Journal articles: 'ARIMA'

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Panjaitan, Helmi, Alan Prahutama, and Sudarno Sudarno. "PERAMALAN JUMLAH PENUMPANG KERETA API MENGGUNAKAN METODE ARIMA, INTERVENSI DAN ARFIMA (Studi Kasus : Penumpang Kereta Api Kelas Lokal EkonomiDAOP IV Semarang)." Jurnal Gaussian 7, no. 1 (February 28, 2018): 96–109. http://dx.doi.org/10.14710/j.gauss.v7i1.26639.

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Autoregressive Integrated Moving Average (ARIMA) is stationary time series model after differentiation. Differentiation value of ARIMA method is an integer so it is only able to model in the short term. The best model using ARIMA method is ARIMA([13]; 1; 0) with an MSE value of 1,870844. The Intervention method is a model for time series data which in practice has extreme fluctuations both up and down. In the data plot the number of train passengers was found to be extreme fluctuation. The data used was from January 2009 to June 2017 where fluctuation up significantly in January 2016 (T=85 to T=102) so the intervention model that was suspected was a step function. The best model uses the Intervention step function is ARIMA ([13]; 1; 1) (b=0; s=18; r=0) with MSE of 1124. Autoregressive Fractionally Integrated Moving Average (ARFIMA) method is a development of the ARIMA method. The advantage of the ARFIMA method is the non-integer differentiation value so that it can overcome long memory effect that can not be solve with the ARIMA method. ARFIMA model is capable of modeling high changes in the long term (long term persistence) and explain long-term and short-term correlation structures at the same time. The number of local economy class train passengers in DAOP IV Semarang contains long memory effects, so the ARFIMA method is used to obtain the best model. The best model obtained is the ARMA(0; [1,13]) model with the differential value is 0,367546, then the model can be written into ARFIMA (0; d; [1,13]) with an MSE value of 0,00964. Based on the analysis of the three methods, the best method of analyzing the number of local economy class train passengers in DAOP IV Semarang is the ARFIMA method with the model is ARFIMA (0; 0,367546; [1,13]). Keywords: Train Passengers, ARIMA, Intervention, ARFIMA, Forecasting

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ALKALI, MUSA ABUBAKAR. "ASSESSING THE FORECASTING PERFORMANCE OF ARIMA AND ARIMAX MODELS OF RESIDENTIAL PRICES IN ABUJA NIGERIA." Asia Proceedings of Social Sciences 4, no. 1 (April 17, 2019): 4–6. http://dx.doi.org/10.31580/apss.v4i1.528.

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This paper compared the out of sample forecasting ability of two Box-Jenkins ARIMA family models: ARIMAX and ARIMA. The forecasting models were tested to forecast real estate residential price in Abuja, Nigeria with quarterly data of average sales of residential price from the first quarter of year 2000 to the last quarter of year 2017. The result shows that the ARIMAX forecasting models, with macroeconomic factors as exogenous variables such as the household income, interest rate, gross domestic products, exchange rate and crude oil price and their lags, provide the best out of sample forecasting models for 2 bedroom, 3 bedroom, 4 bedroom and 5 bedroom, than ARIMA models. Generally, both ARIMA and ARIMAX models are good for short term forecasting modelling.

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Bielak, Jarosław. "Prognozowanie rynku pracy woj. lubelskiego z wykorzystaniem modeli ARIMA i ARIMAX." Barometr Regionalny. Analizy i Prognozy, no. 1 (19) (May 13, 2010): 27–44. http://dx.doi.org/10.56583/br.1379.

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W artykule przedstawiono metodę prognozowania rynku pracy – poziomu bezrobocia i przeciętnego zatrudnienia – w woj. lubelskim w oparciu o modele ARIMA i ARIMAX. Dodatkowymi zmiennymi egzogenicznymi wprowadzanymi do standardowych modeli ARIMA były szeregi wartości indeksu nastrojów gospodarczych. Pokazano różnice we wskaźnikach charakteryzujących jakość prognoz generowanych przez model ARIMAX i „czysty” model ARIMA. Uwzględniono modele budowane dla danych kwartalnych i dla danych miesięcznych oraz omówiono sposób konwersji kwartalnych szeregów czasowych indeksu nastrojów gospodarczych do szeregów miesięcznych. Wykonano analizę weryfikującą rzeczywistą przydatność takiej metody prognozowania i korzyści płynące z jej stosowania.

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Grillenzoni, Carlo. "ARIMA Processes with ARIMA Parameters." Journal of Business & Economic Statistics 11, no. 2 (April 1993): 235. http://dx.doi.org/10.2307/1391375.

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Grillenzoni, Carlo. "ARIMA Processes With ARIMA Parameters." Journal of Business & Economic Statistics 11, no. 2 (April 1993): 235–50. http://dx.doi.org/10.1080/07350015.1993.10509952.

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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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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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TAMUKE, Edmund, Emerson Abraham JACKSON, and Abdulai SILLAH. "FORECASTING INFLATION IN SIERRA LEONE USING ARIMA AND ARIMAX: A COMPARATIVE EVALUATION. MODEL BUILDING AND ANALYSIS TEAM." Theoretical and Practical Research in the Economic Fields 9, no. 1 (June 30, 2018): 63. http://dx.doi.org/10.14505/tpref.v9.1(17).07.

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The study has provided empirical investigation of both ARIMA and ARIMAX methodology as a way of providing forecast of Headline Consumer Price Index (HCPI) for Sierra Leone based on data collected from the Sierra Leone Statistical Office and the Bank of Sierra Leone. In this, the main research question of addressing outcomes from in and out-of-sample forecast were provided using the Static technique and this shows that both methodologies were proved to have tracked past and future occurrences of HCPI with minimal margin of error as indicated in the MAPE results. In a similar note, the key objective of identifying whether the ARIMAX methodology or the ARIMA methodology is a better predictor of forecasting future trends in HCPI. However, on the whole, both ARIMA and ARIMAX seem to have provided very good outcome in predicting future events of HCPI, particularly when Static technique is used as the option for forecasting outcomes, with the ARIMAX marginally coming out as the preferred choice on the basis of its evaluation outcomes.

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ISMAIL, NUR AFIQAH, NURIN ALYA RAMZI, and Pauline Jin Wee Mah. "FORECASTING THE UNEMPLOYMENT RATE IN MALAYSIA DURING COVID-19 PANDEMIC USING ARIMA AND ARFIMA MODELS." MALAYSIAN JOURNAL OF COMPUTING 7, no. 1 (February 28, 2022): 982. http://dx.doi.org/10.24191/mjoc.v7i1.14641.

