Journal articles: 'ARIMA Modeling'

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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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Pfeifer, Phillip E., and Stuart Jay Deutsch. "Seasonal Space-Time ARIMA Modeling." Geographical Analysis 13, no. 2 (September 3, 2010): 117–33. http://dx.doi.org/10.1111/j.1538-4632.1981.tb00720.x.

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Pack, David J. "In defense of ARIMA modeling." International Journal of Forecasting 6, no. 2 (July 1990): 211–18. http://dx.doi.org/10.1016/0169-2070(90)90006-w.

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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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N. N. Jambhulkar, N. N. Jambhulkar. "Modeling of Rice Production in Punjab using ARIMA Model." International Journal of Scientific Research 2, no. 8 (June 1, 2012): 1–2. http://dx.doi.org/10.15373/22778179/aug2013/1.

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Susanti, Riana, and Askardiya Radmoyo Adji. "ANALISIS PERAMALAN IHSG DENGAN TIME SERIES MODELING ARIMA." Jurnal Manajemen Kewirausahaan 17, no. 1 (June 30, 2020): 97. http://dx.doi.org/10.33370/jmk.v17i1.393.

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ABSTRAK Prediksi harga saham merupakan hal yang selalu menarik minat investor dan pemangku kepentingan lain terhadap pasar saham. Dalam perdagangan saham, pergerakan IHSG yang akan datang dapat digunakan sebagai dasar untuk melakukan pengambilan keputusan pelaku investasi. Penelitian ini bertujuan menguji model time series Autoregressive Integrated Moving Average (ARIMA) untuk memprediksi IHSG di Bursa Efek Indonesia. ARIMA adalah model untuk menghasilkan perkiraan dari data historis. Data dalam penelitian ini dikumpulkan dari IHSG harian dari 2 Januari 2017 sampai 3 Januari 2018. Data diperoleh dari laporan bulanan yang dipublikasikan Bursa Efek Indonesia. Hasil prediksi menunjukkan bahwa model ini cukup akurat untuk peramalan. Hasil penelitian menunjukkan bahwa model ARIMA yang memiliki kinerja terbaik untuk memprediksi IHSG, yaitu model ARIMA (7,3,1) Kata Kunci: Prediksi harga saham, IHSG, Time Series, ARIMA

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Agrienvi. "Frits Fahridws Damanik." Agrienvi, Jurnal Ilmu Pertanian 13, no. 02 (February 3, 2020): 1–8. http://dx.doi.org/10.36873/aev.v13i02.657.

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ABSTRACTChili is one of the leading commodities of vegetables which has strategic value at national and regional levels.An unexpected increase in chili prices often results a surge of inflation and economic turmoil. Study and modeling ofchili production are needed as a planning and evaluation material for policy makers. One of the most frequently usedmethods in modeling and forecasting time series data is Autoregressive Integrated Moving Avarage (ARIMA). Theresults of ARIMA modeling on chili production data found that the data were unstationer conditions of the mean sothat must differenced while the data on the production of small chilli carried out the stages of data transformation anddifferencing due to the unstationer of data on variants and the mean. The best ARIMA model that can be appliedbased on the smallest AIC and MSE criteria for data on the amount of chili and small chilli production in CentralKalimantan Province is ARIMA (3,1,0).Keywords: modeling of chilli, forecasting of chilli, Autoregresive Integrated Moving Avarage, ARIMA, Box-Jenkins.

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Agrienvi. "DOI: https://doi.org/10.36873/ae , Frits Fahridws Damanik." Agrienvi: Jurnal Ilmu Pertanian 13, no. 02 (February 12, 2020): 1–8. http://dx.doi.org/10.36873/aev.v13i02.723.

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ABSTRACTChili is one of the leading commodities of vegetables which has strategic value at national and regional levels.An unexpected increase in chili prices often results a surge of inflation and economic turmoil. Study and modeling ofchili production are needed as a planning and evaluation material for policy makers. One of the most frequently usedmethods in modeling and forecasting time series data is Autoregressive Integrated Moving Avarage (ARIMA). Theresults of ARIMA modeling on chili production data found that the data were unstationer conditions of the mean so thatmust differenced while the data on the production of small chilli carried out the stages of data transformation anddifferencing due to the unstationer of data on variants and the mean. The best ARIMA model that can be applied basedon the smallest AIC and MSE criteria for data on the amount of chili and small chilli production in Central KalimantanProvince is ARIMA (3,1,0).Keywords: modeling of chilli, forecasting of chilli, Autoregresive Integrated Moving Avarage, ARIMA, Box-Jenkins.

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SAADAT, SH, M. SALEM, M. GHORANNEVISS, and P. KHORSHID. "Stochastic modeling of plasma mode forecasting in tokamak." Journal of Plasma Physics 78, no. 2 (November 11, 2011): 99–104. http://dx.doi.org/10.1017/s0022377811000456.

