Journal articles on the topic 'ARIMAX model'
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Amelia, R., D. Y. Dalimunthe, E. Kustiawan, and I. Sulistiana. "ARIMAX model for rainfall forecasting in Pangkalpinang, Indonesia." IOP Conference Series: Earth and Environmental Science 926, no. 1 (November 1, 2021): 012034. http://dx.doi.org/10.1088/1755-1315/926/1/012034.
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Abstract In recent years, the weather and climate are unpredictable and the most visible is the rotation of the rainy season and the dry season. The extreme changes in rainfall can cause disasters and losses for the community. For that we need to predict the rainfall to anticipate the worst events. Rainfall is included in the periodic series data, so the forecasting method that can be used is the ARIMAX model which is ARIMA model expanded by adding the exogen variable. The aim of this research is to predict the rainfall data in Pangkalpinang City, Indonesia. The best model for each rainfall is ARIMAX (0,1,3) for monthly rainfall data and ARIMAX (0,1,2) for maximum daily rainfall. This research shows that there is an influence maximum wind speed variable to monthly rainfall and maximum daily rainfall in the Pangkalpinang City. Nevertheless, when viewed from the ARIMA and ARIMAX models based on the obtained AIC value, the ARIMAX value is still better than ARIMA. However, the prediction value using ARIMAX needs to increase again to take into account seasonal data rainfall. Then, possible to add other exogeneous factors besides maximum wind speed.
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Chen, Yun-Peng, Le-Fan Liu, Yang Che, Jing Huang, Guo-Xing Li, Guo-Xin Sang, Zhi-Qiang Xuan, and Tian-Feng He. "Modeling and Predicting Pulmonary Tuberculosis Incidence and Its Association with Air Pollution and Meteorological Factors Using an ARIMAX Model: An Ecological Study in Ningbo of China." International Journal of Environmental Research and Public Health 19, no. 9 (April 28, 2022): 5385. http://dx.doi.org/10.3390/ijerph19095385.
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The autoregressive integrated moving average with exogenous regressors (ARIMAX) modeling studies of pulmonary tuberculosis (PTB) are still rare. This study aims to explore whether incorporating air pollution and meteorological factors can improve the performance of a time series model in predicting PTB. We collected the monthly incidence of PTB, records of six air pollutants and six meteorological factors in Ningbo of China from January 2015 to December 2019. Then, we constructed the ARIMA, univariate ARIMAX, and multivariate ARIMAX models. The ARIMAX model incorporated ambient factors, while the ARIMA model did not. After prewhitening, the cross-correlation analysis showed that PTB incidence was related to air pollution and meteorological factors with a lag effect. Air pollution and meteorological factors also had a correlation. We found that the multivariate ARIMAX model incorporating both the ozone with 0-month lag and the atmospheric pressure with 11-month lag had the best performance for predicting the incidence of PTB in 2019, with the lowest fitted mean absolute percentage error (MAPE) of 2.9097% and test MAPE of 9.2643%. However, ARIMAX has limited improvement in prediction accuracy compared with the ARIMA model. Our study also suggests the role of protecting the environment and reducing pollutants in controlling PTB and other infectious diseases.
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Kurnia, Alma, and Ibnu Hadi. "Peramalan Nilai Ekspor Produk Industri Alas Kaki Menggnakan Model ARIMAX dengan Efek Variasi Kalender." Jurnal Statistika dan Aplikasinya 3, no. 2 (December 30, 2019): 25–34. http://dx.doi.org/10.21009/jsa.03204.
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Model ARIMAX adalah model ARIMA dengan peubah tambahan. Peubah tambahan yang digunakan untuk data deret waktu dengan variasi kalender berupa variabel dummy. Pada makalah ini, akan dilakukan penghitungan peramalan nilai ekspor produk industri alas kaki bulan Juli 2019 sampai dengan Jui 2020 dengan menggunakan model ARIMAX dengan efek variasi kalender. Efek variasi kalender yang ditemukan pada data nilai ekspor produk industri alas kaki adalah libur hari raya Idul Fitri. Data yang digunakan pada makalah ini yaitu data nilai ekspor produk industri alas kaki mulai dari bulan Januari tahun 2010 sampai dengan bulan Juni tahun 2019. Pemodelan ARIMAX dilakukan dengan menggabungkan model regresi dummy dari data aktual dan model ARIMA dari data residual.
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Putera, Muhammad Luthfi Setiarno. "IMPROVISASI MODEL ARIMAX-ANFIS DENGAN VARIASI KALENDER UNTUK PREDIKSI TOTAL TRANSAKSI NON-TUNAI." Indonesian Journal of Statistics and Its Applications 4, no. 2 (July 31, 2020): 296–310. http://dx.doi.org/10.29244/ijsa.v4i2.603.
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Developed information technology boosts interest to use non-cash payment media in many areas. Following the high usage of a non-cash scheme in many payment transactions recently, the objective of this work is two-fold that is to predict the total of a non-cash transaction by using various time-series models and to compare the forecasting accuracy of those models. As a country with a mostly dense Moslem population, plenty of economical activities are arguably influenced by the Islamic calendar effect. Therefore the models being compared are ARIMA, ARIMA with Exogenous (ARIMAX), and a hybrid between ARIMAX and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). By taking such calendar variation into account, the result shows that ARIMAX-ANFIS is the best method in predicting non-cash transactions since it produces lower MAPE. It is indicated that non-cash transaction increases significantly ahead of Ied Fitr occurrence and hits the peak in December. It demonstrates that the hybrid model can improve the accuracy performance of prediction.
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Putera, Muhammad Luthfi Setiarno. "PERAMALAN TRANSAKSI PEMBAYARAN NON-TUNAI MENGGUNAKAN ARIMAX-ANN DENGAN KONFIGURASI KALENDER." BAREKENG: Jurnal Ilmu Matematika dan Terapan 14, no. 1 (March 1, 2020): 135–46. http://dx.doi.org/10.30598/barekengvol14iss1pp135-146.
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Akses internet yang luas mendorong kian seringnya sistem pembayaran non-tunai digunakan. Di Indonesia, berbagai aktivitas dan transaksi ekonomi seringkali dipengaruhi oleh pergerakan kalender, terutama kalender Hijriyah. Tujuan penelitian ini untuk memodelkan dan meramalkan total pembayaran non-tunai di Indonesia dengan menambahkan konfigurasi kalender sebagai variabel. Digunakan metode ARIMA, ARIMAX dan hibrida ARIMAX-ANN yang akan dibandingkan akurasinya. Diperoleh model terbaik untuk peramalan jumlah pembayaran non-tunai adalah ARIMAX-ANN dengan RMSE terkecil, yaitu Rp 20,9 triliun. Spesifikasi model terbaik tersebut adalah ARIMAX(2,1,1) yang dihibrida dengan ANN yang inputnya diseleksi melalui regresi stepwise. Selain memenuhi asumsi galat yang identik, independen, dan berdistribusi normal, ARIMAX-ANN juga mampu mengikuti dinamika dan tren dari pembayaran non-tunai, khususnya pada bulan jatuhnya Idul Fitri dan periode akhir tahun.
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Pektaş, Ali Osman, and H. Kerem Cigizoglu. "ANN hybrid model versus ARIMA and ARIMAX models of runoff coefficient." Journal of Hydrology 500 (September 2013): 21–36. http://dx.doi.org/10.1016/j.jhydrol.2013.07.020.
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Diksa, I. Gusti Bagus Ngurah. "Forecasting the Existence of Chocolate with Variation and Seasonal Calendar Effects Using the Classic Time Series Approach." Jurnal Matematika, Statistika dan Komputasi 18, no. 2 (January 1, 2022): 237–50. http://dx.doi.org/10.20956/j.v18i2.18542.