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The unemployment issue is one of the most common problems faced by many countries around the world. The unemployment rates in developed countries often fluctuate throughout time. Similarly, Malaysia is also affected by the inconsistent unemployment rate especially during the COVID-19 pandemic. Therefore, in order to understand the trend better, ARIMA and ARFIMA were used to model and forecast the unemployment rate in Malaysia in this study. The dataset on the unemployment rate in Malaysia from January 2010 until July 2021 was obtained from Bank Negara Malaysia (BNM) official portal. The best time series models found were ARIMA (2, 1, 2) and ARFIMA (0, −0.2339, 0). The performance of the models was evaluated using mean absolute percentage error (MAPE), mean absolute error (MAE) and root mean square error (RMSE). It appeared that the ARFIMA model emerged as a better forecast model since it had better performance compared to ARIMA in forecasting the unemployment rate in Malaysia.

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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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Sukarna, Sukarna, Muhammad Abdy, Aswi Aswi, and Nurkaila Kaito. "Peramalan Jumlah Kedatangan Wisatawan Mancanegara di Sulawesi Selatan Menggunakan Model ARFIMA." Journal of Mathematics Computations and Statistics 5, no. 2 (October 31, 2022): 129. http://dx.doi.org/10.35580/jmathcos.v5i2.38793.

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Pariwisata dianggap sebagai suatu aset yang strategis untuk mendorong pembangunan pada wilayah-wilayah tertentu yang mempunyai potensi objek wisata. Faktor-faktor yang mempengaruhi wisatawan mancanegara berkunjung ke suatu wilayah negara, diantaranya nilai tukar mata uang, inflasi disuatu wilayah kunjungan wisatawan, dan letak geografis suatu wilayah negara. Peningkatan yang tidak terduga pada jumlah kunjungan wisatawan ini dapat berdampak kesulitan bagi para pelaku wisatawan dalam hal memberikan pelayanan terbaik dan sebaliknya jika terjadi penurunan jumlah kunjungan wisatawan hal yang dikhawatirkan akan terjadi pengangguran.Oleh karena itu, diperlukan suatu peramalan yang dapat memberikan informasi atau gambaran pada proses jumlah kunjungan wisatawan mancanegara. Dalam analisis runtun waktu terdapat data yang memiliki ciri proses jangka pendek dan data yang memiliki ciri proses jangka panjang. Model yang dapat menangani kedua jenis data ini adalah model autoregressive fractionally integrated moving average (ARFIMA). Model ARFIMA merupakan pengembangan dari model ARIMA, dengan differencing bernilai pecahan. Penelitian ini bertujuan untuk menentukan model ARFIMA pada peramalan jumlah kunjungan wisatawan mancanegara Sulawesi Selatan di masa yang akan datang. Pada penelitian ini, nilai AIC antara ARFIMA([1,8],d,0) dengan d ̂_gph=0,02 dan ARFIMA(0,d,1) dengan d ̂_(R/S)=0,12 relatif sama, hasil komparasi dengan model ARIMA memberikan hasil bahwa tidak diperoleh nilai ARIMA yang sesuai sehingga penulis menggunakan model ARFIMA untuk peramalan dan hasil peramalan jumlah kedatangan wisatawan mancanegara Sulawesi Selatan dapat dilihat dengan perbandingan data out sample.Keywords: Parawisata, peramalan, ARFIMA.

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Sasi, Archana, and Thiruselvan Subramanian. "Forecasting stochastic consumer portability visitation pattern in fair price shops of India." Journal of Information and Optimization Sciences 44, no. 3 (2023): 439–54. http://dx.doi.org/10.47974/jios-1364.

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In India, the Public Distribution System (PDS) is a critical tool for accomplishing the aim of “Zero Hunger”. Despite the enormous resources used, PDS has several inefficiencies that are caused by the monopoly of agents engaged in last-mile grain supply. Various state governments in India have been employing portability as an innovative solution to address this problem. In this article, we examined a huge-scale data on the deployment of portable beneficiaries arriving in a particular FPS of Kerala state in India over three years. A comparison is made between Auto-Regressive Integrated Moving Average (ARIMA) method which makes forecasts in univariate data and ARIMA with exogenous variables called ARIMAX. We followed Mean Absolute Percentage Error (MAPE) and Mean Absolute Deviation (MAD) as the accuracy performance measure of the models and observed that the ARIMAX model outperforms the ARIMA model with the least forecasting errors.

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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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Prendin, Francesco, José-Luis Díez, Simone Del Favero, Giovanni Sparacino, Andrea Facchinetti, and Jorge Bondia. "Assessment of Seasonal Stochastic Local Models for Glucose Prediction without Meal Size Information under Free-Living Conditions." Sensors 22, no. 22 (November 10, 2022): 8682. http://dx.doi.org/10.3390/s22228682.

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Accurate blood glucose (BG) forecasting is key in diabetes management, as it allows preventive actions to mitigate harmful hypoglycemic/hyperglycemic episodes. Considering the encouraging results obtained by seasonal stochastic models in proof-of-concept studies, this work assesses the methodology in two datasets (open-loop and closed-loop) recorded in free-living conditions. First, similar postprandial glycemic profiles are grouped together with fuzzy C-means clustering. Then, a seasonal stochastic model is identified for each cluster. Finally, real-time BG forecasting is performed by weighting each model’s prediction. The proposed methodology (named C-SARIMA) is compared to other linear and nonlinear black-box methods: autoregressive integrated moving average (ARIMA), its variant with input (ARIMAX), a feed-forward neural network (NN), and its modified version (NN-X) fed by BG, insulin, and carbohydrates (timing and dosing) information for several prediction horizons (PHs). In the open-loop dataset, C-SARIMA grants a median root-mean-squared error (RMSE) of 20.13 mg/dL (PH = 30) and 27.23 mg/dL (PH = 45), not significantly different from ARIMA and NN. Over a longer PH, C-SARIMA achieves an RMSE = 31.96 mg/dL (PH = 60) and RMSE = 33.91 mg/dL (PH = 75), significantly outperforming the ARIMA and NN, without significant differences from the ARIMAX for PH ≥ 45 and the NN-X for PH ≥ 60. Similar results hold on the closed-loop dataset: for PH = 30 and 45 min, the C-SARIMA achieves an RMSE = 21.63 mg/dL and RMSE = 29.67 mg/dL, not significantly different from the ARIMA and NN. On longer PH, the C-SARIMA outperforms the ARIMA for PH > 45 and the NN for PH > 60 without significant differences from the ARIMAX for PH ≥ 45. Although using less input information, the C-SARIMA achieves similar performance to other prediction methods such as the ARIMAX and NN-X and outperforming the CGM-only approaches on PH > 45min.