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AbstractThe structure of magnetohydrodynamic (MHD) modes has always been an interesting study in tokamaks. The mode number of tokamak plasma is the most important parameter, which plays a vital role in MHD instabilities. If it could be predicted, then the time of exerting external fields, such as feedback fields and Resonance Helical Field, could be obtained. Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average are useful models to predict stochastic processes. In this paper, we suggest using ARIMA model to forecast mode number. The ARIMA model shows correct mode number (m = 4) about 0.5 ms in IR-T1 tokamak and equations of Mirnov coil fluctuations are obtained. It is found that the recursive estimates of the ARIMA model parameters change as the plasma mode changes. A discriminator function has been proposed to determine plasma mode based on the recursive estimates of model parameters.

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Maxwell, Obubu, Ikediuwa Udoka Chinedu, Anabike Charles Ifeanyi, and Nwokike Chukwudike C. "On Modeling Murder Crimes in Nigeria." Scientific Review, no. 58 (August 1, 2018): 157–62. http://dx.doi.org/10.32861/sr.58.157.162.

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This paper examines the modelling and forecasting Murder crimes using Auto-Regressive Integrated Moving Average models (ARIMA). Twenty-nine years data obtained from Nigeria Information Resource Center were used to make predictions. Among the most effective approaches for analyzing time series data is the method propounded by Box and Jenkins, the Autoregressive Integrated Moving Average (ARIMA). The augmented Dickey-Fuller test for unit root was applied to the data set to investigate for Stationarity, the data set was found to be non-stationary hence transformed using first-order differencing to make them Stationary. The Stationarities were confirmed with time series plots. Statistical analysis was performed using GRETL software package from which, ARIMA (0, 1, 0) was found to be the best and adequate model for Murder crimes. Forecasted values suggest that Murder would slightly be on the increase.

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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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Bai, Fang. "Performance Reliability Modeling Based on ARIMA Model." Advanced Materials Research 452-453 (January 2012): 1049–53. http://dx.doi.org/10.4028/www.scientific.net/amr.452-453.1049.

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Performance reliability method (PRM) has great advantages on modeling with few failure data by using the performance degradation data. The purpose of this paper is to find the defects of PRM based on HOLT model, and PRM method based on ARIMA model was proposed by introducing the product fleet real-time performance reliability information to the measuring points. Finally, an example applying on the aero-engine remaining life prediction was taken to validate that PRM method based on ARIMA model can avoid variances errors derived from the formula, compared with the PRM model based on HOLT model. The relative error of the improved method has 11.765% lower. So we can draw a conclusion that this method is effective and feasible.

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Sim, So-Kumneth, Philipp Maass, and Pedro Lind. "Wind Speed Modeling by Nested ARIMA Processes." Energies 12, no. 1 (December 26, 2018): 69. http://dx.doi.org/10.3390/en12010069.

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Wind speed modelling is of increasing interest, both for basic research and for applications, as, e.g., for wind turbine development and strategies to construct large wind power plants. Generally, such modelling is hampered by the non-stationary features of wind speed data that, to a large extent, reflect the turbulent dynamics in the atmosphere. We study how these features can be captured by nested ARIMA models. In this approach, wind speed fluctuations in given time windows are modelled by one stochastic process, and the parameter variation between successive windows by another one. For deriving the wind speed model, we use 20 months of data collected at the FINO1 platform at the North Sea and use a variable transformation that best maps the wind speed onto a Gaussian random variable. We find that wind speed increments can be well reproduced for up to four standard deviations. The distributions of extreme variations, however, strongly deviate from the model predictions.

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Khorshid, Motaz, Assem Tharwat, Amer Bader, and Ahmed Omran. "The ARIMA versus Artificial Neural Network Modeling." IJCI. International Journal of Computers and Information 2, no. 1 (June 1, 2009): 30–40. http://dx.doi.org/10.21608/ijci.2009.33936.

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Bai, Fang. "Performance Reliability Modeling Based on ARIMA Model." Advanced Materials Research 452-453 (January 2012): 1049–53. http://dx.doi.org/10.4028/scientific5/amr.452-453.1049.

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Vasileiadou, Eleftheria, and Rens Vliegenthart. "Studying dynamic social processes with ARIMA modeling." International Journal of Social Research Methodology 17, no. 6 (July 9, 2013): 693–708. http://dx.doi.org/10.1080/13645579.2013.816257.

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Makwinja, Rodgers, Seyoum Mengistou, Emmanuel Kaunda, Tena Alemiew, Titus Bandulo Phiri, Ishmael Bobby Mphangwe Kosamu, and Chikumbusko Chiziwa Kaonga. "Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)." Forecasting 3, no. 1 (February 8, 2021): 39–55. http://dx.doi.org/10.3390/forecast3010004.

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Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

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Sun, Kaiying. "Equity Return Modeling and Prediction Using Hybrid ARIMA-GARCH Model." International Journal of Financial Research 8, no. 3 (June 12, 2017): 154. http://dx.doi.org/10.5430/ijfr.v8n3p154.

