularly in rainfed agricultural systems. This study employs the Autoregressive Integrated Moving Average (ARIMA) technique, a data mining approach commonly used for time series analysis and future forecasting. Given the increasing importance of climate change forecasting in averting unexpected natural hazards such as floods, frost, forest fires, and droughts, accurate weather data forecasting becomes imperative. The objective of this study was to develop a Seasonal Auto-Regressive Integrative Moving Average (SARIMA) model for forecasting weekly rainfall in Junagadh Station, Gujarat. Utilizing 53 years of historical data (1963 to 2016), the SARIMA model predicts weekly rainfall for the subsequent five years (2018 to 2022). Through comprehensive evaluation using ACF and PACF plots, AIC, SBC, MAPE, and MAE values, the study identifies SARIMA (0,0,4)(0,1,1)52 as the optimal model, offering the most accurate prediction. The robust results affirm that the SARIMA model provides reliable and satisfactory weekly rainfall predictions. This research contributes valuable insights into the precision and efficacy of SARIMA models for rainfall forecasting, aiding in strategic water resource management in the Junagadh region.

A Comparative Analysis of Traditional SARIMA and Machine Learning Models for CPI Data Modelling in Pakistan

Background. In economic theory, a steady consumer price index (CPI) and its associated low inflation rate (IR) are very much preferred to a volatile one. CPI is considered a major variable in measuring the IR of a country. These indices are those of price changes and have major significance in monetary policy decisions. In this study, different conventional and machine learning methodologies have been applied to model and forecast the CPI of Pakistan. Methods. Pakistan’s yearly CPI data from 1960 to 2021 were modelled using seasonal autoregressive moving average (SARIMA), neural network autoregressive (NNAR), and multilayer perceptron (MLP) models. Several forms of the models were compared by employing the root mean square error (RMSE), mean square error (MSE), and mean absolute percentage error (MAPE) as the key performance indicators (KPIs). Results. The 20-hidden-layered MLP model appeared as the best-performing model for CPI forecasting based on the KPIs. Forecasted values of Pakistan’s CPI from 2022 to 2031 showed an astronomical increase in value which is unpleasant to consumers and economic management. Conclusion. The increasing CPI trend observed if not addressed will trigger a rising purchasing power, thereby causing higher commodity prices. It is recommended that the government put vibrant policies in place to address this alarming situation.

Predicting Potential Evapotranspiration for Kalaburagi District using a Seasonal Arima Model

Forecasting potential evapotranspiration (PET) is of great importance in effectively managing irrigation systems. This article centers around models designed to simulate future PET levels for the Kalaburagi district. The study calculates potential evapotranspiration using temperature data in degrees Celsius, employing the Thornthwaite method, and prediction is performed using the Seasonal Autoregressive Moving Average (SARIMA) method. These models are developed based on autocorrelation function (ACF) and partial autocorrelation function (PACF) analysis. Model selection is based on minimizing Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) values. The chosen models for different stations in Kalaburagi, Chincholi, Sedam, Chittapur, Aland, Jewargi, and Afzalpur respectively are SARIMA (1,0,1)(2,1,0)12, SARIMA(1,0,1)(2,1,0)12, SARIMA(1,0,0)(2,1,0)12, SARIMA(1,0,1)(2,1,0)12, SARIMA (1,0,1) (2,1,0) 12, and SARIMA(1,0,1)(2,1,0)12. The results indicate that the models developed for Jewargi and Chincholi stations show particular promise compared to the other two stations, with all four models performing well. These models have the potential to significantly enhance decision-making in irrigation planning and command area management practices, contributing to improved water resource management.