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Chocolate is the raw material for making cakes, so consumption of chocolate also increases on Eid al-Fitr. However, this is different in the United States where the tradition of sharing chocolate cake is carried out on Christmas. To monitor the existence of this chocolate can be through the movement of data on Google Trends. This study aims to predict the existence of chocolate from the Google trend where the use of chocolate by the community fluctuates according to the calendar variance and seasonal rhythm. The method used is classic time series, namely nave, double exponential smoothing, multiplicative decomposition, addictive decomposition, holt winter multiplicative, holt winter addictive, time series regression, hybrid time series, ARIMA, and ARIMAX. Based on MAPE in sample, the best time series model to model the existence of chocolate in Indonesia is ARIMAX (1,0,0) while for the United States it is Hybrid Time Series Regression-ARIMA(2,1,[10]). For forecasting the existence of chocolate in Indonesia, the best models in forecasting are ARIMA (([11],[12]),1,1) and Naïve Seasonal. In contrast to the best forecasting model for the existence of chocolate in the United States, namely Hybrid Naïve Seasonal-SARIMA (2,1,0)(0,0,1)12 Hybrid Time Series Regression- ARIMA(2,1,[10]), Time Series Regression, Winter Multiplicative, ARIMAX([3],0,0).
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Rizalde, Fadlika Arsy, Sri Mulyani, and Nelayesiana Bachtiar. "Forecasting Hotel Occupancy Rate in Riau Province Using ARIMA and ARIMAX." Proceedings of The International Conference on Data Science and Official Statistics 2021, no. 1 (January 4, 2022): 578–89. http://dx.doi.org/10.34123/icdsos.v2021i1.199.
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Hotel Occupancy Rate is one of the important leading indicators for calculating the Accommodation Sub-Category of Gross Regional Domestic Product (GRDP). By the extreme decline of the Hotel Occupancy Rate data due to COVID-19 and the unavailability of current data to counting GRDP quarterly, the Hotel Occupancy Rate prediction needs to do with the appropriate forecasting method. The authors use data from Google Trends as an additional variable in predicting the Hotel Occupancy Rate using the ARIMAX model and then compares it with the ARIMA model. The results showed that the ARIMAX model had better accuracy than ARIMA, with a MAPE value of 9.64 percent and an RMSE of 4.21 percent. This research concluded that if there is no change in government policy related to social restrictions until the end of the year, the ARIMAX model predicts the December 2021 Hotel Occupancy Rate of 38.59 percent.
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Musa, Mohammed Ibrahim. "Malaria Disease Distribution in Sudan Using Time Series ARIMA Model." International Journal of Public Health Science (IJPHS) 4, no. 1 (March 1, 2015): 7. http://dx.doi.org/10.11591/ijphs.v4i1.4705.
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<p>Malaria is widely spread and distributed in the tropical and subtropical regions of the world. Sudan is a sub-Saharan African country that is highly affected by malaria with 7.5 million cases and 35,000 deaths every year. The auto-regressive integrated moving average (ARIMA) model was used to predict the spread of malaria in the Sudan. The ARIMA model used malaria cases from 2006 to 2011 as a training set, and data from 2012 as a testing set, and created the best model fitted to forecast the malaria cases in Sudan for years 2013 and 2014. The ARIMAX model was carried out to examine the relationship between malaria cases and climate factors with diagnostics of previous malaria cases using the least Bayesian Information Criteria (BIC) values. The results indicated that there were four different models, the ARIMA model of the average for the overall states is (1,0,1)(0,1,1)<sup>12</sup>. The ARIMAX model showed that there is a significant variation between the states in Sudan.</p>
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Musa, Mohammed Ibrahim. "Malaria Disease Distribution in Sudan Using Time Series ARIMA Model." International Journal of Public Health Science (IJPHS) 4, no. 1 (March 1, 2015): 7. http://dx.doi.org/10.11591/.v4i1.4705.
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<p>Malaria is widely spread and distributed in the tropical and subtropical regions of the world. Sudan is a sub-Saharan African country that is highly affected by malaria with 7.5 million cases and 35,000 deaths every year. The auto-regressive integrated moving average (ARIMA) model was used to predict the spread of malaria in the Sudan. The ARIMA model used malaria cases from 2006 to 2011 as a training set, and data from 2012 as a testing set, and created the best model fitted to forecast the malaria cases in Sudan for years 2013 and 2014. The ARIMAX model was carried out to examine the relationship between malaria cases and climate factors with diagnostics of previous malaria cases using the least Bayesian Information Criteria (BIC) values. The results indicated that there were four different models, the ARIMA model of the average for the overall states is (1,0,1)(0,1,1)<sup>12</sup>. The ARIMAX model showed that there is a significant variation between the states in Sudan.</p>
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Khairunnisa, Sarah, Nusyrotus Sa’dah, Isnani, Rohmah Artika, and Prihantini. "Forecasting and Effectiveness Analysis of Domestic Airplane Passengers in Yogyakarta Adisutjipto Airport with Autoregressive Integrated Moving Average with Exogeneous (ARIMAX) Model." Proceeding International Conference on Science and Engineering 3 (April 30, 2020): 365–69. http://dx.doi.org/10.14421/icse.v3.529.
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Airplane is one of the public transportations options that many people choose when traveling long distance. Nowadays, it is notes that the number of passengers domestic flight has increased from the previous months. This increase, especially occurs on the holidays, such as year-end holidays, Eid, and others. The increase of airplane passengers is inversely proportional to the number of available airplane. Forecasting the number of airplane passangers is necessary to prepare additional facilities when there is increasing passengers. This research focused on forecasting domestic airplane passengers at Adisucipto Airport, Yogyakarta using ARIMAX method to forecast the number of domestic airplane passengers and the effectiveness of domestic passengers at the international airport. The purpose of this research is to determine the best ARIMAX model and forecast airplane passengers in Adisucipto airport. The results will show the effectiveness of ARIMAX model with the effect of calendar variance on domestic airplane passenger forecasting at international airport. Based on the result of AIC and RMSE values, it shows that the ARIMAX(1,0,1) model with calendar variation is better than ARIMA(1,0,1) in predicting the number of airplane passengers at Yogyakarta Adisutjipto airport.
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Obi, C. V., and C. N. Okoli. "Comparative Performance of the ARIMA, ARIMAX and SES Model for Estimating Reported Cases of Diabetes Mellitus in Anambra State, Nigeria." European Journal of Engineering and Technology Research 6, no. 1 (January 12, 2021): 63–68. http://dx.doi.org/10.24018/ejers.2021.6.1.2321.
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This study examined the performance of the ARIMA, ARIMAX and the Single Exponential Smoothing (SES) model for the estimation of diabetes cases in Anambra State with the following specific objectives: to fit the model to the data, to determine the best fit model for estimating diabetes mellitus cases and forecast for expected cases for period of five years. The secondary data used for the study is sourced from records of Anambra state Ministry of Health. The Akaike information criterion is adopted for assessing the performance of the models. The R-software is employed for the analysis of data. The results obtained showed that the data satisfied normality and stationarity requirements. The finding of the study showed that ARIMA model has least value of AIC of 1177.92, following the ARIMAX model with value of AIC=1542.25 and SEM recorded highest value of 1595.67. The findings further revealed that the ARIMA has the least values across the measures of accuracy. More so, five years predictions of the cases of diabetes mellitus were obtained using the models under study. From the results of the findings, ARIMA model proved to be best alternative for estimating reported cases of diabetes mellitus in Anambra state. Based on the findings, we recommend there is need for medical practitioners /health planners to create awareness and inform patients about the possible related risk factors of death through early diagnosis and intervention.
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Obi, C. V., and C. N. Okoli. "Comparative Performance of the ARIMA, ARIMAX and SES Model for Estimating Reported Cases of Diabetes Mellitus in Anambra State, Nigeria." European Journal of Engineering and Technology Research 6, no. 1 (January 12, 2021): 63–68. http://dx.doi.org/10.24018/ejeng.2021.6.1.2321.