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Adekanmbi et al.,, Adekanmbi et al ,. "ARIMA and ARIMAX Stochastic Models for Fertility in Nigeria." International Journal of Mathematics and Computer Applications Research 7, no. 5 (2017): 1–20. http://dx.doi.org/10.24247/ijmcaroct20171.

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Nimesh, Ruby, Sangeeta Arora, Kalpana Kusum Mahajan, and Amar Nath Gill. "Predicting air quality using ARIMA, ARFIMA and HW smoothing." Model Assisted Statistics and Applications 9, no. 2 (March 26, 2014): 137–49. http://dx.doi.org/10.3233/mas-130285.

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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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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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Miftahuddin, Miftahuddin, Vivi Dina Melani, Muhammad Subianto, and Indah Manfaati Nur. "PERBANDINGAN NILAI AKURASI PERAMALAN MODEL TERBAIK ARFIMA-GPH DAN INTERVENSI MULTI INPUT DALAM PERAMALAN IHPBI." Jurnal Statistika Universitas Muhammadiyah Semarang 10, no. 1 (April 29, 2022): 1. http://dx.doi.org/10.26714/jsunimus.10.1.2022.1-6.

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Indeks Harga Perdagangan Besar Indonesia (IHPBI) diperlukan sebagai suatu penunjuk awal dalam analisis harga konsumen, dimana ketika terjadi inflasi maka stabilitas ekonomi Indonesia akan mulai terganggu. Sehingga untuk menekan laju inflasi pemerintah perlu mengambil suatu kebijakan menaikkan suku bunga sebagai satu solusi. Penelitian ini bertujuan untuk melihat IHPBI dalam 3 tahun ke depan melalui peramalan runtun waktumenggunakan metode ARFIMA-GPH dan intervensi multi input. Hal ini dilakukan untuk mengetahui pergerakan IHPBI selama 3 tahun kedepan dan untuk membandingkan kedua metode tersebut. Hasil yang diperoleh menunjukkan bahwa model yang dipilih adalah ARFIMA (1,d,0) dengan nilai d 0,1579, intervensi multi input pada Januari 2009 dengan ARIMA (1,1,1) orde (b=0, s=1, r=1 ) dan intervensi pada November 2013 dengan orde ARIMA (1,1,2) (b=1, s=1, r=0). Peramalan IHPBI untuk 3 tahun ke depan meningkat secara perlahan setiap bulannya. Metode terbaik berdasarkan perbandingan nilai akurasi peramalan adalah intervensi Januari 2013 dengan ARIMA(1,1,2) dan orde (b=1, s=1, r=0), memiliki nilai akurasi MAE dan MAPE terkecil, yaitu MAE sebesar 0,0119 dan MAPE sebesar 0,9079% yang menunjukkan bahwa metode dan model dalam peramalan sangat baik karena nilai akurasi model peramalan mendekati 0.PERBANDINGAN NILAI AKURASI PERAMALAN MODEL TERBAIK ARFIMA-GPH DAN INTERVENSI MULTI INPUT DALAM PERAMALAN IHPBI

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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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Kumar, Manish, and M. Thenmozhi. "Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models." International Journal of Banking, Accounting and Finance 5, no. 3 (2014): 284. http://dx.doi.org/10.1504/ijbaaf.2014.064307.

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Pierre, Agbessi Akuété, Salami Adekunlé Akim, Agbosse Kodjovi Semenyo, and Birregah Babiga. "Peak Electrical Energy Consumption Prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU Approaches." Energies 16, no. 12 (June 15, 2023): 4739. http://dx.doi.org/10.3390/en16124739.

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Forecasting peak electrical energy consumption is important because it allows utilities to properly plan for the production and distribution of electrical energy. This reduces operating costs and avoids power outages. In addition, it can help reduce environmental impact by allowing for more efficient power generation and reducing the need for additional fossil fuels during periods of high demand. In the current work, electric power consumption data from “Compagnie Electrique du Benin (CEB)” was used to deduce the peak electric power consumption at peak hours. The peak consumption of electric power was predicted using hybrid approaches based on traditional time series prediction methods (autoregressive integrated moving average (ARIMA)) and deep learning methods (long short-term memory (LSTM), gated recurrent unit (GRU)). The ARIMA approach was used to model the trend term, while deep learning approaches were employed to interpret the fluctuation term, and the outputs from these models were combined to provide the final result. The hybrid approach, ARIMA-LSTM, provided the best prediction performance with root mean square error (RMSE) of 7.35, while for the ARIMA-GRU hybrid approach, the RMSE was 9.60. Overall, the hybrid approaches outperformed the single approaches, such as GRU, LSTM, and ARIMA, which exhibited RMSE values of 18.11, 18.74, and 49.90, respectively.

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Maya Sierra, Giuliana, and Nini Johana Marin Rodríguez. "Modelación y comovimientos de la tasa de cambio colombiana, 2011-2017." Revista de Métodos Cuantitativos para la Economía y la Empresa 28 (November 8, 2019): 301–41. http://dx.doi.org/10.46661/revmetodoscuanteconempresa.2966.

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La tasa de cambio está influenciada por múltiples factores macroeconómicos nacionales e internacionales, lo que genera altos niveles de incertidumbre. El objetivo de esta investigación es la construcción de modelos ARIMA-GARCH y ARIMAX-GARCH como herramienta para el pronóstico de la tasa de cambio en Colombia a partir de los retornos diarios de los precios de cierre USD/COP y su análisis de correlación dinámica con algunas variables de interés. Los resultados sugieren que la incorporación de variables exógenas significativas dentro de la modelación ARIMAX-GARCH con correlación persistente según el modelo DCC (por sus siglas en inglés Dinamic Conditional Correlation) al par USD/COP genera pronósticos fuera de muestra con mejor desempeño que los modelos univariados ARIMA-GARCH.

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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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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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Mahmad Azan, Atiqa Nur Azza, Nur Faizatul Auni Mohd Zulkifly Mototo, and Pauline Jin Wee Mah. "The Comparison between ARIMA and ARFIMA Model to Forecast Kijang Emas (Gold) Prices in Malaysia using MAE, RMSE and MAPE." Journal of Computing Research and Innovation 6, no. 3 (September 13, 2021): 22–33. http://dx.doi.org/10.24191/jcrinn.v6i3.225.