PREDIKSI JUMLAH PEMAKAIAN AIR BERSIH MENGGUNAKAN METODE HYBRID SINGULAR SPECTRUM ANALYSIS (SSA) DAN SARIMA DI PDAM TIRTANADI SIBOLANGIT

Clean water is one of the basic human needs that is needed on an ongoing basis. Water has an important role in human life. As the population increases, the use of clean water also increases, resulting in the need for demand for the availability of clean water to continue to increase, this is explained by data from the Central Statistics Agency (2022) that the population growth rate in Sibolangit District from 2021-2022 is 0.25% and that This is also explained by data on the amount of clean water consumption by PDAM Tirtanadi Sibolangit which has increased by 6.5%. The purpose of this study is to apply the SSA-SARIMA hybrid model to predict the amount of clean water consumption in the coming period at PDAM Tirtandi Sibolangit so that there is no shortage and wastage of clean water. Hybrid SSA-SARIMA is a combination of two data analysis methods that take advantage of the advantages of each method, namely Singular Spectrum Analysis (SSA) and Seasonal Autoregressive Moving Average (SARIMA). SSA is a technique used to separate signals into periodic and non-periodic components while SARIMA is used to model time series data with trend and seasonal patterns and make predictions for future periods. SARIMA cannot separate periodic and non-periodic signals in data like SSA did. The data used in this study is monthly data on clean water usage from January 2018 to December 2022. The prediction results for the amount of clean water consumption in PDAM Tirtanadi Sibolangit in 2024 use the SSA-SARIMA(1,1,0)(1,0,0) hybrid model. )12 experienced a decrease in the use of clean water with a level of forecasting accuracy having a MAPE value of 6.920446%.

Impact of the COVID-19 Pandemic on the Provision of Dialysis Service and Mortality in Veterans Receiving Maintenance Hemodialysis in the VA: An Interrupted Time-Series Analysis

Introduction: According to the US Renal Data System (USRDS), patients with end-stage kidney disease (ESKD) on maintenance dialysis had higher mortality during early COVID-19 pandemic. Less is known about the effect of the pandemic on the delivery of outpatient maintenance hemodialysis and its impact on death. We examined the effect of pandemic-related disruption on the delivery of dialysis treatment and mortality in patients with ESKD receiving maintenance hemodialysis in the Veterans Health Administration (VHA) facilities, the largest integrated national healthcare system in the USA. Methods: Using national VHA electronic health records data, we identified 7,302 Veterans with ESKD who received outpatient maintenance hemodialysis in VHA healthcare facilities during the COVID-19 pandemic (February 1, 2020, to December 31, 2021). We estimated the average change in the number of hemodialysis treatments received and deaths per 1,000 patients per month during the pandemic by conducting interrupted time-series analyses. We used seasonal autoregressive moving average (SARMA) models, in which February 2020 was used as the conditional intercept and months thereafter as conditional slope. The models were adjusted for seasonal variations and trends in rates during the pre-pandemic period (January 1, 2007, to January 31, 2020). Results: The number (95% CI) of hemodialysis treatments received per 1,000 patients per month during the pre-pandemic and pandemic periods were 12,670 (12,525–12,796) and 12,865 (12,729–13,002), respectively. Respective all-cause mortality rates (95% CI) were 17.1 (16.7–17.5) and 19.6 (18.5–20.7) per 1,000 patients per month. Findings from SARMA models demonstrate that there was no reduction in the dialysis treatments delivered during the pandemic (rate ratio: 0.999; 95% CI: 0.998–1.001), but there was a 2.3% (95% CI: 1.5–3.1%) increase in mortality. During the pandemic, the non-COVID hospitalization rate was 146 (95% CI: 143–149) per 1,000 patients per month, which was lower than the pre-pandemic rate of 175 (95% CI: 173–176). In contrast, there was evidence of higher use of telephone encounters during the pandemic (3,023; 95% CI: 2,957–3,089), compared with the pre-pandemic rate (1,282; 95% CI: 1,241–1,324). Conclusions: We found no evidence that there was a disruption in the delivery of outpatient maintenance hemodialysis treatment in VHA facilities during the COVID-19 pandemic and that the modest rise in deaths during the pandemic is unlikely to be due to missed dialysis.