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This study examined the performance of the ARIMA, ARIMAX and the Single Exponential Smoothing (SES) model for the estimation of diabetes cases in Anambra State with the following specific objectives: to fit the model to the data, to determine the best fit model for estimating diabetes mellitus cases and forecast for expected cases for period of five years. The secondary data used for the study is sourced from records of Anambra state Ministry of Health. The Akaike information criterion is adopted for assessing the performance of the models. The R-software is employed for the analysis of data. The results obtained showed that the data satisfied normality and stationarity requirements. The finding of the study showed that ARIMA model has least value of AIC of 1177.92, following the ARIMAX model with value of AIC=1542.25 and SEM recorded highest value of 1595.67. The findings further revealed that the ARIMA has the least values across the measures of accuracy. More so, five years predictions of the cases of diabetes mellitus were obtained using the models under study. From the results of the findings, ARIMA model proved to be best alternative for estimating reported cases of diabetes mellitus in Anambra state. Based on the findings, we recommend there is need for medical practitioners /health planners to create awareness and inform patients about the possible related risk factors of death through early diagnosis and intervention.
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Nourani, Vahid, Samira Roumianfar, and Elnaz Sharghi. "Using Hybrid ARIMAX-ANN Model for Simulating Rainfall - Runoff - Sediment Process Case Study." International Journal of Applied Metaheuristic Computing 4, no. 2 (April 2013): 44–60. http://dx.doi.org/10.4018/jamc.2013040104.
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The need for accurate modeling of rainfall-runoff-sediment processes has grown rapidly in the past decades. This study investigates the efficiency of black-box models including Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average with eXogenous input (ARIMAX) models for forecasting the rainfall-runoff-sediment process. According to the complex behavior of the rainfall-runoff-sediment time series, they include both linear and nonlinear components; therefore, employing a hybrid model which combines the advantages of both linear and non-linear models improves the accuracy of prediction. In this paper, a hybrid of ARIMAX-ANN model is applied to rainfall-runoff-sediment modeling of a watershed. At the first step of the hybrid modeling, the ARIMAX method is applied to forecast the linear component of the rainfall-runoff process and then in the second step, an ANN model is used to find the non-linear relationship among the residuals of the fitted linear ARIMAX model. Finally, total effective time series of runoff, obtained by the hybrid ARIMAX-ANN model are imposed as input to the proposed ANN model for prediction daily suspended sediment load of the watershed. The proposed model is more appropriate, as it uses the semi-linear relation for prediction of sediment load.
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Amelia, Ririn, Elyas Kustiawan, Ineu Sulistiana, and Desy Yuliana Dalimunthe. "FORECASTING RAINFALL IN PANGKALPINANG CITY USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE WITH EXOGENOUS (SARIMAX)." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 1 (March 21, 2022): 137–46. http://dx.doi.org/10.30598/barekengvol16iss1pp137-146.
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Changes in extreme rainfall can cause disasters or losses for the wider community, so information about future rainfall is also needed. Rainfall is included in the category of time series data. One of the time series methods that can be used is Autoregressive Integrated Moving Average (ARIMA) or Seasonal ARIMA (SARIMA). However, this model only involves one variable without involving its dependence on other variables. One of the factors that can affect rainfall is wind speed which can affect the formation of convective clouds. In this study, the ARIMA model was expanded by adding eXogen variables and seasonal elements, namely the SARIMAX model (Seasonal ARIMA with eXogenous input). Based on the analysis that has been carried out, the prediction of rainfall in Pangkalpinang City, Bangka Belitung Islands Province can be modeled with the SARIMAX model (0,1,3)(0,1,1){12} for monthly rainfall and SARIMAX (0,1,2 )(0,1,3){12} for maximum daily rainfall. When compared with the actual data and previous studies using ARIMAX, the SARIMAX model is still better in the forecasting process when compared to the ARIMAX model. If viewed based on the AIC value of the SARIMA model, the SARIMAX model is also more suitable to be used to predict rainfall in Pangkalpinang City.
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Agbenyega, Diana Ayorkor, John Andoh, Samuel Iddi, and Louis Asiedu. "Modelling Customs Revenue in Ghana Using Novel Time Series Methods." Applied Computational Intelligence and Soft Computing 2022 (April 18, 2022): 1–8. http://dx.doi.org/10.1155/2022/2111587.
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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.
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Putri, J. A., Suhartono Suhartono, H. Prabowo, N. A. Salehah, D. D. Prastyo, and Setiawan Setiawan. "Forecasting Currency in East Java: Classical Time Series vs. Machine Learning." Indonesian Journal of Statistics and Its Applications 5, no. 2 (June 30, 2021): 284–303. http://dx.doi.org/10.29244/ijsa.v5i2p284-303.
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Most research about the inflow and outflow currency in Indonesia showed that these data contained both linear and nonlinear patterns with calendar variation effect. The goal of this research is to propose a hybrid model by combining ARIMAX and Deep Neural Network (DNN), known as hybrid ARIMAX-DNN, for improving the forecast accuracy in the currency prediction in East Java, Indonesia. ARIMAX is class of classical time series models that could accurately handle linear pattern and calendar variation effect. Whereas, DNN is known as a machine learning method that powerful to tackle a nonlinear pattern. Data about 32 denominations of inflow and outflow currency in East Java are used as case studies. The best model was selected based on the smallest value of RMSE and sMAPE at the testing dataset. The results showed that the hybrid ARIMAX-DNN model improved the forecast accuracy and outperformed the individual models, both ARIMAX and DNN, at 26 denominations of inflow and outflow currency. Hence, it can be concluded that hybrid classical time series and machine learning methods tend to yield more accurate forecasts than individual models, both classical time series and machine learning methods.
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Islam, Farhana, and Monzur Alam Imteaz. "Use of Teleconnections to Predict Western Australian Seasonal Rainfall Using ARIMAX Model." Hydrology 7, no. 3 (August 5, 2020): 52. http://dx.doi.org/10.3390/hydrology7030052.
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Increased demand for engineering propositions to forecast rainfall events in an area or region has resulted in developing different rainfall prediction models. Interestingly, rainfall is a very complicated natural system that requires consideration of various attributes. However, regardless of the predictability performance, easy to use models have always been welcomed over the complex and ambiguous alternatives. This study presents the development of Auto–Regressive Integrated Moving Average models with exogenous input (ARIMAX) to forecast autumn rainfall in the South West Division (SWD) of Western Australia (WA). Climate drivers such as Indian Ocean Dipole (IOD) and El Nino Southern Oscillation (ENSO) were used as predictors. Eight rainfall stations with 100 years of continuous data from two coastal regions (south coast and north coast) were selected. In the south coast region, Albany (0,1,1) with exogenous input DMIOct–Nino3Nov, and Northampton (0,1,1) with exogenous input DMIJan–Nino3Nov were able to forecast autumn rainfall 4 months and 2 months in advance, respectively. Statistical performance of the ARIMAX model was compared with the multiple linear regression (MLR) model, where for calibration and validation periods, the ARIMAX model showed significantly higher correlations (0.60 and 0.80, respectively), compared to the MLR model (0.44 and 0.49, respectively). It was evident that the ARIMAX model can predict rainfall up to 4 months in advance, while the MLR has shown strict limitation of prediction up to 1 month in advance. For WA, the developed ARIMAX model can help to overcome the difficulty in seasonal rainfall prediction as well as its application can make an invaluable contribution to stakeholders’ economic preparedness plans.
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Assakhiy, Rasyada, Samsul Anwar, and A. R. Fitriana. "PERAMALAN REALISASI PENERIMAAN ZAKAT PADA BAITULMAL ACEH DENGAN MEMPERTIMBANGKAN EFEK DARI VARIASI KALENDER." Jurnal Ekonomi Pembangunan 27, no. 2 (December 31, 2019): 27–45. http://dx.doi.org/10.14203/jep.27.2.2019.27-45.