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In this paper, a hybrid ARIMA-GARCH model is proposed to model and predict the equity returns for three US benchmark indices: Dow Transportation, S&P 500 and VIX. Equity returns are univariate time series data sets, one of the methods to predict them is using the Auto-Regressive Integrated Moving Average (ARIMA) models. Despite the fact that the ARIMA models are powerful and flexible, they are not be able to handle the volatility and nonlinearity that are present in the time series data. However, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models are designed to capture volatility clustering behavior in time series. In this paper, we provide motivations and descriptions of the hybrid ARIMA-GARCH model. A complete data analysis procedure that involves a series of hypothesis testings and a model fitting procedure using the Akaike Information Criterion (AIC) is provided in this paper as well. Simulation results of out of sample predictions are also provided in this paper as a reference.

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Mayilsamy, Kathiresh, Maideen Abdhulkader Jeylani A,, Mahaboob Subahani Akbarali, and Haripranesh Sathiyanarayanan. "Modeling of a simplified hybrid algorithm for short-term load forecasting in a power system network." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 40, no. 3 (July 15, 2021): 676–88. http://dx.doi.org/10.1108/compel-01-2021-0005.

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Purpose The purpose of this paper is to develop a hybrid algorithm, which is a blend of auto-regressive integral moving average (ARIMA) and multilayer perceptron (MLP) for addressing the non-linearity of the load time series. Design/methodology/approach Short-term load forecasting is a complex process as the nature of the load-time series data is highly nonlinear. So, only ARIMA-based load forecasting will not provide accurate results. Hence, ARIMA is combined with MLP, a deep learning approach that models the resultant data from ARIMA and processes them further for Modelling the non-linearity. Findings The proposed hybrid approach detects the residuals of the ARIMA, a linear statistical technique and models these residuals with MLP neural network. As the non-linearity of the load time series is approximated in this error modeling process, the proposed approach produces accurate forecasting results of the hourly loads. Originality/value The effectiveness of the proposed approach is tested in the laboratory with the real load data of a metropolitan city from South India. The performance of the proposed hybrid approach is compared with the conventional methods based on the metrics such as mean absolute percentage error and root mean square error. The comparative results show that the proposed prediction strategy outperforms the other hybrid methods in terms of accuracy.

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Rahaman, Md, Balbhadra Thakur, Ajay Kalra, and Sajjad Ahmad. "Modeling of GRACE-Derived Groundwater Information in the Colorado River Basin." Hydrology 6, no. 1 (February 18, 2019): 19. http://dx.doi.org/10.3390/hydrology6010019.

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Groundwater depletion has been one of the major challenges in recent years. Analysis of groundwater levels can be beneficial for groundwater management. The National Aeronautics and Space Administration’s twin satellite, Gravity Recovery and Climate Experiment (GRACE), serves in monitoring terrestrial water storage. Increasing freshwater demand amidst recent drought (2000–2014) posed a significant groundwater level decline within the Colorado River Basin (CRB). In the current study, a non-parametric technique was utilized to analyze historical groundwater variability. Additionally, a stochastic Autoregressive Integrated Moving Average (ARIMA) model was developed and tested to forecast the GRACE-derived groundwater anomalies within the CRB. The ARIMA model was trained with the GRACE data from January 2003 to December of 2013 and validated with GRACE data from January 2014 to December of 2016. Groundwater anomaly from January 2017 to December of 2019 was forecasted with the tested model. Autocorrelation and partial autocorrelation plots were drawn to identify and construct the seasonal ARIMA models. ARIMA order for each grid was evaluated based on Akaike’s and Bayesian information criterion. The error analysis showed the reasonable numerical accuracy of selected seasonal ARIMA models. The proposed models can be used to forecast groundwater variability for sustainable groundwater planning and management.

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Esther, N., and N. Magdaline. "ARIMA Modeling to Forecast Pulses Production in Kenya." Asian Journal of Economics, Business and Accounting 2, no. 3 (January 10, 2017): 1–8. http://dx.doi.org/10.9734/ajeba/2017/32414.

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Geurts, Michael D., and J. Patrick Kelly. "“In defense of ARIMA modeling”, by D.J. Pack." International Journal of Forecasting 6, no. 4 (December 1990): 497–99. http://dx.doi.org/10.1016/0169-2070(90)90026-8.

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Won, Youjip, and Soohan Ahn. "GOP ARIMA: Modeling the nonstationarity of VBR processes." Multimedia Systems 10, no. 5 (June 20, 2005): 359–78. http://dx.doi.org/10.1007/s00530-005-0166-7.

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Rafidah, Ali, and Yacob Suhaila. "Modeling River Stream Flow Using Support Vector Machine." Applied Mechanics and Materials 315 (April 2013): 602–5. http://dx.doi.org/10.4028/www.scientific.net/amm.315.602.

