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Journal articles on the topic 'Regression with ARIMA Errors'

Author: Grafiati

Published: 9 September 2021

Last updated: 18 February 2022

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1

Furtado, Pedro. "Epidemiology SIR with Regression, Arima, and Prophet in Forecasting Covid-19." Engineering Proceedings 5, no. 1 (2021): 52. http://dx.doi.org/10.3390/engproc2021005052.

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Epidemiology maths resorts to Susceptible-Infected-Recovered (SIR)-like models to describe contagion evolution curves for diseases such as Covid-19. Other time series estimation approaches can be used to fit and forecast curves. We use data from the Covid-19 pandemic infection curves of 20 countries to compare forecasting using SEIR (a variant of SIR), polynomial regression, ARIMA and Prophet. Polynomial regression deg2 (POLY d(2)) on differentiated curves had lowest 15 day forecast errors (6% average error over 20 countries), SEIR (errors 25–68%) and ARIMA (errors 15–85%) were better for span

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Son, Heung-gu, Yunsun Kim, and Sahm Kim. "Time Series Clustering of Electricity Demand for Industrial Areas on Smart Grid." Energies 13, no. 9 (2020): 2377. http://dx.doi.org/10.3390/en13092377.

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This study forecasts electricity demand in a smart grid environment. We present a prediction method that uses a combination of forecasting values based on time-series clustering. The clustering of normalized periodogram-based distances and autocorrelation-based distances are proposed as the time-series clustering methods. Trigonometrical transformation, Box–Cox transformation, autoregressive moving average (ARMA) errors, trend and seasonal components (TBATS), double seasonal Holt–Winters (DSHW), fractional autoregressive integrated moving average (FARIMA), ARIMA with regression (Reg-ARIMA), an

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Bianco, A. M., M. García Ben, E. J. Martínez, and V. J. Yohai. "Outlier Detection in Regression Models with ARIMA Errors using Robust Estimates." Journal of Forecasting 20, no. 8 (2001): 565–79. http://dx.doi.org/10.1002/for.768.

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White, Alexander K., and Samir K. Safi. "The Efficiency of Artificial Neural Networks for Forecasting in the Presence of Autocorrelated Disturbances." International Journal of Statistics and Probability 5, no. 2 (2016): 51. http://dx.doi.org/10.5539/ijsp.v5n2p51.

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<p>We compare three forecasting methods, Artificial Neural Networks (ANNs), Autoregressive Integrated Moving Average (ARIMA) and Regression models. Using computer simulations, the major finding reveals that in the presence of autocorrelated errors ANNs perform favorably compared to ARIMA and regression for nonlinear models. The model accuracy for ANN is evaluated by comparing the simulated forecast results with the real data for unemployment in Palestine which were found to be in excellent agreement.</p>

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Guo, Ni, Wei Chen, Manli Wang, Zijian Tian, and Haoyue Jin. "Appling an Improved Method Based on ARIMA Model to Predict the Short-Term Electricity Consumption Transmitted by the Internet of Things (IoT)." Wireless Communications and Mobile Computing 2021 (April 10, 2021): 1–11. http://dx.doi.org/10.1155/2021/6610273.

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The rapid development of the Internet of Things (IoT) has brought a data explosion and a new set of challenges. It has been an emergency to construct a more robust and precise model to predict the electricity consumption data collected from the Internet of Things (IoT). Accurately forecasting the electricity consumption is a crucial technology for the planning of the energy resource which could lead to remarkable conservation of the building electricity consumption. This paper is focused on the electricity consumption forecasting of an office building with a small-scale dataset, and 117 daily

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Mohamed, Jama. "Time Series Modeling and Forecasting of Somaliland Consumer Price Index: A Comparison of ARIMA and Regression with ARIMA Errors." American Journal of Theoretical and Applied Statistics 9, no. 4 (2020): 143. http://dx.doi.org/10.11648/j.ajtas.20200904.18.

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Takyi Appiah, Sampson, Albert Buabeng, and N. K. Dumakor-Dupey. "Multivariate Analysis of the Effect of Climate Conditions on Gold Production in Ghana." Ghana Mining Journal 18, no. 1 (2018): 72–77. http://dx.doi.org/10.4314/gm.v18i1.9.