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Baitulmal Aceh merupakan sebuah lembaga pemerintah daerah Provinsi Aceh yang bertanggung jawab sebagai pengelola dan pendistribusi zakat, infak dan sedekah (ZIS). Peramalan potensi zakat pada masa yang akan datang dibutuhkan oleh Baitulmal Aceh sebagai salah satu landasan penyusunan kebijakan pengelolaan ZIS. Penelitian ini bertujuan untuk meramalkan potensi zakat yang terkumpul pada tahun 2018 dan 2019 dengan mempertimbangkan efek dari variasi kalender. Data yang digunakan dalam penelitian ini adalah data realisasi penerimaan zakat bulanan mulai dari bulan Januari 2015 hingga Desember 2017 yang diperoleh dari Baitulmal Aceh. Data tersebut dianalisis dengan model Autoregressive Integrated Moving Average with Exogenous Variable (ARIMAX) dan Seasonal Autoregressive Integrated Moving Average (SARIMA) sebagai model pembanding. Hasil penelitian menunjukkan bahwa model ARIMAX dengan orde ARIMA(2,0,2) (1,0,2)12, t, V1, ..., V11 jauh lebih baik daripada model SARIMA dengan orde ARIMA(0,1,2)(0,1,1)12 berdasarkan indikator ketepatan hasil ramalannya (RMSE dan MAPE). Realisasi penerimaan zakat pada tahun 2018 dan 2019 masing-masing diperkirakan sebesar Rp. 1.347.526.504 dan Rp. 1.359.728.268. Hasil peramalan tersebut dapat digunakan sebagai salah satu rujukan bagi Baitulmal Aceh dalam menyusun kebijakan pendistribusian zakat pada tahun-tahun yang akan datang.
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Munir, Said, and Martin Mayfield. "Application of Density Plots and Time Series Modelling to the Analysis of Nitrogen Dioxides Measured by Low-Cost and Reference Sensors in Urban Areas." Nitrogen 2, no. 2 (April 13, 2021): 167–95. http://dx.doi.org/10.3390/nitrogen2020012.
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Temporal variability of NO2 concentrations measured by 28 Envirowatch E-MOTEs, 13 AQMesh pods, and eight reference sensors (five run by Sheffield City Council and three run by the Department for Environment, Food and Rural Affairs (DEFRA)) was analysed at different time scales (e.g., annual, weekly and diurnal cycles). Density plots and time variation plots were used to compare the distributions and temporal variability of NO2 concentrations. Long-term trends, both adjusted and non-adjusted, showed significant reductions in NO2 concentrations. At the Tinsley site, the non-adjusted trend was −0.94 (−1.12, −0.78) µgm−3/year, whereas the adjusted trend was −0.95 (−1.04, −0.86) µgm−3/year. At Devonshire Green, the non-adjusted trend was −1.21 (−1.91, −0.41) µgm−3/year and the adjusted trend was −1.26 (−1.57, −0.83) µgm−3/year. Furthermore, NO2 concentrations were analysed employing univariate linear and nonlinear time series models and their performance was compared with a more advanced time series model using two exogenous variables (NO and O3). For this purpose, time series data of NO, O3 and NO2 were obtained from a reference site in Sheffield, which were more accurate than the measurements from low-cost sensors and, therefore, more suitable for training and testing the model. In this article, the three main steps used for model development are discussed: (i) model specification for choosing appropriate values for p, d and q, (ii) model fitting (parameters estimation), and (iii) model diagnostic (testing the goodness of fit). The linear auto-regressive integrated moving average (ARIMA) performed better than the nonlinear counterpart; however, its performance in predicting NO2 concentration was inferior to ARIMA with exogenous variables (ARIMAX). Using cross-validation ARIMAX demonstrated strong association with the measured concentrations, with a correlation coefficient of 0.84 and RMSE of 9.90. ARIMAX can be used as an early warning tool for predicting potential pollution episodes in order to be proactive in adopting precautionary measures.
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Du, Zhicheng, Lin Xu, Wangjian Zhang, Dingmei Zhang, Shicheng Yu, and Yuantao Hao. "Predicting the hand, foot, and mouth disease incidence using search engine query data and climate variables: an ecological study in Guangdong, China." BMJ Open 7, no. 10 (October 2017): e016263. http://dx.doi.org/10.1136/bmjopen-2017-016263.
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ObjectivesHand, foot, and mouth disease (HFMD) has caused a substantial burden in China, especially in Guangdong Province. Based on the enhanced surveillance system, we aimed to explore whether the addition of temperate and search engine query data improves the risk prediction of HFMD.DesignEcological study.Setting and participantsInformation on the confirmed cases of HFMD, climate parameters and search engine query logs was collected. A total of 1.36 million HFMD cases were identified from the surveillance system during 2011–2014. Analyses were conducted at aggregate level and no confidential information was involved.Outcome measuresA seasonal autoregressive integrated moving average (ARIMA) model with external variables (ARIMAX) was used to predict the HFMD incidence from 2011 to 2014, taking into account temperature and search engine query data (Baidu Index, BDI). Statistics of goodness-of-fit and precision of prediction were used to compare models (1) based on surveillance data only, and with the addition of (2) temperature, (3) BDI, and (4) both temperature and BDI.ResultsA high correlation between HFMD incidence and BDI (r=0.794, p<0.001) or temperature (r=0.657, p<0.001) was observed using both time series plot and correlation matrix. A linear effect of BDI (without lag) and non-linear effect of temperature (1 week lag) on HFMD incidence were found in a distributed lag non-linear model. Compared with the model based on surveillance data only, the ARIMAX model including BDI reached the best goodness-of-fit with an Akaike information criterion (AIC) value of −345.332, whereas the model including both BDI and temperature had the most accurate prediction in terms of the mean absolute percentage error (MAPE) of 101.745%.ConclusionsAn ARIMAX model incorporating search engine query data significantly improved the prediction of HFMD. Further studies are warranted to examine whether including search engine query data also improves the prediction of other infectious diseases in other settings.
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Jadevicius, Arvydas, and Simon Huston. "Property market modelling and forecasting: simple vs complex models." Journal of Property Investment & Finance 33, no. 4 (July 6, 2015): 337–61. http://dx.doi.org/10.1108/jpif-08-2014-0053.
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Purpose – The commercial property market is complex, but the literature suggests that simple models can forecast it. To confirm the claim, the purpose of this paper is to assess a set of models to forecast UK commercial property market. Design/methodology/approach – The employs five modelling techniques, including Autoregressive Integrated Moving Average (ARIMA), ARIMA with a vector of an explanatory variable(s) (ARIMAX), Simple Regression (SR), Multiple Regression, and Vector Autoregression (VAR) to model IPD UK All Property Rents Index. The Bank Rate, Construction Orders, Employment, Expenditure, FTSE AS Index, Gross Domestic Product (GDP), and Inflation are all explanatory variables selected for the research. Findings – The modelling results confirm that increased model complexity does not necessarily yield greater forecasting accuracy. The analysis shows that although the more complex VAR specification is amongst the best fitting models, its accuracy in producing out-of-sample forecasts is poorer than of some less complex specifications. The average Theil’s U-value for VAR model is around 0.65, which is higher than that of less complex SR with Expenditure (0.176) or ARIMAX (3,0,3) with GDP (0.31) as an explanatory variable models. Practical implications – The paper calls analysts to make forecasts more user-friendly, which are easy to use or understand, and for researchers to pay greater attention to the development and improvement of simpler forecasting techniques or simplification of more complex structures. Originality/value – The paper addresses the issue of complexity in modelling commercial property market. It advocates for simplicity in modelling and forecasting.
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Intan, Solikhah Novita, Etik Zukhronah, and Supriyadi Wibowo. "Peramalan Banyaknya Pengunjung Pantai Glagah Menggunakan Metode Autoregressive Integrated Moving Average Exogenous (ARIMAX) dengan Efek Variasi Kalender." Indonesian Journal of Applied Statistics 1, no. 2 (March 13, 2019): 70. http://dx.doi.org/10.13057/ijas.v1i2.26298.