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Gold is known as the most valuable commodity in the world because it is a universal currency recognized by every single bank across the globe. Thus, many people were interested in investing gold since gold market was always steadier compared to other investment (Khamis and Awang, 2020). However, the credibility of gold was questionable due to the changes in gold prices caused by a variety of circumstances (Henriksen, 2018). Hence, information on the inflation of gold prices were needed to understand the trend in order to plan for the future in accordance with international gold price standards. The aim of this study was to identify the trend of Kijang Emas monthly average prices in Malaysia from the year 2010 to 2021, to determine the best fit time series model for Kijang Emas prices in Malaysia and using univariate time series models to forecast Kijang Emas prices in Malaysia. The ARIMA and ARFIMA models were used in this study to model and forecast the prices of gold (Kijang Emas) in Malaysia. Each of the actual monthly Kijang Emas prices for 2021 were found to be within the 95% predicted intervals for both the ARIMA and ARFIMA models. The performances for each model were checked by considering the values of MAE, RMSE and MAPE. From the findings, all the MAE, RMSE and MAPE values showed that the ARFIMA model emerged as the better model in forecasting the Kijang Emas prices in Malaysia compared to the ARIMA model.

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Ahmar, Ansari Saleh, Suryo Guritno, Abdurakhman, Abdul Rahman, Awi, Alimuddin, Ilham Minggi, et al. "Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO)." Journal of Physics: Conference Series 954 (January 2018): 012010. http://dx.doi.org/10.1088/1742-6596/954/1/012010.

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Riestiansyah, Faiq, Devina Damayanti, Miranda Reswara, and Ronny Susetyoko. "Perbandingan metode ARIMA dan ARIMAX dalam Memprediksi Jumlah Wisatawan Nusantara di Pulau Bali." Jurnal Infomedia 7, no. 2 (December 9, 2022): 58. http://dx.doi.org/10.30811/jim.v7i2.3336.

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Indonesia memiliki berbagai potensi pemanfaatan yang berbeda tergantung dari sumber daya alamnya seperti bahan tambang, lahan pertanian, pariwisata dan lain-lain. Untuk meningkatkan pendapatan pada sektor pariwisata diperlukan data peramalan jumlah wisatawan yang berkunjung ke Pulau Bali. Data hasil peramalan tersebut dapat menjadi acuan untuk pengembangan dan pengoptimalisasian hal yang perlu diperbaiki di sektor kepariwisataan ini. Tujuan dari dilakukannya penelitian ini adalah untuk mengetahui perbandingan hasil prediksi terhadap Jumlah Wisatawan Nusantara yang berkunjung ke Pulau Bali. Salah satu model yang sering digunakan untuk masalah peramalan adalah model ARIMA. Model ARIMA yang juga disebut Runtut Waktu Box-Jenkins ini hanya cocok digunakan untuk kasus peramalan jangka pendek, karena jika digunakan untuk peramalan jangka panjang, model ini biasanya akan cenderung menghasilkan grafik time series datar. Setelah melakukan kedua pemodelan (ARIMA dan ARIMAX) selanjutnya membandingkan performa kedua model tersebut dalam melakukan prediksi Jumlah Wisatawan Nusantara yang berkunjung ke Pulau Bali dalam waktu tertentu dengan melihat error (RMSE) dari masing - masing model. Semakin rendah nilai RMSE maka semakin baik model tersebut bekerja dalam melakukan prediksi. Harapannya hasil dari penelitian ini dapat dimanfaatkan oleh siapapun yang memiliki kepentingan dalam pengembangan sektor pariwisata di Pulau Bali.

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Kumar, Sohan, and Mahesh Kumar. "Forecasting of Onion Price in Patna District through ARIMA Model." Environment and Ecology 41, no. 3 (July 2023): 1299–308. http://dx.doi.org/10.60151/envec/riar1851.

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ABSTRACT The present study entitled “Forecasting of onion price in Patna district in Bihar through autoregressive Integrated Moving Average (ARIMA) model”. Forecasting of onion price plays an important role in many decisions by policy maker. The secondary data of onion price were collected for 2002 to 2015 from Agriculture marketing (agmarknet.gov.in). The data from 2002 to 2015 were used for analysis of forecasting onion price and validity tests were also calculated. After study, it was found that the ARIMA (1,0,0) model is best fitted among all the models namely ARIMA (0,0,0), ARIMA (0,0,1), ARIMA (0,1,1), ARIMA (0,1,2), ARIMA (1,0,1), ARIMA (1,0,2), ARIMA (2,0,0), ARIMA (2,0,1), ARIMA (2,1,0), ARIMA (2,1,1), ARIMA (2,1,2).The parameters of all these models were computed and tested for their significance. Various statistics were also computed for selecting the adequate and parsimonious model i.e., t-test and chi-square test. This is supported by low value of MAPE, MAE, RMSE, BIC for forecasting of onion price in Patna district of Bihar. Forecasting of onion price for the next four years were calculated by the selected ARIMA model. The results showed that there was a lot of fluctuation in onion price.

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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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Díaz Sosa, María Eliana, Edwin Andrés Cruz Pérez, and Wilmer Dario Pineda Ríos. "Modelamiento del precio de la papa criolla en el departamento de Cundinamarca por medio de series de tiempo y modelos dinámicos." Comunicaciones en Estadística 14, no. 1 (February 1, 2021): 31–52. http://dx.doi.org/10.15332/23393076.6633.

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El presente trabajo tiene como objetivo evaluar el comportamiento y pronóstico del precio de la papa criolla en el departamento de Cundinamarca, según los factores climáticos desde enero de 2012 hasta abril de 2018. Para ello, se tomaron en consideración, por un lado, análisis basados en series de tiempo (ARIMA, ARIMAX) y, por el otro, modelos lineales dinámicos (con y sin covariables). En los modelos trabajados se usaron como variables las condiciones climáticas de la zona en cuestión, a las cuales se les aplicó un método de imputación de datos debido a la ausencia de información. Luego fueron agrupados en tres factores construidos por Análisis Factorial para Series de Tiempo (TSFA). Finalmente, se procedió a comparar los indicadores de los cuatro modelos, llegando a la conclusión de que los modelos ARIMA Y ARIMAX generan las mejores predicciones respecto del precio de la papa criolla en el departamento de Cundinamarca.

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Mah, P. J. W., N. A. M. Ihwal, and N. Z. Azizan. "FORECASTING FRESH WATER AND MARINE FISH PRODUCTION IN MALAYSIA USING ARIMA AND ARFIMA MODELS." MALAYSIAN JOURNAL OF COMPUTING 3, no. 2 (December 31, 2018): 81. http://dx.doi.org/10.24191/mjoc.v3i2.4887.