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The change in climatic conditions and its catastrophic effect on mining activities has become a source of worry for mining industries and therefore needs due attention. This study examined the effect some climate factors have on gold production in Ghana. First, a direct Multiple Linear Regression was applied on the climate factors with the aim of determining the relative effect of each factor on gold production which exhibited a time series structure. The consequence is that, the estimates of the coefficients and their standard errors will be wrongly estimated if the time series structure of t

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ROGALSKA, Magdalena, and Zdzisław HEJDUCKI. "COMPARATIVE ANALYSIS OF BUILDING PRODUCTION FORECASTING USING REGRESSION, NEURAL NETWORKS AND ARIMA METHODS." Scientific Journal of the Military University of Land Forces 160, no. 2 (2011): 285–96. http://dx.doi.org/10.5604/01.3001.0002.3006.

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The study analyzed the possibility of forecasting of Lower Silesia building production using regression methods, neural networks and ARIM (Autoregressive Integrated Moving Average). For the forecasting regression method was used daily weather data of Lower Silesia and the economic data - the number of employees in the construction sector and the average earnings of workers in this sector.The analysis of errors: ME, MAE, MPE, MAPE and Theil coefficients I, I2,I12, I22, I32 was performed. The way of further research was proposed.

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Wang, Yu, Changan Zhu, Xiaodong Ye, Jianghai Zhao, and Deji Wang. "Wind Speed Prediction based on Spatio-Temporal Covariance Model Using Autoregressive Integrated Moving Average Regression Smoothing." International Journal of Pattern Recognition and Artificial Intelligence 35, no. 08 (2021): 2159031. http://dx.doi.org/10.1142/s021800142159031x.

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It is essential to enhance the ability of wind speeds forecasting for wind energy and wind resource planning. For this purpose, a hybrid strategy has been proposed based on spatio-temporal covariance model which combined the spatio-temporal ordinary kriging (STOK) technology with autoregressive integrated moving average (ARIMA) regression smoothing method. This is because wind speed time series exhibits a long-term dependency. In the case study, both STOK method and ARIMA method are employed and their performances are compared. The ARIMA model can obtain a necessary and sufficient smoothing co

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Chen, Yining, and Robert A. Leitch. "An Analysis of the Relative Power Characteristics of Analytical Procedures." AUDITING: A Journal of Practice & Theory 18, no. 2 (1999): 35–69. http://dx.doi.org/10.2308/aud.1999.18.2.35.

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The overall objective of this study is to analyze the relative effectiveness and efficiency of several analytical procedures. To accomplish this, we test the power characteristics of analytical procedures in simulated business and economic environments. The analytical procedures we test include the Martingale, Census X-11, ARIMA, and stepwise regression expectation models. The power characteristics are measured by both positive and negative testing approaches, with and without accompanying tests of details, and with simple and dispersed error seeding patterns. The results suggest that the step

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Jones, Richard H. "Time series regression with unequally spaced data." Journal of Applied Probability 23, A (1986): 89–98. http://dx.doi.org/10.2307/3214345.

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Regression analysis with stationary errors is extended to the case when observations are not equally spaced. The errors are modelled as either a discrete-time ARMA process with missing observations, or as a continuous-time autoregression with observational error observed at arbitrary times. Using a state-space representation, a Kalman filter is used to calculate the exact likelihood. The linear regression coefficients are separated out of the likelihood so non-linear optimization is required only with respect to the parameters modelling the error structure.

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Jones, Richard H. "Time series regression with unequally spaced data." Journal of Applied Probability 23, A (1986): 89–98. http://dx.doi.org/10.1017/s0021900200117000.

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Regression analysis with stationary errors is extended to the case when observations are not equally spaced. The errors are modelled as either a discrete-time ARMA process with missing observations, or as a continuous-time autoregression with observational error observed at arbitrary times. Using a state-space representation, a Kalman filter is used to calculate the exact likelihood. The linear regression coefficients are separated out of the likelihood so non-linear optimization is required only with respect to the parameters modelling the error structure.

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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 fo

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Alupotha, Kalana N. "Regression with ARIMA Error Model for Government Expenditure in Sri Lanka." Journal of Applied Mathematics and Computation 5, no. 3 (2021): 165–70. http://dx.doi.org/10.26855/jamc.2021.09.003.

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Rahman, Khalilur, Margaretha Ari Anggorowati, and Agung Andiojaya. "Prediction and Simulation Spatio-Temporal Support Vector Regression for Nonlinear Data." International Journal on Information and Communication Technology (IJoICT) 6, no. 1 (2020): 31. http://dx.doi.org/10.21108/ijoict.2020.61.477.