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Support Vector Machine (SVM) is a new tool from Artificial Intelligence (AI) field has been successfully applied for a wide variety of problem especially in river stream flow forecasting. In this paper, SVM is proposed for river stream flow forecasting. To assess the effectiveness SVM, we used monthly mean river stream flow record data from Pahang River at Lubok Paku, Pahang. The performance of the SVM model is compared with the statistical Autoregressive Integrated Moving Average (ARIMA) and the result showed that the SVM model performs better than the ARIMA models to forecast river stream flow Pahang River.

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Azizi, Amir, Amir Yazid B. Ali, Loh Wei Ping, and Mohsen Mohammadzadeh. "Estimating and Modeling Uncertainties Affecting Production Throughput Using ARIMA-Multiple Linear Regression." Advanced Materials Research 488-489 (March 2012): 1263–67. http://dx.doi.org/10.4028/www.scientific.net/amr.488-489.1263.

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Throughput of each production stage cannot meet the demand in the real production system because of the disruptions and interruptions of the production line for example break time and scrap. On the other hand, demand changes over time due to volume variation and product redesign as the customers’ needs are changing. This situation leads to planning and controlling under uncertain condition. This paper proposes a hybrid model of autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR) for estimating and modeling the random variables of production line in order to forecast the throughput in presence of production variations and demand fluctuation. The random variables under consideration of this study are demand, break-time, scrap, and lead-time. The random variables are formulated in the MLR model, where the mean absolute percentage of error (MAPE) was 2.53%. Further, nine ARIMA models with different parameters in MLR model are fitted to the data and compared by their MAPE. The best model with the lowest MAPE was when the ARIMA parameters set for p=1, d=0, and q=3. Finally the proposed model using ARIMA-MLR is formulated by MAPE of 1.55%.

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Matskul, Valerii, Diana Okara, and Nataliia Podvalna. "The Ukraine and EU trade balance: prediction via various models of time series." SHS Web of Conferences 73 (2020): 01020. http://dx.doi.org/10.1051/shsconf/20207301020.

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This article is the first to study, simulate and forecast the monthly dynamics of the trade balance between Ukraine and the European Union for the period from 2005 to 2019. In the presented work, three types of models were used for modeling and forecasting: Automated Neural Networks, additive models ARIMA *ARIMAS (Autoregressive integrated moving average with season component) and Holts model with a damped trend. When modeling using the Automated Neural Networks module, various ensembles of networks and neuron activation functions in hidden layers were used. It turned out that Automated Neural Networks have poor prognostic ability (as in the case considered by us, when modeling insufficiently long series of dynamics). Therefore, when modeling and forecasting the dynamics of the Ukraine-EU trade balance, classical (so-called Box-Jenkins) time series models were used. In this case, the time series is divided into several components (in our case, terms): the main trend is the trend, the seasonal component and the random component (the so-called white noise). By smoothing the initial series, a trend was found, and an analysis of the autocorrelation functions revealed a one-year seasonality. Eliminating the trend and the seasonal component, we obtained a random component, which has a Gaussian distribution. This made it possible to apply first the ARIMA* ARIMAS additive model, and then the Holt model of exponential smoothing with a damped trend. Adequate models of Ukraine-EU trade balance dynamics have been obtained, according to which the forecast has been made. A comparative analysis of the models used. The best model was chosen for forecasting, which allowed to get a good forecast (in comparison with actual data).

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Mbithe Titus, Cecilia, Anthony Wanjoya, and Thomas Mageto. "Time Series Modeling of Guinea Fowls Production in Kenya Using the ARIMA and ARFIMA Models." International Journal of Data Science and Analysis 7, no. 1 (2021): 1. http://dx.doi.org/10.11648/j.ijdsa.20210701.11.

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Triyani, Winda, and Rina Reorita. "KAJIAN PEMODELAN DERET WAKTU: METODE VARIASI KALENDER YANG DIPENGARUHI OLEH EFEK VARIASI LIBURAN." Jurnal Ilmiah Matematika dan Pendidikan Matematika 4, no. 1 (June 29, 2012): 135. http://dx.doi.org/10.20884/1.jmp.2012.4.1.2948.

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Calendar variation method is a technique that combines ARIMA modeling and regression modeling. Calendar variation is a cyclical pattern with varying periods due to the different calendar date position for each year. There are two types of calendar variation, trading day variation and holiday variation. In this research, modeling of time series with holiday variation was studied and modification of the modeling was developed for the case of holiday effect due to Eid’s day occur. The case study was conducted to the data of train passenger number at DAOP V Purwokerto. It was found that the last model for the underlying data was the regression model with the residual following seasonal ARIMA (1,1,1)(0,0,1)12 without constant parameter.

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SHU, Y. "Wireless Traffic Modeling and Prediction Using Seasonal ARIMA Models." IEICE Transactions on Communications E88-B, no. 10 (October 1, 2005): 3992–99. http://dx.doi.org/10.1093/ietcom/e88-b.10.3992.

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Sowell, Fallaw. "Modeling long-run behavior with the fractional ARIMA model." Journal of Monetary Economics 29, no. 2 (April 1992): 277–302. http://dx.doi.org/10.1016/0304-3932(92)90016-u.