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Malaysia is surrounded by sea, rivers and lakes which provide natural sources of fish for human consumption. Hence, fish is one source of protein supply to the country and fishery is a sub-sector that contribute to the national gross domestic product. Since fish forecasting is crucial in fisheries management for managers and scientists, time series modelling can be one useful tool. Time series modelling have been used in many fields of studies including the fields of fisheries. In a previous research, the ARIMA and ARFIMA models were used to model marine fish production in Malaysia and the ARFIMA model emerged to be a better forecast model. In this study, we consider fitting the ARIMA and ARFIMA to both the marine and freshwater fish production in Malaysia. The process of model fitting was done using the “ITSM 2000, version 7.0” software. The performance of the models were evaluated using the mean absolute error, root mean square error and mean absolute percentage error. It was found in this study that the selection of the best fit model depends on the forecast accuracy measures used.

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Peng, Zhang, Farman Ullah Khan, Faridoon Khan, Parvez Ahmed Shaikh, Dai Yonghong, Ihsan Ullah, and Farid Ullah. "An Application of Hybrid Models for Weekly Stock Market Index Prediction: Empirical Evidence from SAARC Countries." Complexity 2021 (December 6, 2021): 1–10. http://dx.doi.org/10.1155/2021/5663302.

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The foremost aim of this research was to forecast the performance of three stock market indices using the multilayer perceptron (MLP), recurrent neural network (RNN), and autoregressive integrated moving average (ARIMA) on historical data. Moreover, we compared the extrapolative abilities of a hybrid of ARIMA with MLP and RNN models, which are called ARIMA-MLP and ARIMA-RNN. Because of the complicated and noisy nature of financial data, we combine novel machine-learning techniques such as MLP and RNN with ARIMA model to predict the three stock market data. The data used in this study are taken from the Pakistan Stock Exchange, National Stock Exchange India, and Sri Lanka Stock Exchange. In the case of Pakistan, the findings show that the ARIMA-MLP and ARIMA-RNN beat the individual ARIMA, MLP, and RNN models in terms of accuracy. Similarly, in the case of Sri Lanka and India, the hybrid models show more robustness in terms of forecasting than individual ARIMA, MLP, and RNN models based on root-mean-square error and mean absolute error. Apart from this, ARIMA-MLP outperformed the ARIMA-RNN in the case of Pakistan and India, while in the context of Sri Lanka, ARIMA-RNN beat the ARIMA-MLP in forecasting. Our findings reveal that the hybrid models can be regarded as a suitable option for financial time-series forecasting.

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Yuliawanti, Felia Dria, Dian C. Rini Novitasari, Nanang Widodo, Abdulloh Hamid, and Wika Dianita Utami. "PENERAPAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) UNTUK PREDIKSI BILANGAN SUNSPOT." BAREKENG: Jurnal Ilmu Matematika dan Terapan 15, no. 3 (September 1, 2021): 555–64. http://dx.doi.org/10.30598/barekengvol15iss3pp555-564.

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Peristiwa magnetik pada matahari ditandai dengan salah satu tanda yaitu munculnya sunspot atau bintik matahari. Sunspot terletak di fotosfer matahari yang memiliki warna lebih gelap dari pancaran sekitarnya. Tujuan dari penelitian ini adalah untuk memprediksi bilangan sunspot dengan menggunakan metode ARIMA. Metode ARIMA dilakukan dengan melihat plot ACF dan PACF untuk mendapatkan model yang akan digunakan dalam prediksi. Penelitian ini menggunakan data bilangan sunspot yang dimulai dari bulan Januari tahun 1987 hingga bulan Desember 2019 sebanyak 396 data. Dari data tersebut didapatkan 4 model ARIMA yaitu ARIMA(3,1,2), ARIMA(3,1,1), ARIMA(2,1,2), ARIMA(2,1,1). Dari keempat model tersebut, model terbaik yang digunakan untuk prediksi yaitu ARIMA(2,1,2) dengan nilai AIC sebesar -884,87.

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Ramos, Karen Gail, and Irish Joan O. Ativo. "Forecasting Monthly Prices of Selected Agricultural Commodities in The Philippines Using ARIMA Model." International Journal of Research Publication and Reviews 04, no. 01 (2023): 1983–93. http://dx.doi.org/10.55248/gengpi.2023.4157.

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Prices of commodities affected both producers and consumers thus, determining its future value is relevant for future decision-making. This study aims to guide the policymakers in creating guidelines for the benefit of the producers and consumers of agricultural commodities like sitao, eggplant, tomato, whole chicken, pork ham, and pork liempo. The researchers analyzed the data behavior of the selected commodities for the years 2013-2022 which all be observed to have an upward trend with fluctuations. These fluctuations are found to be connected to different factors such as seasonality of production, surplus of volume, pest & diseases, typhoon devastation, and importation, among others. After the analysis of price behavior, the researcher then, forecasted the price of this agricultural produce using the ARIMA technique. The data was first tested for its stationary through Augmented Dicker Fuller (ADF) Test, which resulted in the first differencing. The results of the ARIMA technique revealed that ARIMA (2,1,2), ARIMA (8,1,3), ARIMA (9,1,3), ARIMA (67,1,29), ARIMA (1,1,35) ARIMA (3,1,2), ARIMA (1,13), ARIMA (3,1,6), ARIMA (3,1,2), and ARIMA (3,2,5) for the whole chicken, pork ham, pork belly, beef brisket, chicken egg, sitao, eggplant, tomato, carrot, and cabbage respectively, are the best-fit models to forecast the next five years (2023-2027) prices of the commodities

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Purnama, Drajat Indra. "Peramalan Harga Emas Saat Pandemi Covid-19 Menggunakan Model Hybrid Autoregressive Integrated Moving Average - Support Vector Regression." Jambura Journal of Mathematics 1, no. 1 (January 2, 2021): 52–65. http://dx.doi.org/10.34312/jjom.v1i1.8430.