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<p>Spatio-temporal model forecasting method is a forecasting model that combines forecasting with a function of time and space. This method is expected to be able to answer the challenge to produce more accurate and representative forecasting. Using the ability of method Support Vector Regression in dealing with data that is mostly patterned non-linear premises n adding a spatial element in the model of forecasting in the form of a model forecasting Spatio- Temporal. Some simulations have done with generating data that follows the Threshold Autoregressive model. The models are correlated

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Marković, Hrvoje, Bojana Dalbelo Bašić, Hrvoje Gold, Fangyan Dong, and Kaoru Hirota. "GPS Data-based Non-parametric Regression for Predicting Travel Times in Urban Traffic Networks." PROMET - Traffic&Transportation 22, no. 1 (2012): 1–13. http://dx.doi.org/10.7307/ptt.v22i1.159.

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A model for predicting travel times by mining spatio-temporal data acquired from vehicles equipped with Global Positioning System (GPS) receivers in urban traffic networks is presented. The proposed model, which uses k-nearest neighbour (kNN) non-parametric regression, is compared with models that use historical averages and the seasonal autoregressive integrated moving average (ARIMA) model. The main contribution is provision of a methodology for mining GPS data that involves examining areas that cannot be covered with conventional fixed sensors. The work confirms that the method that predict

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Wu, Rongning, and Qin Wang. "Shrinkage estimation for linear regression with ARMA errors." Journal of Statistical Planning and Inference 142, no. 7 (2012): 2136–48. http://dx.doi.org/10.1016/j.jspi.2012.02.047.

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Li, Changshun, Ziyang Xie, Bo Chen, et al. "Different Time Scale Distribution of Negative Air Ions Concentrations in Mount Wuyi National Park." International Journal of Environmental Research and Public Health 18, no. 9 (2021): 5037. http://dx.doi.org/10.3390/ijerph18095037.

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The concentration of negative air ions (NAIs) is an important indicator of air quality. Here, we analyzed the distribution patterns of negative air ion (NAI) concentrations at different time scales using statistical methods; then described the contribution of meteorological factors of the different season to the concentration of NAIs using correlation analysis and regression analysis; and finally made the outlook for the trends of NAI concentrations in the prospective using the auto regressive integrated moving average (ARIMA) models. The dataset of NAI concentrations and meteorological factor

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19

Wang, Ya-wen, Zhong-zhou Shen, and Yu Jiang. "Comparison of autoregressive integrated moving average model and generalised regression neural network model for prediction of haemorrhagic fever with renal syndrome in China: a time-series study." BMJ Open 9, no. 6 (2019): e025773. http://dx.doi.org/10.1136/bmjopen-2018-025773.

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ObjectivesHaemorrhagic fever with renal syndrome (HFRS) is a serious threat to public health in China, accounting for almost 90% cases reported globally. Infectious disease prediction may help in disease prevention despite some uncontrollable influence factors. This study conducted a comparison between a hybrid model and two single models in forecasting the monthly incidence of HFRS in China.DesignTime-series study.SettingThe People’s Republic of China.MethodsAutoregressive integrated moving average (ARIMA) model, generalised regression neural network (GRNN) model and hybrid ARIMA-GRNN model w

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20

Xu, Dehe, Qi Zhang, Yan Ding, and Huiping Huang. "Application of a Hybrid ARIMA–SVR Model Based on the SPI for the Forecast of Drought—A Case Study in Henan Province, China." Journal of Applied Meteorology and Climatology 59, no. 7 (2020): 1239–59. http://dx.doi.org/10.1175/jamc-d-19-0270.1.

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AbstractDrought forecasts could effectively reduce the risk of drought. Data-driven models are suitable forecast tools because of their minimal information requirements. The motivation for this study is that because most data-driven models, such as autoregressive integrated moving average (ARIMA) models, can capture linear relationships but cannot capture nonlinear relationships they are insufficient for long-term prediction. The hybrid ARIMA–support vector regression (SVR) model proposed in this paper is based on the advantages of a linear model and a nonlinear model. The multiscale standard

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Mardianto, Is, Muhamad Ichsan Gunawan, Dedy Sugiarto, and Abdul Rochman. "Comparison of Rice Price Forecasting Using the ARIMA Method on Amazon Forecast and Sagemaker." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 3 (2020): 537–43. http://dx.doi.org/10.29207/resti.v4i3.1902.