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Diarsih, Inas Husna, Tarno Tarno, and Agus Rusgiyono. "PEMODELAN PRODUKSI BAWANG MERAH DI JAWA TENGAH DENGAN MENGGUNAKAN HYBRID AUTOREGRESSIVE INTEGRATED MOVING AVERAGE – ADAPTIVE NEURO FUZZY INFERENCE SYSTEM." Jurnal Gaussian 7, no. 3 (August 29, 2018): 281–92. http://dx.doi.org/10.14710/j.gauss.v7i3.26661.

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Red onion is one of the strategic horticulture commodities in Indonesia considering its function as the main ingredients of the basic ingredients of Indonesian cuisine. In an effort to increase production to supply national necessary, Central Java as the main center of red onion production should be able to predict the production of several periods ahead to maintain the balance of national production. The purpose of this research is to get the best model to forecast the production of red onion in Central Java by ARIMA, ANFIS, and hybrid ARIMA-ANFIS method. Model accuracy is measured by the smallest RMSE and AIC values. The results show that the best model to modeling red onion production in Central Java is obtained by hybrid ARIMA-ANFIS model which is a combination between SARIMA ([2], 1, [12]) and residual ARIMA using ANFIS model with input et,1, et,2 on the grid partition technique, gbell membership function, and membership number of 2 that produce RMSE 12033 and AIC 21.6634. While ARIMA model yield RMSE 13301,24 and AIC 21,89807 with violation of assumption. And the ANFIS model produces RMSE 14832 and AIC 22,0777. This shows that ARIMA-ANFIS hybrid method is better than ARIMA and ANFIS.Keywords: production of red onion, ARIMA, ANFIS, hybrid ARIMA-ANFIS

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Suhartono, Suhartono, Salafiyah Isnawati, Novi Ajeng Salehah, Dedy Dwi Prastyo, Heri Kuswanto, and Muhammad Hisyam Lee. "Hybrid SSA-TSR-ARIMA for water demand forecasting." International Journal of Advances in Intelligent Informatics 4, no. 3 (November 11, 2018): 238. http://dx.doi.org/10.26555/ijain.v4i3.275.

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Water supply management effectively becomes challenging due to the human population and their needs have been growing rapidly. The aim of this research is to propose hybrid methods based on Singular Spectrum Analysis (SSA) decomposition, Time Series Regression (TSR), and Automatic Autoregressive Integrated Moving Average (ARIMA), known as hybrid SSA-TSR-ARIMA, for water demand forecasting. Monthly water demand data frequently contain trend and seasonal patterns. In this research, two groups of different hybrid methods were developed and proposed, i.e. hybrid methods for individual SSA components and for aggregate SSA components. TSR was used for modeling aggregate trend component and Automatic ARIMA for modeling aggregate seasonal and noise components separately. Firstly, simulation study was conducted for evaluating the performance of the proposed methods. Then, the best hybrid method was applied to real data sample. The simulation showed that hybrid SSA-TSR-ARIMA for aggregate components yielded more accurate forecast than other hybrid methods. Moreover, the comparison of forecast accuracy in real data also showed that hybrid SSA-TSR-ARIMA for aggregate components could improve the forecast accuracy of ARIMA model and yielded better forecast than other hybrid methods. In general, it could be concluded that the hybrid model tends to give more accurate forecast than the individual methods. Thus, this research in line with the third result of the M3 competition that stated the accuracy of hybrid method outperformed, on average, the individual methods being combined and did very well in comparison to other methods.

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Asghar Ali Shah, Syed, Nagina Zeb, and Alamgir Alamgir. "Forecasting Major Food Crops Production in Khyber Pakhtunkhwa,Pakistan." Journal of Applied and Advanced Research 2, no. 1 (March 9, 2017): 21. http://dx.doi.org/10.21839/jaar.2017.v2i1.40.

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The present study was undertaken to investigate forecasting of major food crops production in Khyber Pakhtunkhwa. The study was based on secondary data covers a period of about 30 years i.e. starting from 1984-85 to 2013-14, whereas, ARIMA modeling has been employed to fit the best time series model for major food crops production i.e. wheat, maize, sugarcane and rice. It reveals through the results that for major food crops production, the time series models which were found to be most suitable are as ARIMA (0, 2, 1), ARIMA (1, 2, 3), ARIMA (0, 2, 1) and random model ARIMA (0, 1, 0) respectively based on forecast evaluation criteria. It was concluded from the results of analyzed data that time series models were found adequate for forecasting major food crops production in Khyber Pakhtunkhwa.

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Boubaker, Sahbi. "Identification of monthly municipal water demand system based on autoregressive integrated moving average model tuned by particle swarm optimization." Journal of Hydroinformatics 19, no. 2 (January 6, 2017): 261–81. http://dx.doi.org/10.2166/hydro.2017.035.