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ABSTRAKInvestasi emas merupakan salah satu investasi yang menjadi favorit dimasa pandemi Covid 19 seperti sekarang ini. Hal ini dikarenakan harga emas yang nilainya relatif fluktuatif tetapi menunjukkan tren peningkatan. Investor dituntut pandai dalam berinvestasi emas, mampu memprediksi peluang dimasa yang akan datang. Salah satu model peramalan data deret waktu adalah model Autoregressive Integrated Moving Average (ARIMA). Model ARIMA baik digunakan pada data yang berpola linear tetapi jika digunakan pada data data nonlinear keakuratannya menurun. Untuk mengatasi permasalahan data nonlinear dapat menggunakan model Support Vector Regression (SVR). Pengujian linearitas pada data harga emas menunjukkan adanya pola data linear dan nonlinear sekaligus sehingga digunakan kombinasi ARIMA dan SVR yaitu model hybrid ARIMA-SVR. Hasil peramalan menggunakan model hybrid ARIMA-SVR menunjukkan hasil lebih baik dibanding model ARIMA. Hal ini dibuktikan dengan nilai MAPE model hybrid ARIMA-SVR lebih kecil dibandingkan nilai MAPE model ARIMA. Nilai MAPE model hybrid ARIMA-SVR sebesar 0,355 pada data training dan 4,001 pada data testing, sedangkan nilai MAPE model ARIMA sebesar 0,903 pada data training dan 4,076 pada data testing.ABSTRACTGold investment is one of the favorite investments during the Covid 19 pandemic as it is today. This is because the price of gold is relatively volatile but shows an increasing trend. Investors are required to be smart in investing in gold, able to predict future opportunities. One of the time series data forecasting models is the Autoregressive Integrated Moving Average (ARIMA) model. The ARIMA model is good for use on linear patterned data but if it is used on nonlinear data the accuracy decreases. To solve the problem of nonlinear data, you can use the Support Vector Regression (SVR) model. The linearity test on the gold price data shows that there are linear and nonlinear data patterns at the same time so that a combination of ARIMA and SVR is used, namely the ARIMA-SVR hybrid model. Forecasting results using the ARIMA-SVR hybrid model show better results than the ARIMA model. This is evidenced by the MAPE value of the ARIMA-SVR hybrid model which is smaller than the MAPE value of the ARIMA model. The MAPE value of the ARIMA-SVR hybrid model is 0.355 on the training data and 4.001 on the testing data, while the MAPE value of the ARIMA model is 0.903 in the training data and 4.076 in the testing data.

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Purnama, Drajat Indra. "Peramalan Harga Emas Saat Pandemi Covid-19 Menggunakan Model Hybrid Autoregressive Integrated Moving Average - Support Vector Regression." Jambura Journal of Mathematics 3, no. 1 (January 2, 2021): 52–65. http://dx.doi.org/10.34312/jjom.v3i1.8430.

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ABSTRAKInvestasi emas merupakan salah satu investasi yang menjadi favorit dimasa pandemi Covid 19 seperti sekarang ini. Hal ini dikarenakan harga emas yang nilainya relatif fluktuatif tetapi menunjukkan tren peningkatan. Investor dituntut pandai dalam berinvestasi emas, mampu memprediksi peluang dimasa yang akan datang. Salah satu model peramalan data deret waktu adalah model Autoregressive Integrated Moving Average (ARIMA). Model ARIMA baik digunakan pada data yang berpola linear tetapi jika digunakan pada data data nonlinear keakuratannya menurun. Untuk mengatasi permasalahan data nonlinear dapat menggunakan model Support Vector Regression (SVR). Pengujian linearitas pada data harga emas menunjukkan adanya pola data linear dan nonlinear sekaligus sehingga digunakan kombinasi ARIMA dan SVR yaitu model hybrid ARIMA-SVR. Hasil peramalan menggunakan model hybrid ARIMA-SVR menunjukkan hasil lebih baik dibanding model ARIMA. Hal ini dibuktikan dengan nilai MAPE model hybrid ARIMA-SVR lebih kecil dibandingkan nilai MAPE model ARIMA. Nilai MAPE model hybrid ARIMA-SVR sebesar 0,355 pada data training dan 4,001 pada data testing, sedangkan nilai MAPE model ARIMA sebesar 0,903 pada data training dan 4,076 pada data testing.ABSTRACTGold investment is one of the favorite investments during the Covid 19 pandemic as it is today. This is because the price of gold is relatively volatile but shows an increasing trend. Investors are required to be smart in investing in gold, able to predict future opportunities. One of the time series data forecasting models is the Autoregressive Integrated Moving Average (ARIMA) model. The ARIMA model is good for use on linear patterned data but if it is used on nonlinear data the accuracy decreases. To solve the problem of nonlinear data, you can use the Support Vector Regression (SVR) model. The linearity test on the gold price data shows that there are linear and nonlinear data patterns at the same time so that a combination of ARIMA and SVR is used, namely the ARIMA-SVR hybrid model. Forecasting results using the ARIMA-SVR hybrid model show better results than the ARIMA model. This is evidenced by the MAPE value of the ARIMA-SVR hybrid model which is smaller than the MAPE value of the ARIMA model. The MAPE value of the ARIMA-SVR hybrid model is 0.355 on the training data and 4.001 on the testing data, while the MAPE value of the ARIMA model is 0.903 in the training data and 4.076 in the testing data.

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'Aina, Maula Qorri, Putriaji Hendikawati, and Walid Walid. "Pemodelan Runtun Waktu Harga Saham Bulanan BBRI.JK dengan Metode MODWT-ARIMA." Unnes Journal of Mathematics 11, no. 1 (May 30, 2022): 69–79. http://dx.doi.org/10.15294/ujm.v11i1.29154.

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MODWT-ARIMA is a time series modeling that combines the MODWT process and the ARIMA process. The MODWT process is used as pre-processing data while the ARIMA process as a time series modeling for data from MODWT decomposition. This study aims to show that time series modeling with a combined MODWT-ARIMA process provides more accurate forecast result compared to the ARIMA model. The modeled data is monthly time period data of BBRIâ€™s stock price started from January 2018 to July 2018. Accuracy measurement of the forecasting result is based on the RMSE value. The result is the MODWT-ARIMA model has a RMSE value which is smaller than the ARIMA model with RMSE Forecasting value using MODWT-ARIMA method for the period January 2018 to July 2018 are, 3687,560, 3571,892, 3287,686, 3072,610, 2832,533, 3147,472, 2964,491. The result of diagnostic checking from ARIMA model for D2, D3, and S3, shows that the residual model is not white noise while of the ARIMA model for the time series of monthly stock prices show thet the residual model is white noise. Theoritically, a model that has no white noiseâ€™s residual is considered to be less able to describe the properties of the observed data and further residual modeing should be done. However, this research is sufficient for the ARIMA model and it turns out that it has been able to show that the MODWT-ARIMA model is more effective than the ARIMA model.

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YÜCESAN, MELİH. "YSA, ARIMA ve ARIMAX Yöntemleriyle Satış Tahmini: Beyaz Eşya Sektöründe bir Uygulama - Sales Forecast with YSA, ARIMA and ARIMAX Methods: An Application in the White Goods Sector." Journal of Business Research - Turk 10, no. 1 (March 30, 2018): 689–706. http://dx.doi.org/10.20491/isarder.2018.414.

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Al-Qazzaz, Redha Ali, and Suhad A. Yousif. "High performance time series models using auto autoregressive integrated moving average." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 1 (July 1, 2022): 422. http://dx.doi.org/10.11591/ijeecs.v27.i1.pp422-430.