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Rice is one of the main commodities of trade in Indonesia. PT Food Station as the management company of Cipinang Rice Main Market every day publishes data on price, type of rice and the amount of rice that enters and exits Jakarta area. This study aims to forecast rice prices in the Jakarta area using data held by PT FoodStation during the 2016-2018 data period. Rice price prediction is carried out for the next 30 days using the Auto Regressive Integrated Moving Average (ARIMA) method on the Amazon Forecast and Amazon Sagemaker platforms. The ARIMA model is a form of regression analysis that m

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Yan, Xu, and Lan Shuangting. "ARIMA and Multiple Linear Regression Additive Model for SO2 Monitoring Data’s Calibration." E3S Web of Conferences 214 (2020): 03041. http://dx.doi.org/10.1051/e3sconf/202021403041.

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SO2 is one of the main air pollutants produced by industrial waste gas, civil combustion and automobile exhaust. Real-time monitoring of the concentration of SO2 can grasp the air quality in time and take corresponding measures to the pollution sources. Monitoring data may be affected by the internal factors and the external factors. ARIMA was used for the internal factor as A. Meteorological factors were taken as external factors, and the difference of SO2 between the standard data and monitoring data was taken as dependent variable. Multivariate linear regression was modeled as B. Time serie

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Chib, Siddhartha, and Edward Greenberg. "Bayes inference in regression models with ARMA (p, q) errors." Journal of Econometrics 64, no. 1-2 (1994): 183–206. http://dx.doi.org/10.1016/0304-4076(94)90063-9.

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Priyadi, Devita, and Iffatul Mardhiyah. "MODEL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DALAM PERAMALAN NILAI HARGA SAHAM PENUTUP INDEKS LQ45." Jurnal Ilmiah Informatika Komputer 26, no. 1 (2021): 78–94. http://dx.doi.org/10.35760/ik.2021.v26i1.3695.

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Data indeks LQ45 dapat digunakan membantu manajer investasi, investor ataupun calon investor terkait dalam proses perencanaan dan proses pengambilan keputusan dalam membeli ataupun menjual saham. Oleh karena itu data LQ45 memiliki peran penting dalam melakukan peramalan untuk mencapai tujuan tersebut. Peramalan deret waktu (time series) menggunakan penerapan model Autoregressive Integrated Moving Average (ARIMA) untuk meramalkan nilai harga saham penutup dalam Indeks LQ45 pada data mingguan. Data yang digunakan merupakan data dari 25 November 2019 sampai dengan 30 November 2020. Hasil pengujia

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Al-Douri, Yamur, Hussan Hamodi, and Jan Lundberg. "Time Series Forecasting Using a Two-Level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans." Algorithms 11, no. 8 (2018): 123. http://dx.doi.org/10.3390/a11080123.

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The aim of this study has been to develop a novel two-level multi-objective genetic algorithm (GA) to optimize time series forecasting data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). Level 1 is for the process of forecasting time series cost data, while level 2 evaluates the forecasting. Level 1 implements either a multi-objective GA based on the ARIMA model or a multi-objective GA based on the dynamic regression model. Level 2 utilises a multi-objective GA based on different forecasting error rates to identify a proper forecasting. Our method is comp

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Myakisheva, Natalia V., Ekaterina V. Gaidukova, Sergei V. Shanochkin, and Anna A. Batmazova. "Seasonal and Annual Probabilistic Forecasting of Water Levels in Large Lakes (Case Study of the Ladoga Lake)." International Letters of Natural Sciences 82 (April 2021): 13–19. http://dx.doi.org/10.18052/www.scipress.com/ilns.82.13.

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The production functions of water-dependent sectors of the economy can include the water level in the lake as a natural resource. This characteristic must be able to reliably predict for the effective functioning of sectors of the economy. In the article the main attention is paid to the methods of forecasting based on the extrapolation of natural variations of the large lakes water level. As an example, is considered. In this paper, it is assumed that the level varies accordingly to a stochastic multi-cycle process with principal energy-containing zones in frequency bands associated with seas

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Hejase, Hassan A. N., and Ali H. Assi. "Time-Series Regression Model for Prediction of Mean Daily Global Solar Radiation in Al-Ain, UAE." ISRN Renewable Energy 2012 (April 11, 2012): 1–11. http://dx.doi.org/10.5402/2012/412471.