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In this paper, a modeling-identification approach for the monthly municipal water demand system in Hail region, Saudi Arabia, is developed. This approach is based on an auto-regressive integrated moving average (ARIMA) model tuned by the particle swarm optimization (PSO). The ARIMA (p, d, q) modeling requires estimation of the integer orders p and q of the AR and MA parts; and the real coefficients of the model. More than being simple, easy to implement and effective, the PSO-ARIMA model does not require data pre-processing (original time-series normalization for artificial neural network (ANN) or data stationarization for traditional stochastic time-series (STS)). Moreover, its performance indicators such as the mean absolute percentage error (MAPE), coefficient of determination (R2), root mean squared error (RMSE) and average absolute relative error (AARE) are compared with those of ANN and STS. The obtained results show that the PSO-ARIMA outperforms the ANN and STS approaches since it can optimize simultaneously integer and real parameters and provides better accuracy in terms of MAPE (5.2832%), R2 (0.9375), RMSE (2.2111 × 105m3) and AARE (5.2911%). The PSO-ARIMA model has been implemented using 69 records (for both training and testing). The results can help local water decision makers to better manage the current water resources and to plan extensions in response to the increasing need.

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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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Jafarian-Namin, Samrad, Alireza Goli, Mojtaba Qolipour, Ali Mostafaeipour, and Amir-Mohammad Golmohammadi. "Forecasting the wind power generation using Box–Jenkins and hybrid artificial intelligence." International Journal of Energy Sector Management 13, no. 4 (November 4, 2019): 1038–62. http://dx.doi.org/10.1108/ijesm-06-2018-0002.

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Purpose The purpose of this paper is to forecast wind power generation in an area through different methods, and then, recommend the most suitable one using some performance criteria. Design/methodology/approach The Box–Jenkins modeling and the Neural network modeling approaches are applied to perform forecasting for the last 12 months. Findings The results indicated that among the tested artificial neural network (ANN) model and its improved model, artificial neural network-genetic algorithm (ANN-GA) with RMSE of 0.4213 and R2 of 0.9212 gains the best performance in prediction of wind power generation values. Finally, a comparison between ANN-GA and ARIMA method confirmed a far superior power generation prediction performance for ARIMA with RMSE of 0.3443 and R2 of 0.9480. Originality/value Performance of the ARIMA method is evaluated in comparison to several types of ANN models including ANN, and its improved model using GA as ANN-GA and particle swarm optimization (PSO) as ANN-PSO.

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Ruska, Ruslana, Andrii Aliluiko, Svitlana Plaskon, and Ivan Novosad. "FORECASTING THE OBJECTIVE NUMBER OF HIGHER EDUCATION INSTITUTIONS IN RELATION TO MODERN FACTORS." Economic Analysis, no. 30(3) (2020): 127–37. http://dx.doi.org/10.35774/econa2020.03.127.

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Introduction. Education is an indicator of the country's intellectual potential. Higher education is a strategic resource through which the state is competitive in the global labour market.complete higher education attests to the professional and cultural level of a considerable number of the population, especially young people, and is not only an indicator of economic growth but also of social stability. The purpose of investigation was determined by the analysis of current state of higher education institutions and the main influencing factors for them. The coefficient of interest of graduates in receiving higher education in Ukraine is determined on the basis of regression analysis. Arima models were constructed using time series theory for prediction of the number of higher education institutions for future periods. Purpose. The purpose of the study is the construction the predictive models of the dynamics of the number of future students and the number of higher education institutions in Ukraine. Methodology. Regression analysis is used as one of the main methods of scientific research in the process of writing the article; time series theory, in particular Arima modeling of the Statistica application package; methods of mathematical modeling, in particular approximating polynomials in the process of modeling the dynamics of the institutions of higher education and the number of students, to determine the ‘coefficient of interest’. Results. The study found that the number of institutions of higher education depends on the one hand on the time factor, on the other – on the number of students. Given that the number of students and the amount higher education institutions can be characterized as a dynamic process, the theory of time series, in particular Arima modelling, was applied. Using Arima models, the number of students and the number of higher education institutions for the next two years is predicted. The relative errors for these models are 6% and 0. 4%, respectively. Based on statistics on the number of graduates of all secondary education institutions and the number of students admitted to higher education institutions of Ukraine, a ‘coefficient of interest’ in higher education was derived, which allows predicting the number of future entrants. The Arima model predicts the number of Ukrainian students in foreign educational institutions. The obtained forecast values regarding the number of students, the amount of higher education institutions of Ukraine, by various methods, adequately reflect the real situation today.

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Mahiyuddin, Wan Rozita Wan, Nur Izzah Jamil, Zamtira Seman, Nurul Izzah Ahmad, Nor Aini Abdullah, Mohd Talib Latif, and Mazrura Sahani. "Forecasting Ozone Concentrations Using Box-Jenkins ARIMA Modeling in Malaysia." American Journal of Environmental Sciences 14, no. 3 (March 1, 2018): 118–28. http://dx.doi.org/10.3844/ajessp.2018.118.128.

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Mélard, G., and J. M. Pasteels. "Automatic ARIMA modeling including interventions, using time series expert software." International Journal of Forecasting 16, no. 4 (October 2000): 497–508. http://dx.doi.org/10.1016/s0169-2070(00)00067-4.