Predictive Modeling for Forecasting Air Quality Index (AQI) Using Time Series Analysis

Air pollution is a widespread problem in India. The study focuses on forecasting the air quality index (AQI) using time series modeling techniques for the most polluted area of Dehradun City in Uttarakhand state, India. The train test approach of machine learning and Akaike information criterion (AIC) have been used on the monthly data of five years to select the best auto-regressive model. Using the auto-correlation functions (ACF and PACF) and the seasonality component in the time-series dataset, a seasonal auto-regressive moving average (ARMA) model with its minimum AIC has been chosen to forecast the AQI. This model is also validated by comparing its predicted values with the actual values of AQI. The results showed that the seasonal ARMA model of (1,0,0)(1,0,0)12 could forecast AQI based on a stationary dataset. The research also indicates that the asthma patients of the Himalayan Drugs-ISBT region may experience more health effects, especially in winter, due to poor air quality. The model can be helpful for a scientist and the government to take precautionary measures in advance.

Forecasting HFMD Cases Using Weather Variables and Google Search Queries in Sabah, Malaysia

HFMD is a viral-mediated infectious illness of increasing public health importance. This study aimed to develop a forecasting tool utilizing climatic predictors and internet search queries for informing preventive strategies in Sabah, Malaysia. HFMD case data from the Sabah State Health Department, climatic predictors from the Malaysia Meteorological Department, and Google search trends from the Google trends platform between the years 2010-2018 were utilized. Cross-correlations were estimated in building a seasonal auto-regressive moving average (SARIMA) model with external regressors, directed by measuring the model fit. The selected variables were then validated using test data utilizing validation metrics such as the mean average percentage error (MAPE). Google search trends evinced moderate positive correlations to the HFMD cases (r0-6weeks: 0.47-0.56), with temperature revealing weaker positive correlations (r0-3weeks: 0.17-0.22), with the association being most intense at 0-1 weeks. The SARIMA model, with regressors of mean temperature at lag 0 and Google search trends at lag 1, was the best-performing model. It provided the most stable predictions across the four-week period and produced the most accurate predictions two weeks in advance (RMSE = 18.77, MAPE = 0.242). Trajectorial forecasting oscillations of the model are stable up to four weeks in advance, with accuracy being the highest two weeks prior, suggesting its possible usefulness in outbreak preparedness.

Predicting Flood Streamflow with Auto Regressive Integrated Moving Average Models

Flooding is the most common natural disaster and continues to increase in frequency and intensity due to climate changes [7]. Currently, there is a lack of efficient tools to predict flooding. This research is aimed to create a Time Series Machine Learning (ML) program using Auto Regressive Moving Average (ARIMA) models to forecast streamflow, one of the most prominent factors in flood prediction. A streamflow dataset from the Ganges River, Bangladesh was used to plot several graphs of the river Log Volume to observe possible trends. Another plot was graphed to check and quantify how much the distribution of the stream volume changed over the course of 10 years using KL Divergence. The plot analyses and Partial Autocorrelation Function (PACF) and Autocorrelation Function (ACF) tests were used to help obtain the ARIMA parameters of (p, d, q) as (1, 1, 1). However, the forecasted streamflow of the ARIMA function was not accurate when compared with previously recorded data because of heavy seasonality. As a result, the final program was redesigned with Seasonal ARIMA (SARIMA) to account for the inaccuracy. The SARIMA model was used to forecast the streamflow of subsequent years and was close to the actual recorded data. Such accuracy indicates that this method can be a useful tool in navigating and preparing for floods.