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<pre>Glagah Beach is one of the tourist destinations in Kulon Progo Regency, Yogyakarta which is the most visited by tourists. Glagah Beach visitors data show that in the month of Eid Al-Fitr there was a significant increase. This shows that there is an effect of the calendar variation of Eid al-Fitr. Therefore, it is needed a method that can be used to analyze time series data which contains effects of calendar variations, that is ARIMAX method. The aim of this study are to find the best ARIMAX model and to predict the number of visitors to Glagah Beach in the future. The result shows that the best ARIMAX model was ARIMAX([24],0,0). Forecasting from January to September 2016 are 37211, 21306, 26247, 24148, 28402, 29309, 81724, 26029, and 23688 visitors.</pre><br /> Keywords: Glagah Beach; variation of calendar; Eid al-Fitr; ARIMAX.
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Barzola-Monteses, Julio, Mónica Mite-León, Mayken Espinoza-Andaluz, Juan Gómez-Romero, and Waldo Fajardo. "Time Series Analysis for Predicting Hydroelectric Power Production: The Ecuador Case." Sustainability 11, no. 23 (November 20, 2019): 6539. http://dx.doi.org/10.3390/su11236539.
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Electrical generation in Ecuador mainly comes from hydroelectric and thermo-fossil sources, with the former amounting to almost half of the national production. Even though hydroelectric power sources are highly stable, there is a threat of droughts and floods affecting Ecuadorian water reservoirs and producing electrical faults, as highlighted by the 2009 Ecuador electricity crisis. Therefore, predicting the behavior of the hydroelectric system is crucial to develop appropriate planning strategies and a good starting point for energy policy decisions. In this paper, we developed a time series predictive model of hydroelectric power production in Ecuador. To this aim, we used production and precipitation data from 2000 to 2015 and compared the Box-Jenkins (ARIMA) and the Box-Tiao (ARIMAX) regression methods. The results showed that the best model is the ARIMAX (1,1,1) (1,0,0)12, which considers an exogenous variable precipitation in the Napo River basin and can accurately predict monthly production values up to a year in advance. This model can provide valuable insights to Ecuadorian energy managers and policymakers.
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Paul, Ranjit Kumar. "ARIMAX-GARCH-WAVELET model for forecasting volatile data." Model Assisted Statistics and Applications 10, no. 3 (July 20, 2015): 243–52. http://dx.doi.org/10.3233/mas-150328.
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Hidayat, Saeful, and Nisrina Hakim. "PERAMALAN EKSPOR LUAR NEGERI BANTEN MENGGUNAKAN MODEL ARIMAX." Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika 2, no. 2 (August 30, 2021): 204–13. http://dx.doi.org/10.46306/lb.v2i2.75.
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Forecasting Banten exports is one of the important elements for formulating economic growth targets in the development planning document prepared by the Banten Provincial Government. The purpose of this study is to forecast exports by utilizing export data from the BPS and the United States manufacturing purchasing managers index (PMI) which is derived from the Institute of Supply Management (ISM). The results showed that the ARIMAX model has a very good forecasting ability. This is indicated by the MAPE value which reached 8.84 percent. However, forecasting accuracy will decrease as the forecasting time span increases
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Ifeanyichukwu Ugoh, Christogonus, Chinwendu Alice Uzuke, and Dominic Obioma Ugoh. "Application of ARIMAX Model on Forecasting Nigeria’s GDP." American Journal of Theoretical and Applied Statistics 10, no. 5 (2021): 216. http://dx.doi.org/10.11648/j.ajtas.20211005.12.
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Susila, Muktar Redy. "PENGARUH HARI RAYA IDUL FITRI TERHADAP INFLASI DI INDONESIA DENGAN PENDEKATAN ARIMAX (VARIASI KALENDER)." BAREKENG: Jurnal Ilmu Matematika dan Terapan 14, no. 3 (October 10, 2020): 369–78. http://dx.doi.org/10.30598/barekengvol14iss3pp369-378.
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Abstrak
Tujuan dari penilitian ini yaitu meneliti pengaruh dari hari raya Idul Fitri terhadap inflasi bulanan di Indonesia. Digunakan metode ARIMAX (Variasi Kalender) untuk mengetahui besar pengaruh dari Idul Fitri terhadap inflasi bulanan di Indonesia. Karakteristik inflasi Juli 2008 hingga Juni 2019 memiliki keunikan. Rata-rata inflasi bulanan yaitu 0,39 dan varians inflasi bulanan yaitu 0,26. Berdasarkan model ARIMAX menunjukan bahwa bulan Januari, Mei, Juni, Juli, Agustus, November, Desember, dan hari raya Idul Fitri memberikan pengaruh signifikan terhadap inflasi bulanan Indonesia. Efek yang diberikan hari raya Idhul Fitri yaitu sebesar 0,47. Pada saat bulan Idul Fitri tiba angka inflasinya akan lebih tinggi sebesar 0,47 dibandingkan bulan lainnya.
Kata Kunci : Inflasi, ARIMAX, Idul Fitri.
Abstract
The purpose of this study is to calculate the effect of Eid Al-Fitr to Indonesian monthly inflation. The ARIMAX (Calendar Variation) method is used to determine the effect of Eid Al-Fitr on Indonesian monthly inflation. The characteristics of inflation in July 2008 to June 2019 are unique. The average of inflation is 0,39 and the variance of inflation is 0,26. The ARIMAX model shows that January, May, June, July, August, November, December, and Eid Al-Fitr has a significant influence on Indonesian monthly inflation. The effect of the Eid Al-Fitr was 0,47. When the Eid Al-Fitr arrives, the inflation rate will be higher 0,47 than other months.
Keywords: Inflation, ARIMAX, Eid al-Fitr.
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Maggina, Anastasia. "MBAR Models: A Test of ARIMAX Modelling." Review of Pacific Basin Financial Markets and Policies 14, no. 02 (June 2011): 347–66. http://dx.doi.org/10.1142/s0219091511002299.
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The main purpose of this paper is to provide evidence on some of the standard models of accounting earnings and returns relations mainly collected through the literature. Standard models such as earnings level and earnings changes, among others, have been investigated in this study. Models that correspond better to the data drawn from the Athens Stock Exchange have been selected. Models I, II, V, VII and IX have statistically significant coefficients of explanatory variables. In addition, model II with the MSE (minimum value of squared residuals) loss function in ARIMAX (2,0,2) is prevalent. Models that include prior earnings in various forms using levels, changes in price and changes in earnings, change in price to beginning price, lagged parameters and differentiated price models have statistically significant explanatory power.
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Fauziyah, Endah, Dwi Ispriyanti, and Tarno Tarno. "PEMODELAN DAN PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG) MENGGUNAKAN ARIMAX-TARCH." Jurnal Gaussian 10, no. 4 (December 31, 2021): 595–604. http://dx.doi.org/10.14710/j.gauss.v10i4.33102.
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The Composite Stock Price Index (IHSG) is a value that describes the combined performance of all shares listed on the Indonesia Stock Exchange. JCI serves as a benchmark for investors in investing. The method used to predict future conditions based on past data is forecasting . Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) is amodel time series that can be used for forecasting. Financial data has high volatility which causes the variance of the residual model which is not constant (heteroscedasticity). ARCH / GARCH model is used to solve the heteroscedasticity problem in the model. If the data is heteroscedastic and asymmetric, then the model can be used Threshold Autoregressive Conditional Heteroskedasticity (TARCH). The data used are the Composite Stock Price Index (IHSG) for the January 2000 - April 2020 period and the dollar exchange rate data for the January 2000 - April 2020 period asvariables independent from the ARIMAX model. The best model used to predict the JCI from the results of this study is the ARIMAX (1,1,0) -TARCH (1,2) model with an AIC value of -0.819074.
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Cui, Herui, and Xu Peng. "Short-Term City Electric Load Forecasting with Considering Temperature Effects: An Improved ARIMAX Model." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/589374.