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Bianchi, Lisa, Jeffrey Jarrett, and R. Choudary Hanumara. "Improving forecasting for telemarketing centers by ARIMA modeling with intervention." International Journal of Forecasting 14, no. 4 (December 1998): 497–504. http://dx.doi.org/10.1016/s0169-2070(98)00037-5.

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Tran, N., and D. A. Reed. "Automatic arima time series modeling for adaptive I/O prefetching." IEEE Transactions on Parallel and Distributed Systems 15, no. 4 (April 2004): 362–77. http://dx.doi.org/10.1109/tpds.2004.1271185.

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Bako, Hadiza Yakubu. "Predictive Modeling of Pelagic Fish Catch using Seasonal ARIMA Models." Agriculture, Forestry and Fisheries 2, no. 3 (2013): 136. http://dx.doi.org/10.11648/j.aff.20130203.13.

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Yunus, Kalid, Torbjorn Thiringer, and Peiyuan Chen. "ARIMA-Based Frequency-Decomposed Modeling of Wind Speed Time Series." IEEE Transactions on Power Systems 31, no. 4 (July 2016): 2546–56. http://dx.doi.org/10.1109/tpwrs.2015.2468586.

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D, Desi Fransiska. "Rainfall Data Modeling in Simalungun Regency Using the Arima Box-Jenkins Method." International Journal of Basic and Applied Science 10, no. 1 (April 6, 2021): 21–27. http://dx.doi.org/10.35335/ijobas.v10i1.4.

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One of the components of the environment that determines the success of plant cultivation is climate. To predict rainfall, the author uses the ARIMA Box Jenkins method, which is a quantitative forecasting method. The data used are data for the period July 2012 to June 2017. In this study, the right model is the ARIMA model (2,0,2) with Xt = 4.05668 + 0.9416Xt-1 - 1.0039Xt-2 - 0, 8558et-1 + 0.9617et-2 + et which is used to forecast rainfall for the next 12 periods. The selection is based on the smallest MSE (average error squared) value of 0.033401954 and the smallest RMSE (root mean square error value), which is 0.001115691 and the smallest MAPE (absolute average error percentage) is -0 , 00801773.

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Syeda, JA. "Forecasting of Climatic Variables in Dinajpur of Bangladesh." Journal of Environmental Science and Natural Resources 10, no. 2 (November 29, 2018): 163–70. http://dx.doi.org/10.3329/jesnr.v10i2.39030.

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An attempt was made to forecast the 17 monthly climatic variables for 2005-2012 of Dinajpur using the univariate Box-Jenkin’s ARIMA (autoregressive integrated moving average) modeling techniques for 1948-2004. The 8 years data for 1973-1980 were missing and those data were replaced with the 4 years monthly forecasted data for 1948-1972 and 1981-2004 (reversing the years). The well fitted ARIMA (autoregressive integrated moving average) models were selected from the possible 16 ARIMA models based on the minimum root mean square forecasting errors (RMSFE) with the last 24 observations for all the cases and the residuals followed stationarity and normality. Several outliers were detected in the data which were replaced by the forecasted value. The fitted model for sunshine data (1989-2004) was found ARIMA (1, 1, 1)(1, 1, 1)12 and for evaporation data (1987-2000) was ARIMA (1, 1, 2)(1, 1, 1)12. . The findings supports that the changing term of the climatic variables may have adverse impacts on the crop production in this country.J. Environ. Sci. & Natural Resources, 10(2): 163-170 2017

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Chen, Yin Ping, Ai Ping Wu, Cui Ling Wang, Hai Ying Zhou, and Shu Xiu Feng. "Time Series Analysis of Pulmonary Tuberculosis Incidence: Forecasting by Applying the Time Series Model." Advanced Materials Research 709 (June 2013): 819–22. http://dx.doi.org/10.4028/www.scientific.net/amr.709.819.

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The main objective of this study is to identify the stochastic autoregressive integrated moving average (ARIMA) model to predict the pulmonary tuberculosis incidence in Qianan. Considering the Box-Jenkins modeling approach, the incidence of pulmonary tuberculosis was collected monthly from 2004 to 2010. The model ARIMA(0,1,1)12 was established finally and the residual sequence was a white noise sequence. Then, this model was used for calculating dengue incidence for the last 6 observations compared with observed data, and performed to predict the monthly incidence in 2011. It is necessary and practical to apply the approach of ARIMA model in fitting time series to predict pulmonary tuberculosis within a short lead time.

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Bokde, Neeraj, Andrés Feijóo, Nadhir Al-Ansari, Siyu Tao, and Zaher Mundher Yaseen. "The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling." Energies 13, no. 7 (April 3, 2020): 1666. http://dx.doi.org/10.3390/en13071666.