Application of the Hybrid Forecasting Models to Road Traffic Accidents in Algeria

Road traffic accidents are a growing public health concern. In this study, we focused on analyzing and forecasting the monthly number of accidents, number of injuries, and number of deaths in Algeria over the period (2015–2020). For this purpose, hybrid forecasting models based on equal weights and in-sample errors were fitted, and we compared them with the seasonal autoregressive moving average (SARIMA) models. The three models retained for forecasting until 2022 are all hybrid models, one based on equal weight and two models based on in-sample errors (using the RMSE indicator). Furthermore, the hybrid models outperformed the SARIMA models for short (6 months), medium (12 months), and long horizon (24 months). The forecasting results showed that we expect an increase in the number of accidents, the number of deaths, and the number of injuries over the next 12 months. Policymakers must enhance strategies for prevention and road safety, especially in rural areas, where the highest rate of fatalities is recorded.

FORECASTING THE IMPORT DEMAND FOR CHEMICAL FERTILIZER IN THAILAND

This study aims to forecast the import demand for chemical fertilizer in Thailand, namely, nitrogen fertilizer, potassium fertilizer, and compound fertilizer. Secondary time series is employed, covering the period from January 2008 to December 2021, or 168 months. The forecasting technique used for the study is the seasonal autoregressive moving average (SARIMA) or the Box-Jenkins method.The empirical results show that the appropriate models for forecasting the import demand of nitrogen fertilizer, potassium fertilizer, and compound fertilizer based on the lowest value of the Akaike criterion and Schwarz criterion are SARIMA(0,0,0)(0,1,1)12, SARIMA(2,0,1)(0,1,1)12, and SARIMA(1,0,0)(2,1,0)12, respectively. The forecasts for the next 12 months (January to December 2022) reveal that the total import demand compared to the previous year for nitrogen fertilizer increased by 5.12%, potassium fertilizer decreased by 8.74%, and compound fertilizer increased by 4.74%.

Forecasting of monthly relative humidity in Delhi, India, using SARIMA and ANN models.

Relative humidity plays an important role in climate change and global warming, making it a research area of greater concern in recent decades. The present study attempted to implement seasonal autoregressive moving average (SARIMA) and artificial neural network (ANN) with multilayer perceptron (MLP) models to forecast the monthly relative humidity in Delhi, India during 2017–2025. The average monthly relative humidity data for the period 2000–2016 have been used to carry out the objectives of the proposed study. The forecast trend in relative humidity declines from 2017 to 2025. The accuracy of the models has been measured using root mean squared error (RMSE) and mean absolute error (MAE). The results showed that the SARIMA model provides the forecasted relative humidity with RMSE of 6.04 and MAE of 4.56. On the other hand, MLP model reported the forecasted relative humidity with RMSE of 4.65 and MAE of 3.42. This study concluded that the ANN model was more reliable for predicting relative humidity than SARIMA model.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40808-022-01385-8.

Talent Cultivation of New Ventures by Seasonal Autoregressive Integrated Moving Average Back Propagation Under Deep Learning.

This study combines the discovery methods and training of innovative talents, China’s requirements for improving talent training capabilities, and analyses the relationship between the number of professional enrollments in colleges and universities and the demand for skills in specific places. The research learns the characteristics and training models of innovative talents, deep learning (DL), neural networks, and related concepts of the seasonal difference Autoregressive Moving Average (ARMA) Model. These concepts are used to propose seasonal autoregressive integrated moving average back propagation (SARIMA-BP). Firstly, the SARIMA-BP artificially sets the weight parameter values and analyzes the model’s convergence speed, superiority, and versatility. Then, particle swarm optimization (PSO) algorithm is used to pre-process the model and test its independence. The accuracy of the model is checked to ensure its proper performance. Secondly, the model analyzes and predicts the relationship between the number of professional enrollments of 10 colleges and universities in a specific place and the talent demand of local related enterprises. Moreover, the established model is optimized and tested by wavelet denoising. Independent testing is done to ensure the best possible performance of the model. Finally, the weight value will not significantly affect the model’s versatility obtained by experiments. The prediction results of professional settings and corporate needs reveal that: there is a moderate correlation between professional locations and corporate needs; colleges and universities should train professional talents for local enterprises and eliminate the practical education concepts.