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Forecasting techniques have received considerable interest from both researchers and academics because of the unique characteristics of businesses and their influence on several areas of the economy. Most academics utilize the autoregressive integrated mov ing average (ARIMA) approach to forecasting the future. However, researchers face challenges, such as analyzing the data and selecting the appropriate ARIMA parameters, especially with large datasets. This study investigates the use of the automatic ARIMA (Auto ARIMA) function for forecasting Brent oil prices. It demonstrates the benefits of using Auto ARIMA over ARIMA for determining the appropriate ARIMA parameters based on measures such as root mean square error ( RMSE ) , mean absolute error ( MAE ) , and aka ike information criterion ( AIC ) without requiring the attention of an expert data scientist as it bypasses several steps needed for manual ARIMA. Auto ARIMA produced an RMSE of 12.5539 and an AIC of 1877.224, which are comparable to the values resulting fr om the manual ARIMA with the help of expert data scientists; thus, it saves analysis time and offers the best model result.

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Pitaloka, Riski Arum, Sugito Sugito, and Rita Rahmawati. "PERBANDINGAN METODE ARIMA BOX-JENKINS DENGAN ARIMA ENSEMBLE PADA PERAMALAN NILAI IMPOR PROVINSI JAWA TENGAH." Jurnal Gaussian 8, no. 2 (May 30, 2019): 194–207. http://dx.doi.org/10.14710/j.gauss.v8i2.26648.

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Import is activities to enter goods into the territory of a country, both commercial and non-commercial include goods that will be processed domestically. Import is an important requirement for industry in Central Java. The increase in high import values can cause deficit in the trade balance. Appropriate information about the projected amount of imports is needed so that the government can anticipate a high increase in imports through several policies that can be done. The forecasting method that can be used is ARIMA Box-Jenkins. The development of modeling in the field of time series forecasting shows that forecasting accuracy increases if it results from the merging of several models called ensemble ARIMA. The ensemble method used is averaging and stacking. The data used are monthly import value data in Central Java from January 2010 to December 2018. Modeling time series with Box-Jenkins ARIMA produces two significant models, namely ARIMA (2,1,0) and ARIMA (0,1,1). Both models are combined using the ARIMA ensemble averaging and stacking method. The best model chosen from the ARIMA method and ensemble ARIMA based on the least RMSE value is the ARIMA model (2,1,0) with RMSE value of 185,8892 Keywords: Import, ARIMA, ARIMA Ensemble, Stacking, Averaging

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Ilie, Ovidiu-Dumitru, Roxana-Oana Cojocariu, Alin Ciobica, Sergiu-Ioan Timofte, Ioannis Mavroudis, and Bogdan Doroftei. "Forecasting the Spreading of COVID-19 across Nine Countries from Europe, Asia, and the American Continents Using the ARIMA Models." Microorganisms 8, no. 8 (July 30, 2020): 1158. http://dx.doi.org/10.3390/microorganisms8081158.

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Since mid-November 2019, when the first SARS-CoV-2-infected patient was officially reported, the new coronavirus has affected over 10 million people from which half a million died during this short period. There is an urgent need to monitor, predict, and restrict COVID-19 in a more efficient manner. This is why Auto-Regressive Integrated Moving Average (ARIMA) models have been developed and used to predict the epidemiological trend of COVID-19 in Ukraine, Romania, the Republic of Moldova, Serbia, Bulgaria, Hungary, USA, Brazil, and India, these last three countries being otherwise the most affected presently. To increase accuracy, the daily prevalence data of COVID-19 from 10 March 2020 to 10 July 2020 were collected from the official website of the Romanian Government GOV.RO, World Health Organization (WHO), and European Centre for Disease Prevention and Control (ECDC) websites. Several ARIMA models were formulated with different ARIMA parameters. ARIMA (1, 1, 0), ARIMA (3, 2, 2), ARIMA (3, 2, 2), ARIMA (3, 1, 1), ARIMA (1, 0, 3), ARIMA (1, 2, 0), ARIMA (1, 1, 0), ARIMA (0, 2, 1), and ARIMA (0, 2, 0) models were chosen as the best models, depending on their lowest Mean Absolute Percentage Error (MAPE) values for Ukraine, Romania, the Republic of Moldova, Serbia, Bulgaria, Hungary, USA, Brazil, and India (4.70244, 1.40016, 2.76751, 2.16733, 2.98154, 2.11239, 3.21569, 4.10596, 2.78051). This study demonstrates that ARIMA models are suitable for making predictions during the current crisis and offers an idea of the epidemiological stage of these regions.

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Derbentsev, Vasily, Natalia Datsenko, Olga Stepanenko, and Vitaly Bezkorovainyi. "Forecasting cryptocurrency prices time series using machine learning approach." SHS Web of Conferences 65 (2019): 02001. http://dx.doi.org/10.1051/shsconf/20196502001.

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This paper describes the construction of the short-term forecasting model of cryptocurrencies’ prices using machine learning approach. The modified model of Binary Auto Regressive Tree (BART) is adapted from the standard models of regression trees and the data of the time series. BART combines the classic algorithm classification and regression trees (C&RT) and autoregressive models ARIMA. Using the BART model, we made a short-term forecast (from 5 to 30 days) for the 3 most capitalized cryptocurrencies: Bitcoin, Ethereum and Ripple. We found that the proposed approach was more accurate than the ARIMA-ARFIMA models in forecasting cryptocurrencies time series both in the periods of slow rising (falling) and in the periods of transition dynamics (change of trend).

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Unggara, Ilham, Aina Musdholifah, and Anny Kartika Sari. "Optimization of ARIMA Forecasting Model using Firefly Algorithm." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 13, no. 2 (April 30, 2019): 127. http://dx.doi.org/10.22146/ijccs.37666.

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Time series prediction aims to control or recognize the behavior of the system based on the data in a certain period of time. One of the most widely used method in time series prediction is ARIMA (Autoregressive Integrated Moving Average). However, ARIMA has a weakness in determining the optimal model. firefly algorithm is used to optimize ARIMA model (p, d, q). by finding the smallest AIC (Akaike Information Criterion) value in determining the best ARIMA model. The data used in the study are daily stock data JCI period January 2013 until August 2016 and data of foreign tourist visits to Indonesia period January 1988 to November 2017.Based on testing, for JCI data, obtained predicted results with Box-Jenkins ARIMA model produces RMSE 49.72, whereas the prediction with the ARIMA Optimization model yielded RMSE 49.48. For the data of Foreign Tourist Visits, the predicted results with the Box-Jenkins ARIMA model resulted in RMSE 46088.9, whereas the predicted results with ARIMA optimization resulted in RMSE 44678.4. From these results it can be concluded that the optimization of ARIMA model with Firefly Algorithm produces better forecasting model than ARIMA model without Optimization.