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In this research, two hybrid intelligent models are proposed for prediction accuracy enhancement for wind speed and power modeling. The established models are based on the hybridisation of Ensemble Empirical Mode Decomposition (EEMD) with a Pattern Sequence-based Forecasting (PSF) model and the integration of EEMD-PSF with Autoregressive Integrated Moving Average (ARIMA) model. In both models (i.e., EEMD-PSF and EEMD-PSF-ARIMA), the EEMD method is used to decompose the time-series into a set of sub-series and the forecasting of each sub-series is initiated by respective prediction models. In the EEMD-PSF model, all sub-series are predicted using the PSF model, whereas in the EEMD-PSF-ARIMA model, the sub-series with high and low frequencies are predicted using PSF and ARIMA, respectively. The selection of the PSF or ARIMA models for the prediction process is dependent on the time-series characteristics of the decomposed series obtained with the EEMD method. The proposed models are examined for predicting wind speed and wind power time-series at Maharashtra state, India. In case of short-term wind power time-series prediction, both proposed methods have shown at least 18.03 and 14.78 percentage improvement in forecast accuracy in terms of root mean square error (RMSE) as compared to contemporary methods considered in this study for direct and iterated strategies, respectively. Similarly, for wind speed data, those improvement observed to be 20.00 and 23.80 percentages, respectively. These attained prediction results evidenced the potential of the proposed models for the wind speed and wind power forecasting. The current proposed methodology is transformed into R package ‘decomposedPSF’ which is discussed in the Appendix.

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Nepal, Surendra Raj. "An Analysis of COVID-19 Cases in Nepal: A Modeling Approach." Journal of Institute of Science and Technology 25, no. 2 (December 25, 2020): 80–92. http://dx.doi.org/10.3126/jist.v25i2.33744.

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Unlike previous coronaviruses infections, COVID-19 has badly affected not only the health of people but also the socioeconomic activities of Nepal. It would help the government of Nepal to manage this crisis if a proper mechanism to predict COVID cases has been developed. This study aims to look for patterns of confirmed, recovery and death cases. Moreover, it tries to check whether Gompertz and Logistic model would be able to read the patterns of total confirmed and death cases. It also forecasts the total number of confirmed as well as death cases. Data from January 23, 2020 to October 30, 2020 obtained from the website of Wikipedia are used for analysis. Gompertz and Logistic models were fitted to the total number of confirmed and death cases and models are compared based on various criteria. Besides, an automatic ARIMA model was used to predict cumulative confirmed and death cases and the accuracy of the model was also checked. ARIMA model forecasted 347,812 confirmed cases and 1,754 death cases till December 31, 2020. At 95 % confidence interval, the confirmed cases were expected between 273,889 and 421,734 whereas death cases were estimated from 1,387 to 2,119. Both models were fitted well in both total confirmed cases and total death cases. It was found that the Logistic model fits better in total confirmed cases whereas in total death cases, the Gompertz model was better. ARIMA model precisely forecasted the number of confirmed and death cases.

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Faal, Maryam, and Farshad Almasganj. "ECG Signal Modeling Using Volatility Properties: Its Application in Sleep Apnea Syndrome." Journal of Healthcare Engineering 2021 (July 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/4894501.

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This study presents and evaluates the mathematical model to estimate the mean and variance of single-lead ECG signals in sleep apnea syndrome. Our objective is to use the volatility property of the ECG signal for modeling. ECG signal is a stochastic signal whose mean and variance are time-varying. So, we propose to decompose this nonstationarity into two additive components; a homoscedastic Autoregressive Integrated Moving Average (ARIMA) and a heteroscedastic time series in terms of Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH), where the former captures the linearity property and the latter the nonlinear characteristics of the ECG signal. First, ECG signals are segmented into one-minute segments. The heteroskedasticity property is then examined through various tests such as the ARCH/GARCH test, kurtosis, skewness, and histograms. Next, the ARIMA model is applied to signals as a linear model and EGARCH as a nonlinear model. The appropriate orders of models are estimated by using the Bayesian Information Criterion (BIC). We assess the effectiveness of our model in terms of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The data in this article is obtained from the Physionet Apnea-ECG database. Results show that the ARIMA-EGARCH model performs better than other models for modeling both apneic and normal ECG signals in sleep apnea syndrome.

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ERSHOV, EVGENY V., OLGA V. YUDINA, LYUDMILA N. VINOGRADOVA, and NIKITA I. SHAKHANOV. "EQUIPMENT CONDITION MODELING BASED ON RANDOM FOREST AND ARIMA MACHINE LEARNING ALGORITHM STACKING." Cherepovets State University Bulletin 4, no. 97 (2020): 32–40. http://dx.doi.org/10.23859/1994-0637-2020-4-97-3.

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The article discusses algorithms for constructing predicative models of the industrial equipment condition using data analysis and machine learning. The model is based on Random Forest (RF) and ARIMA (AR) algorithms. The authors consider approaches to learning algorithms and optimizing parameters. A block diagram of a time series predictive model applying stacking is presented, as well as an assessment of the simulation results.

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Journal articles on the topic 'ARIMA Modeling'

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