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Short-term electric load is significantly affected by weather, especially the temperature effects in summer. External factors can result in mutation structures in load data. Under the influence of the external temperature factors, city electric load cannot be easily forecasted as usual. This research analyzes the relationship between electricity load and daily temperature in city. An improved ARIMAX model is proposed in this paper to deal with the mutation data structures. It is found that information amount of the improved ARIMAX model is smaller than that of the classic method and its relative error is less than AR, ARMA and Sigmoid-Function ANN models. The forecasting results are more accurately fitted. This improved model is highly valuable when dealing with mutation data structure in the field of load forecasting. And it is also an effective technique in forecasting electric load with temperature effects.
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Nieto, M. R., R. B. Carmona Benitez, and J. N. Martinez. "Comparing models to forecast cargo volume at port terminals." Journal of Applied Research and Technology 19, no. 3 (June 30, 2021): 238–49. http://dx.doi.org/10.22201/icat.24486736e.2021.19.3.1695.
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Economic growth has a direct link with the volume of cargo at port terminals. To encourage growth, investment decisions on infrastructure are required that can be performed by the development of econometric models. We compare three time-series models and one machine-learning model to estimate and forecast cargo volume. We apply an ARIMA+GARCH+Bootstrap, a multiplicative Holt-Winters, a support vector regression model, and a time-series model with explanatory variables ARIMAX. The models forecast cargo through the ports of San Pedro using data from 2008 to 2016. The database contains imports and exports of bulk, container, reefer, and ro-ro cargo. Results show that the multiplicative Holt-Winters model is the best method to forecast imports and exports of bulk cargo, while the support vector regression model is the best method to forecast imports and exports of container, reefer, and ro-ro cargo. The Diebold-Mariano Test, the RMSE metric, and the MAPE metric validate the results.
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Intihar, Marko, Tomaž Kramberger, and Dejan Dragan. "Container Throughput Forecasting Using Dynamic Factor Analysis and ARIMAX Model." PROMET - Traffic&Transportation 29, no. 5 (November 5, 2017): 529–42. http://dx.doi.org/10.7307/ptt.v29i5.2334.
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The paper examines the impact of integration of macroeconomic indicators on the accuracy of container throughput time series forecasting model. For this purpose, a Dynamic factor analysis and AutoRegressive Integrated Moving-Average model with eXogenous inputs (ARIMAX) are used. Both methodologies are integrated into a novel four-stage heuristic procedure. Firstly, dynamic factors are extracted from external macroeconomic indicators influencing the observed throughput. Secondly, the family of ARIMAX models of different orders is generated based on the derived factors. In the third stage, the diagnostic and goodness-of-fit testing is applied, which includes statistical criteria such as fit performance, information criteria, and parsimony. Finally, the best model is heuristically selected and tested on the real data of the Port of Koper. The results show that by applying macroeconomic indicators into the forecasting model, more accurate future throughput forecasts can be achieved. The model is also used to produce future forecasts for the next four years indicating a more oscillatory behaviour in (2018-2020). Hence, care must be taken concerning any bigger investment decisions initiated from the management side. It is believed that the proposed model might be a useful reinforcement of the existing forecasting module in the observed port.
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Arumningtyas, Felinda, Alan Prahutama, and Puspita Kartikasari. "Value-At-Risk Analysis Using ARIMAX-GARCHX Approach For Estimating Risk Of Bank Central Asia Stock Returns." Jurnal Varian 5, no. 1 (November 10, 2021): 71–80. http://dx.doi.org/10.30812/varian.v5i1.1474.
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Before buying a stock, an investor must estimate the risk which will be received. VaR is one of the methods that can be used to measure the level of risk. Most stock returns have a high fluctuation, so the variant is heteroscedastic, which is thought to be caused by exogenous variables. The time series model used to model data that is not only influenced by the previous period but is also influenced by exogenous variables is ARIMAX. In contrast, the GARCHX model is used to obtain a more optimal stock return data model with heteroscedasticity cases and is influenced by exogenous variables. This study uses the ARIMAX-GARCHX model to calculate the VaR of the stock returns of PT Bank Central Asia Tbk. The exogenous variables used are the exchange rate return of IDR/USD and the return of the JCI in the period January 3, 2017, to March 31, 2021. The best model chosen is the ARIMAX(2,0,1,1)-GARCHX(1,1,1). VaR calculation is carried out with the concept of moving windows with time intervals of 250, 375, and 500 transaction days. The results obtained at the 95% confidence level, the maximum loss obtained by an investor is 1,4%.
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Chow, Hon Fung. "Short-term electricity grid maximum demand forecasting with the ARIMAX-SVR Machine Learning Hybrid Model." HKIE Transactions 28, no. 1 (April 15, 2021): 22–30. http://dx.doi.org/10.33430/v28n1thie-2020-0005.
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This paper proposes and discusses the viability of a short-term grid maximum demand forecasting model combining autoregressive integrated moving average with regressors (ARIMAX) and support vector regression (SVR). Grid demand forecasting is essential to generation unit scheduling, maintenance planning and system security. Traditionally, grid demand is forecasted using multivariate linear regression models with parameters adjusted to past data. A disadvantage of the linear regression model is that the parameters require regular adjustment, otherwise the prediction accuracy will deteriorate over time. With recent advances in the field of machine learning and lower computational costs, the usage of machine learning in the power industry becomes increasingly practicable. The proposed model is a machine learning model that combines ARIMAX and SVR to exploit their respective effectiveness in predicting linear and non-linear data. In contrast to linear regression models, the machine learning model automatically updates itself when new data is included. The hybrid model is benchmarked against other forecasting models and demonstrated a marked improvement in accuracy, achieving RMSE of 67.7MW and MAPE of 1.32% in a seven-day forecast.
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Huang, Anqiang, Shouyang Wang, and Xun Zhang. "A New Approach to Forecasting Container Throughput of Guangzhou Port with Domain Knowledge." International Journal of Knowledge and Systems Science 4, no. 3 (July 2013): 70–88. http://dx.doi.org/10.4018/ijkss.2013070105.
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Although judgmental models are widely applied in practice to alleviate the limitation of statistical models ignoring domain knowledge, they are still suffering from many kinds of biases and inconsistencies inherent in subjective judgments. Moreover, most of the prior studies are often concentrated on making judgmental adjustments to statistical projections and ignore incorporating domain knowledge in other forecasting steps. This paper proposes a framework under which domain knowledge are integrated with the whole forecasting process and a new forecasting method is developed. The new method is applied to forecasting the container throughput of Guangzhou Port, one of the most important ports of China. In order to test the effectiveness of the new method, the authors compare its performance with that of the ARIMAX model. The results show that the new method significantly outperforms the ARIMAX model.
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Moroke, Ntebogang Dinah. "Box-Jenkins transfer function framework applied to saving-investment nexus in the South African context." Journal of Governance and Regulation 4, no. 1 (2015): 63–77. http://dx.doi.org/10.22495/jgr_v4_i4_p7.
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This paper studied the relationship between investment and savings in South Africa for the period 1990 quarter 1 to 2014 quarter 3. The unit root test confirmed the non-stationarity of the series prior to first differencing. The correlation coefficient and the model assessing a full capacity mobility hypothesis were significant and passed all the diagnostic examinations. The estimated parameter provided evidence of imperfect capital mobility. ARIMAX (5, 1, 0) out-performed all the five models and was used for pre-whitening process. This model was later used to produce a two year forecasts of investment. The error forecast measure provided enough evidence to conclude that ARIMAX (5, 1, 0) provided valid forecasts. These results are recommended when embarking on future saving-investment plans in South Africa.
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Zhang, Rui, Zhen Guo, Yujie Meng, Songwang Wang, Shaoqiong Li, Ran Niu, Yu Wang, Qing Guo, and Yonghong Li. "Comparison of ARIMA and LSTM in Forecasting the Incidence of HFMD Combined and Uncombined with Exogenous Meteorological Variables in Ningbo, China." International Journal of Environmental Research and Public Health 18, no. 11 (June 7, 2021): 6174. http://dx.doi.org/10.3390/ijerph18116174.
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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.