Estimating SPARMA Models with Dependent Error Terms

Abstract We are interested in a class of seasonal autoregressive moving average (SARMA) models with periodically varying parameters, so-called seasonal periodic autoregressive moving average (SPARMA) models under the assumption that the errors are uncorrelated but non-independent (i.e. weak SPARMA models). Relaxing the classical independence assumption on the errors considerably extends the range of application of the SPARMA models, and allows one to cover linear representations of general nonlinear processes. We establish the asymptotic properties of the quasi-generalized least squares (QLS) estimator of these models. Particular attention is given to the estimation of the asymptotic variance matrix of the QLS estimator, which may be very different from that obtained in the standard framework. A set of Monte Carlo experiments are presented.

Deterministic Chaos Detection and Simplicial Local Predictions Applied to Strawberry Production Time Series

In this work, we attempted to find a non-linear dependency in the time series of strawberry production in Huelva (Spain) using a procedure based on metric tests measuring chaos. This study aims to develop a novel method for yield prediction. To do this, we study the system’s sensitivity to initial conditions (exponential growth of the errors) using the maximal Lyapunov exponent. To check the soundness of its computation on non-stationary and not excessively long time series, we employed the method of over-embedding, apart from repeating the computation with parts of the transformed time series. We determine the existence of deterministic chaos, and we conclude that non-linear techniques from chaos theory are better suited to describe the data than linear techniques such as the ARIMA (autoregressive integrated moving average) or SARIMA (seasonal autoregressive moving average) models. We proceed to predict short-term strawberry production using Lorenz’s Analog Method.

Evaluation of prediction models for the malaria incidence in Marodijeh Region, Somaliland.

Malaria is a major public health concern in tropics and subtropics. Accurate malaria prediction is critical for reporting ongoing incidences of infection and its control. Hence, the purpose of this investigation was to evaluate the performances of different models of predicting malaria incidence in Marodijeh region, Somaliland. The study used monthly historical data from January 2011 to December 2020. Five deterministic and stochastic models, i.e. Seasonal Autoregressive Moving Average (SARIMA), Holt-Winters' Exponential Smoothing, Harmonic Model, Seasonal and Trend Decomposition using Loess (STL) and Artificial Neural Networks (ANN), were fitted to the malaria incidence data. The study employed Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Scaled Error (MASE) to measure the accuracy of each model. The results indicated that the artificial neural network (ANN) model outperformed other models in terms of the lowest values of RMSE (39.4044), MAE (29.1615), MAPE (31.3611) and MASE (0.6618). The study also incorporated three meteorological variables (Humidity, Rainfall and Temperature) into the ANN model. The incorporation of these variables into the model enhanced the prediction of malaria incidence in terms of achieving better prediction accuracy measures (RMSE = 8.6565, MAE = 6.1029, MAPE = 7.4526 and MASE = 0.1385). The 2-year generated forecasts based on the ANN model implied a significant increasing trend. The study recommends the ANN model for forecasting malaria cases and for taking the steps to reduce malaria incidence during the times of year when high incidence is reported in the Marodijeh region.

Frequency Domain Calculation of Seasonal VARMA Autocovariances

In applications of the fitting and forecasting of Seasonal Vector AutoRegressive Moving Average (SVARMA) models, it is important to quickly and accurately compute the autocovariances. Recursive time domain approaches rely on expressing the seasonal AutoRegressive matrix polynomials as a high order non-seasonal AutoRegressive matrix polynomial; initialization of the recursions is therefore costly, because a large-dimensional matrix must be inverted. However, the resulting high-order polynomial has many zeroes that are not intelligently exploited, indicating that this time domain approach is not optimal. It is proposed to compute the autocovariances via integrating the spectral density, taking advantage of analytical expressions for the inverse AutoRegressive matrix polynomials in terms of determinant and adjoint. The R and RCPP code can be hundreds of times faster than the time domain methods when the dimension and seasonal period are large.