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The availability of short-term forecast weather model for a particular country or region is essential for operation planning of energy systems. This paper presents the first step by a group of researchers at UAE University to establish a weather model for the UAE using the weather data for at least 10 years and employing various models such as classical empirical models, artificial neural network (ANN) models, and time-series regression models with autoregressive integrated moving-average (ARIMA). This work uses time-series regression with ARIMA modeling to establish a model for the mean daily

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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 graduat

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ALLAL, J., and A. KAAOUACHI. "ADAPTIVE R-ESTIMATION IN A LINEAR REGRESSION MODEL WITH ARMA ERRORS." Statistics: A Journal of Theoretical and Applied Statistics 37, no. 4 (2003): 1. http://dx.doi.org/10.1080/0233188021000055363.

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Allal, J., and A. Kaaouachi. "Adaptive R-estimation in a linear regression model with ARMA errors." Statistics 37, no. 4 (2003): 271–86. http://dx.doi.org/10.1080/715019245.

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Furno, Marilena. "The information matrix test in the linear regression with ARMA errors." Journal of the Italian Statistical Society 5, no. 3 (1996): 369–85. http://dx.doi.org/10.1007/bf02589097.

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Vinod, H. D. "Exact maximum likelihood regression estimation with ARMA (n, n − 1) errors." Economics Letters 17, no. 4 (1985): 355–58. http://dx.doi.org/10.1016/0165-1765(85)90258-7.

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Qin, Peng, and Chunmei Cheng. "Prediction of Seawall Settlement Based on a Combined LS-ARIMA Model." Mathematical Problems in Engineering 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/7840569.

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The analysis and prediction of seawall settlement are important for seawall engineering maintenance and disaster prevention. Based on the measured seawall settlement time series data, a combined LS-ARIMA forecasting model that fits the trend item by the least-square (LS) method and the season item by the differential self-regression moving average (ARIMA) model was proposed in this study. The monitoring data of one seawall project in Zhejiang, China, is taken as an example to verify the model efficiency and prediction ability. The results show that the prediction accuracy of the new combined L

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Chai, Shanglei, Mo Du, Xi Chen, and Wenjun Chu. "A Hybrid Forecasting Model for Nonstationary and Nonlinear Time Series in the Stochastic Process of CO2 Emission Trading Price Fluctuation." Mathematical Problems in Engineering 2020 (August 4, 2020): 1–13. http://dx.doi.org/10.1155/2020/8978504.

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Predicting CO2 emission prices is an important and challenging task for policy makers and market participants, as carbon prices follow a stochastic process of complex time series with nonstationary and nonlinear characteristics. Existing literature has focused on highly precise point forecasting, but it cannot correctly solve the uncertainties related to carbon price datasets in most cases. This study aims to develop a hybrid forecasting model to estimate in advance the maximum or minimum loss in the stochastic process of CO2 emission trading price fluctuation. This model can granulate raw dat

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Hoover, Stephen, Eric Jackson, David Paul, Robert Locke, and Muge Capan. "Time Series Analysis for Forecasting Hospital Census: Application to the Neonatal Intensive Care Unit." Applied Clinical Informatics 07, no. 02 (2016): 275–89. http://dx.doi.org/10.4338/aci-2015-09-ra-0127.

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SummaryAccurate prediction of future patient census in hospital units is essential for patient safety, health outcomes, and resource planning. Forecasting census in the Neonatal Intensive Care Unit (NICU) is particularly challenging due to limited ability to control the census and clinical trajectories. The fixed average census approach, using average census from previous year, is a forecasting alternative used in clinical practice, but has limitations due to census variations.Our objectives are to: (i) analyze the daily NICU census at a single health care facility and develop census forecasti

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Nadirah Mohd Johari, Sarah, Fairuz Husna Muhamad Farid, Nur Afifah Enara Binti Nasrudin, Nur Sarah Liyana Bistamam, and Nur Syamira Syamimi Muhammad Shuhaili. "Predicting Stock Market Index Using Hybrid Intelligence Model." International Journal of Engineering & Technology 7, no. 3.15 (2018): 36. http://dx.doi.org/10.14419/ijet.v7i3.15.17403.

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Predicting financial market changes is an important issue in time series analysis, receiving an increasing attention due to financial crisis. Autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting but ARIMA model cannot capture nonlinear patterns easily. Generalized autoregressive conditional heteroscedasticity (GARCH) model applied understanding of volatility depending to the estimation of previous forecast error and current volatility, improving ARIMA model. Support vector machine (SVM) and artificial neural networ

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Dhamija, Ajay Kumar, Surendra S. Yadav, and P. K. Jain. "Carbon credit returns under EU ETS and its determinants: a multi-phase study." Journal of Advances in Management Research 14, no. 4 (2017): 481–526. http://dx.doi.org/10.1108/jamr-11-2016-0099.