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Sutthichaimethee, Pruethsan, and Danupon Ariyasajjakorn. "Forecast of Carbon Dioxide Emissions from Energy Consumption in Industry Sectors in Thailand." Environmental and Climate Technologies 22, no. 1 (November 1, 2018): 107–17. http://dx.doi.org/10.2478/rtuect-2018-0007.
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Abstract The aim of this research is to forecast CO2emissions from consumption of energy in Industry sectors in Thailand. To study, input-output tables based on Thailand for the years 2000 to 2015 are deployed to estimate CO2emissions, population growth and GDP growth. Moreover, those are also used to anticipate the energy consumption for fifteen years and thirty years ahead. The ARIMAX Model is applied to two sub-models, and the result indicates that Thailand will have 14.3541 % on average higher in CO2emissions in a fifteen-year period (2016-2030), and 31.1536 % in a thirty-year period (2016-2045). This study hopes to be useful in shaping future national policies and more effective planning. The researcher uses a statistical model called the ARIMAX Model, which is a stationary data model, and is a model that eliminates the problems of autocorrelations, heteroskedasticity, and multicollinearity. Thus, the forecasts will be made with minor error.
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Newton, Newton, Anang Kurnia, and I. Made Sumertajaya. "ANALISIS INFLASI MENGGUNAKAN DATA GOOGLE TRENDS DENGAN MODEL ARIMAX DI DKI JAKARTA." Indonesian Journal of Statistics and Its Applications 4, no. 3 (November 30, 2020): 545–56. http://dx.doi.org/10.29244/ijsa.v4i3.694.
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Inflation is an important economic indicator in showing the economic symptoms of a region's price level. DKI Jakarta is the capital of Indonesia chosen as the center of the economic barometer because it can provide the greatest contribution and influence on the Indonesian economy. The ARIMAX model was used for forecasting by adding independent variables in the Google trends data. Google trends data were explored based on seven expenditure groups published by IHK. The purpose of this study was to determine the effect of forecast Google trends using BPS inflation data in DKI Jakarta. The result of the exploration of Google Trends data was forecasted to get the best forecast model results. The result of data analysis indicates that the forecast results approached the original BPS data with the best forecast model is ARIMAX (2.0.3) all variables X. Google Trends data can be used as forecasting but cannot be used as a reference policy decision.
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Fajar, Muhammad, Teuku M. Madinah, and Hendro Prayitno. "PERAMALAN TINGKAT PENGHUNIAN KAMAR DENGAN MEMANFAATKAN DATA GOOGLE TRENDS DI PROVINSI BANTEN." Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika 2, no. 2 (August 30, 2021): 226–32. http://dx.doi.org/10.46306/lb.v2i2.63.
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The purpose of this paper is to forecast the room occupancy rate (ROR) of star hotels by utilizing Google Trends data, in addition to historical ROR data. The data used is the ROR (%) which comes from the Badan Pusat Statistik-Statistics Indonesia and the Google Trends query index using the search word "hotel". The method used is the ARIMAX model. The results of this study indicate that the ARIMAX model has excellent forecasting capabilities. This is shown by the MAPE value which reached 6.541%, which means the added value of the hotel sector in the first quarter of 2021 is not expected to increase significantly.
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Amalia, Atika, Etik Zukhronah, and Sri Subanti. "Peramalan Data Inflow dan Outflow Uang Kartal Bank Indonesia Provinsi DKI Jakarta Menggunakan Model ARIMAX dan SARIMAX." Indonesian Journal of Applied Statistics 4, no. 2 (November 29, 2021): 87. http://dx.doi.org/10.13057/ijas.v4i2.45673.
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<p><strong>A</strong><strong>bstract</strong><strong>.</strong> DKI Jakarta Province plays a crucial role as the center of government and economy in Indonesia. The description of currency inflows and outflows is highly required before Bank Indonesia formulates the appropriate policies to control the circulation of money. The monthly data of currency inflow and outflow of Bank Indonesia of DKI Jakarta show a significant increase in each year particularly before, during, and after Eid al-Fitr. The determination of Eid al-Fitr does not follow the Gregorian calendar but based on the Islamic calendar. The difference in the use of the Gregorian and Islamic calendars in a time series causes a calendar variation. Thus, the determination of Eid al-Fitr in the Gregorian calendar changes as it goes forward eleven days each year or one month every three years. This study aims to obtain the best model and forecast currency inflows and outflows of Bank Indonesia DKI Jakarta using the ARIMAX and SARIMAX models. The study used in-sample data from January 2009 to December 2018 and out-sample data from January to October 2019. The best model was selected based on the smallest out-sample MAPE value. The result showed that the best forecasting model of inflow was ARIMAX (1,0,1). Meanwhile, the best forecasting model for outflow was SARIMAX (2,0,1)(0,0,1)<sup>12</sup>.</p><p><strong>Keywords: </strong>ARIMAX, calendar variation, forecasting, SARIMAX</p>
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Shankar, P. Sai, and M. Krishna Reddy. "Forecasting gold prices in India using ARIMAX and machine learning algorithms." INTERNATIONAL RESEARCH JOURNAL OF AGRICULTURAL ECONOMICS AND STATISTICS 11, no. 2 (September 15, 2020): 299–310. http://dx.doi.org/10.15740/has/irjaes/11.2/299-310.
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Forecasting is a function in management to assist decision making. It is also described as the process of estimation in unknown future situations. In a more general term it is commonly known as prediction which refers to estimation of time series or longitudinal type data. The main object of this paper is to compare the traditional time series model with machine learning algorithms. To predict the gold prices based on economic factors such as inflation, exchange rate, crude price, bank rate, repo rate, reverse repo rate, gold reserve ration, Bombay stock exchange and National stock exchange. Two lagged variables are taken for each variable in the analysis. The ARIMAX model is developed to forecast Indian gold prices using daily data for the period 2016 to 2020 obtained from World Gold Council. We fitted the ARIMAX (4,1,1) model to our data which exhibited the least AIC values. In the mean while, decision tree, random forest, lasso regression, ridge regression, XGB and ensemble models were also examined to forecast the gold prices based on host of explanatory variables. The forecasting performance of the models were evaluated using mean absolute error, mean absolute percentage error and root mean squared errors. Ensemble model out performs than that of the other models for predicting the gold prices based on set of explanatory variables.
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Chadsuthi, Sudarat, Sopon Iamsirithaworn, Wannapong Triampo, and Charin Modchang. "Modeling Seasonal Influenza Transmission and Its Association with Climate Factors in Thailand Using Time-Series and ARIMAX Analyses." Computational and Mathematical Methods in Medicine 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/436495.
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Influenza is a worldwide respiratory infectious disease that easily spreads from one person to another. Previous research has found that the influenza transmission process is often associated with climate variables. In this study, we used autocorrelation and partial autocorrelation plots to determine the appropriate autoregressive integrated moving average (ARIMA) model for influenza transmission in the central and southern regions of Thailand. The relationships between reported influenza cases and the climate data, such as the amount of rainfall, average temperature, average maximum relative humidity, average minimum relative humidity, and average relative humidity, were evaluated using cross-correlation function. Based on the available data of suspected influenza cases and climate variables, the most appropriate ARIMA(X) model for each region was obtained. We found that the average temperature correlated with influenza cases in both central and southern regions, but average minimum relative humidity played an important role only in the southern region. The ARIMAX model that includes the average temperature with a 4-month lag and the minimum relative humidity with a 2-month lag is the appropriate model for the central region, whereas including the minimum relative humidity with a 4-month lag results in the best model for the southern region.
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Yu, Yao, Ruikai Sun, Yindong Sun, and Yaqing Shu. "Integrated Carbon Emission Estimation Method and Energy Conservation Analysis: The Port of Los Angles Case Study." Journal of Marine Science and Engineering 10, no. 6 (May 24, 2022): 717. http://dx.doi.org/10.3390/jmse10060717.