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Jin, Yongchao, Renfang Wang, Xiaodie Zhuang, Kenan Wang, Honglian Wang, Chenxi Wang, and Xiyin Wang. "Prediction of COVID-19 Data Using an ARIMA-LSTM Hybrid Forecast Model." Mathematics 10, no. 21 (October 28, 2022): 4001. http://dx.doi.org/10.3390/math10214001.

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The purpose of this study is to study the spread of COVID-19, establish a predictive model, and provide guidance for its prevention and control. Considering the high complexity of epidemic data, we adopted an ARIMA-LSTM combined model to describe and predict future transmission. A new method of the ARIMA-LSTM model paralleling by weight of regression coefficient was proposed. Then, we used the ARIMA-LSTM model paralleling by weight of regression coefficient, ARIMA model, and ARIMA-LSTM series model to predict the epidemic data in China, and we found that the ARIMA-LSTM model paralleling by weight of regression coefficient had the best prediction accuracy. In the ARIMA-LSTM model paralleling by weight of regression coefficient, MSE = 4049.913, RMSE = 63.639, MAPE = 0.205, R2 = 0.837, MAE = 44.320. In order to verify the effectiveness of the ARIMA-LSTM model paralleling by weight of regression coefficient, we compared the ARIMA-LSTM model paralleling by weight of regression coefficient with the SVR model and found that ARIMA-LSTM model paralleling by weight of regression coefficient has better prediction accuracy. It was further verified with the epidemic data of India and found that the prediction accuracy of the ARIMA-LSTM model paralleling by weight of regression coefficient was still higher than that of the SVR model. In the ARIMA-LSTM model paralleling by weight of regression coefficient, MSE = 744,904.6, RMSE = 863.079, MAPE = 0.107, R2 = 0.983, MAE = 580.348. Finally, we used the ARIMA-LSTM model paralleling by weight of regression coefficient to predict the future epidemic situation in China. We found that in the next 60 days, the epidemic situation in China will become a steady downward trend.

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Ordoñez Mercado, Alipio Francisco. "Modelos híbridos SARIMA-ANN para pronósticos de la COVID-19 en el Perú." Revista IECOS 22, no. 1 (December 27, 2021): 7–22. http://dx.doi.org/10.21754/iecos.v22i1.1332.

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Se ha construido modelos híbridos ANN-ARIMA por remodelamiento, para realizar los pronósticos de los nuevos casos de contagios por Covid-19 en el Perú, para ello se extrajo y uso los casos confirmados de Covid-19 entre el periodo 06/03/20 hasta el 28/02/21, desde la plataforma de los datos abiertos del Ministerio de Salud. Los resultados hallados indican que los 02 mejores modelos corresponden al modelo hibrido multiplicativo NNAR (27,1,6) * ARIMA(3,0,2)(1,0,1), y al modelo hibrido aditivo NNAR (27,1,6) + ARIMA(1,0,1), cuyos valores del error medio absoluto porcentual(MAPE) se diferencian en tan solo el 0.575% por lo que proporcionan casi los mismos pronósticos. Considerando el promedio de los valores del MAPE para los 03 mejores modelos de cada categoría de modelamiento se ha determinado que los modelos híbridos NNAR-ARIMA son mejores que los modelos híbridos MLP-ARIMA, que modelos híbridos aditivos NNAR+ARIMA tienen una superioridad del 1.20% sobre los modelos híbridos multiplicativos NNAR*ARIMA; mientras que la superioridad del modelo hibrido aditivo MLP+ARIMA sobre el modelo hibrido multiplicativo MLP*ARIMA alcanza al 2.31%.

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Zhang, Xiaofan, Chao Liu, and Yuhang Qian. "Coal Price Forecast Based on ARIMA Model." Financial Forum 9, no. 4 (January 28, 2021): 180. http://dx.doi.org/10.18282/ff.v9i4.1530.

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<div>This paper analyzes and determines the decision variables and constraints, establishes the EECM-ARAMA model to analyze and research coal price forecasts. Firstly, we first confirm the influencing factors. Then, we conduct correlation coefficient tests on price and various factors, and get the strength of the correlation between each factor and price. The second is to establish a coal price prediction model. Firstly, we use the EEMD method to transform the original price series into a stable time series, and then formulate three ARIMA models by comparing the size of the influencing factors and the parameter estimation results. After testing, we finally choose the ARIMA 03 model to predict the next 31 days, 35 Weekly and 36-month coal prices. Finally, we combine the models and ideas of the above issues to obtain factors that affect coal price changes and related price prediction models, and combine experience to give some feasible policy recommendations.</div>

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Siamba, Stephen, Argwings Otieno, and Julius Koech. "Application of ARIMA, and hybrid ARIMA Models in predicting and forecasting tuberculosis incidences among children in Homa Bay and Turkana Counties, Kenya." PLOS Digital Health 2, no. 2 (February 1, 2023): e0000084. http://dx.doi.org/10.1371/journal.pdig.0000084.

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Tuberculosis (TB) infections among children (below 15 years) is a growing concern, particularly in resource-limited settings. However, the TB burden among children is relatively unknown in Kenya where two-thirds of estimated TB cases are undiagnosed annually. Very few studies have used Autoregressive Integrated Moving Average (ARIMA), and hybrid ARIMA models to model infectious diseases globally. We applied ARIMA, and hybrid ARIMA models to predict and forecast TB incidences among children in Homa Bay and Turkana Counties in Kenya. The ARIMA, and hybrid models were used to predict and forecast monthly TB cases reported in the Treatment Information from Basic Unit (TIBU) system by health facilities in Homa Bay and Turkana Counties between 2012 and 2021. The best parsimonious ARIMA model that minimizes errors was selected based on a rolling window cross-validation procedure. The hybrid ARIMA-ANN model produced better predictive and forecast accuracy compared to the Seasonal ARIMA (0,0,1,1,0,1,12) model. Furthermore, using the Diebold-Mariano (DM) test, the predictive accuracy of ARIMA-ANN versus ARIMA (0,0,1,1,0,1,12) model were significantly different, p<0.001, respectively. The forecasts showed a TB incidence of 175 TB cases per 100,000 (161 to 188 TB incidences per 100,000 population) children in Homa Bay and Turkana Counties in 2022. The hybrid (ARIMA-ANN) model produces better predictive and forecast accuracy compared to the single ARIMA model. The findings show evidence that the incidence of TB among children below 15 years in Homa Bay and Turkana Counties is significantly under-reported and is potentially higher than the national average.

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Journal articles on the topic 'ARIMA'

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