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Port environmental problems have gradually become the primary concern of port authorities. The future trend of port carbon emissions is crucial to port authorities and managers in formulating regulations and optimizing operation schedules. Owing to the limitations of current prediction methods and the complex social–environmental impact, the estimation results of port carbon emissions have insufficient accuracy to support port development in the future. In this work, the stochastic impacts by regression on population, affluence, and technology (STIRPAT)–long short-term memory (LSTM)–autoregressive integrated moving average with explanatory variable (ARIMAX) integrated model is proposed for the estimation of the carbon emission of Port of Los Angeles to improve the reliability of emission prediction. Macroeconomic indicators that affect port throughput are selected using the principal component analysis—multiple linear regression model. The chosen indicators are then combined with long-term historical port throughput data as the input of the multivariate autoregressive integrated moving average (ARIMAX) model to predict port throughput. Indicators related to port carbon emissions are verified by the STIRPAT model. The LSTM–ARIMAX integrated model is then applied to estimate the emission tendency, which can be useful in developing corresponding carbon reduction strategies and further understanding port emissions. Results show that the proposed method can significantly improve the estimation accuracy for port emission by 11% compared with existing techniques. Energy conservation strategies are also put forward to assist port authorities in achieving the peak clipping of port carbon emission.
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NISA, CHAIRUN, I. WAYAN SUMARJAYA, and I. GUSTI AYU MADE SRINADI. "PENGGUNAAN MODEL ARIMAX UNTUK MERAMALKAN DATA CURAH HUJAN BULANAN DI BALI." E-Jurnal Matematika 10, no. 4 (November 30, 2021): 186. http://dx.doi.org/10.24843/mtk.2021.v10.i04.p341.
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Aprilianto, Muhammad, Mila Nirmala Sari Hasibuan, and Syaiful Zuhri Harahap. "Forecasting Health Sector Stock Prices using ARIMAX Method." Sinkron 7, no. 2 (May 2, 2022): 641–48. http://dx.doi.org/10.33395/sinkron.v7i2.11418.
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In daily stock trading activities, stock prices can experience ups and downs. The rise and fall of stock prices occurs due to changes in supply and demand for these shares. The COVID-19 pandemic did not have a negative effect, instead it had a positive impact on stock prices in health companies. companies in the health sector experienced a fairly good profit of 10.46% in the fourth quarter of 2021. This fact made investors interested in buying shares in companies in the health sector in the hope of selling them when demand increased, resulting in doubled profits. Stock conditions continue to fluctuate every day, making investors need to pay attention and study the past data of the health sector company that will be selected before deciding to invest. Therefore, it is necessary to forecast stock prices in the health sector for the next several periods as a step in making investment decisions. The health sector companies that will be modeled are PT Kimia Farma (Persero) Tbk and PT Kalbe Farma Tbk. The method used in this study is the ARIMAX model. The test and analysis results show that based on the RMSE and MAPE values, the best model is ARIMAX(5,13) for PT Kalbe Farma Tbk shares with a MAPE value of 1% in in-sample data and 0.6% in out-sample data.
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Rahmi, Nur Silviyah. "PERAMALAN INFLOW UANG KARTAL BANK INDONESIA KPW TASIKMALAYA JAWA BARAT DENGAN METODE KLASIK DAN MODERN." Jurnal Statistika Universitas Muhammadiyah Semarang 8, no. 2 (November 30, 2020): 166. http://dx.doi.org/10.26714/jsunimus.8.2.2020.166-174.
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Ketersediaan uang kartal di Bank Indonesia (BI) dapat ditinjau melalui arus keluar masuknya uang kartal yang disebut dengan istilah inflow. Banyaknya uang yang beredar di masyarakat akan berpengaruh pada kondisi perekonomian suatu negara, sehingga Bank Indonesia (BI) menyusun perencanaan kebutuhan uang rupiah. Penelitian ini bertujuan untuk meramalkan inflow uang kartal di KPw Bank Indonesia (BI) Tasikmalaya dengan menggunakan pemodelan ARIMA, ARIMAX, Metode Dekomposisi, Metode Winter’s, MLP (Multilayer Perceptron) atau FFNN (Feed Forward Neural Network), Regresi Time Series, Metode Naïve dan Model Hybrid. Dari delapan metode runtun waktu tersebut baik klasik maupun modern akan dicari metode mana yang memberikan hasil akurasi ramalan yang terbaik dengan kriteria RMSE, MAPE dan MAD. Kesimpulan yang dihasilkan yaitu Hybrid ARIMA-NN yang merupakan gabungan dari model ARIMA dengan neural network tidak menjamin kinerja hasil peramalan yang lebih baik. Seperti yang disebutkan dalam hasil M3 Competition, semakin kompleks metode yang digunakan belum tentu metode tersebut menghasilkan akurasi yang lebih baik dibandingkan metode sederhana (klasik). Pada ramalan data inflow KPw BI Tasikmalaya Jawa Barat ini, menghasilkan kesimpulan bahwa metode regresi time series memiliki nilai kriteria pemodelan paling kecil dibandingkan dengan metode lainnya.
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JACKSON, M. L., D. PETERSON, J. C. NELSON, S. K. GREENE, S. J. JACOBSEN, E. A. BELONGIA, R. BAXTER, and L. A. JACKSON. "Using winter 2009–2010 to assess the accuracy of methods which estimate influenza-related morbidity and mortality." Epidemiology and Infection 143, no. 11 (December 12, 2014): 2399–407. http://dx.doi.org/10.1017/s0950268814003276.
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SUMMARYWe used the winter of 2009–2010, which had minimal influenza circulation due to the earlier 2009 influenza A(H1N1) pandemic, to test the accuracy of ecological trend methods used to estimate influenza-related deaths and hospitalizations. We aggregated weekly counts of person-time, all-cause deaths, and hospitalizations for pneumonia/influenza and respiratory/circulatory conditions from seven healthcare systems. We predicted the incidence of the outcomes during the winter of 2009–2010 using three different methods: a cyclic (Serfling) regression model, a cyclic regression model with viral circulation data (virological regression), and an autoregressive, integrated moving average model with viral circulation data (ARIMAX). We compared predicted non-influenza incidence with actual winter incidence. All three models generally displayed high accuracy, with prediction errors for death ranging from −5% to −2%. For hospitalizations, errors ranged from −10% to −2% for pneumonia/influenza and from −3% to 0% for respiratory/circulatory. The Serfling and virological models consistently outperformed the ARIMAX model. The three methods tested could predict incidence of non-influenza deaths and hospitalizations during a winter with negligible influenza circulation. However, meaningful mis-estimation of the burden of influenza can still result with outcomes for which the contribution of influenza is low, such as all-cause mortality.
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Lu, Shaobo. "Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model." Computational Intelligence and Neuroscience 2021 (November 12, 2021): 1–10. http://dx.doi.org/10.1155/2021/1026978.
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Based on the BP neural network and the ARIMA model, this paper predicts the nonlinear residual of GDP and adds the predicted values of the two models to obtain the final predicted value of the model. First, the focus is on the ARMA model in the univariate time series. However, in real life, forecasts are often affected by many factors, so the following introduces the ARIMAX model in the multivariate time series. In the prediction process, the network structure and various parameters of the neural network are not given in a systematic way, so the operation of the neural network is affected by many factors. Each forecasting method has its scope of application and also has its own weaknesses caused by the characteristics of its own model. Secondly, this paper proposes an effective combination method according to the GDP characteristics and builds an improved algorithm BP neural network price prediction model, the research on the combination of GDP prediction model is currently mostly focused on the weighted form, and this article proposes another combination, namely, error correction. According to the price characteristics, we determine the appropriate number of hidden layer nodes and build a BP neural network price prediction model based on the improved algorithm. Validation of examples shows that the error-corrected GDP forecast model is also better than the weighted GDP forecast model, which shows that error correction is also a better combination of forecasting methods. The forecast results of BP neural network have lower errors and monthly prices. The relative error of prediction is about 2.5%. Through comparison with the prediction results of the ARIMA model, in the daily price prediction, the relative error of the BP neural network prediction is 1.5%, which is lower than the relative error of the ARIMA model of 2%.