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Purpose The purpose of this paper is to find out the best method for forecasting European Union Allowance (EUA) returns and determine its price determinants. The previous studies in this area have focused on a particular subset of EUA data and do not take care of the multicollinearities. The authors take EUA data from all three phases and the continuous series, adopt the principal component analysis (PCA) to eliminate multicollinearities and fit seven different homoscedastic models for a comprehensive analysis. Design/methodology/approach PCA is adopted to extract independent factors. Seven di

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Shih, Han, and Suchithra Rajendran. "Comparison of Time Series Methods and Machine Learning Algorithms for Forecasting Taiwan Blood Services Foundation’s Blood Supply." Journal of Healthcare Engineering 2019 (September 17, 2019): 1–6. http://dx.doi.org/10.1155/2019/6123745.

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Purpose. The uncertainty in supply and the short shelf life of blood products have led to a substantial outdating of the collected donor blood. On the other hand, hospitals and blood centers experience severe blood shortage due to the very limited donor population. Therefore, the necessity to forecast the blood supply to minimize outdating as well as shortage is obvious. This study aims to efficiently forecast the supply of blood components at blood centers. Methods. Two different types of forecasting techniques, time series and machine learning algorithms, are developed and the best performin

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McGilchrist, C. A. "Bias of ml and reml estimators in regression models with arma errors." Journal of Statistical Computation and Simulation 32, no. 3 (1989): 127–36. http://dx.doi.org/10.1080/00949658908811169.

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Zinde-Walsh, Victoria, and John W. Galbraith. "Estimation of a linear regression model with stationary ARMA(p, q) errors." Journal of Econometrics 47, no. 2-3 (1991): 333–57. http://dx.doi.org/10.1016/0304-4076(91)90106-n.

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Jesica, Haniela Puja, Dwi Ispriyanti, and Tarno Tarno. "PERAMALAN JUMLAH WISATAWAN YANG BERKUNJUNG KE OBJEK WISATA DI JAWA TENGAH MENGGUNAKAN VARIASI KALENDER ISLAM REGARIMA." Jurnal Gaussian 8, no. 3 (2019): 305–16. http://dx.doi.org/10.14710/j.gauss.v8i3.26676.

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Tourism is one of the most strategically controlled areas that have been developed.The number of tourists in Central Java is constantly rising in the month of Eid Al-Fitr caused by holiday and mudik to hometown. The shift of the Eid Al-Fitr month on the data will form a seasonal pattern with an unequal period, then called moving holiday effect.One of the calendar variationsare often used to remove the moving holiday effect is RegARIMA model. RegARIMA is a combination of the linier regression and ARIMA, which a weight was used as a regression variable and error of regression model was used a va

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Surya, Henry Viriya, and Prastowo Cahjadi. "Komparasi Regresi Ekonometri pada Perekonomian Indonesia 2SLS, VEC, dan ARIMA." Jurnal Ekonomi dan Pembangunan Indonesia 2, no. 2 (2002): 88–112. http://dx.doi.org/10.21002/jepi.v2i2.627.

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This paper compares three models of econometric analysis on economy, in this case the Indonesian economy. The regression models are the two stage least squares (2SLS) which has a strong support from the economic theory of aggregate expenditure, the Vector Error Correction (VEC) and Autoregressive Integrated Moving Average (ARIMA) which both comes from the time series analysis, that do not have to be economic time series. The study tries to find out which are most suitable in analyzing the time series of Indonesian economy. After all the estimation and comparison process, we finally agree that

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Fang, Wennuan. "Research on Expected Return Based on Time Series Model: Taking Kweichow Moutai Co., Ltd. as an Example." Financial Forum 9, no. 2 (2020): 52. http://dx.doi.org/10.18282/ff.v9i2.858.

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<p>With the stable development of China's economy, the economic activities among enterprises are more diversified, and the enterprise value evaluation index system is more perfect. As an important parameter in the enterprise value evaluation index, the expected income can be used to measure the profit quality of the enterprise. In order to explore the expected return of enterprises, this paper selects free cash flow as the specific index, and takes Kweichow moutai Co., Ltd. as an example, analyzes the earnings trend of enterprises through the method of time series. Time series prediction

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Butt, Maliha. "Selection of forecast model for consumption (four sectors) and transmission (two Piplines) of natural gas in Punjab (Pakistan) based on ARIMA model." International Journal of Advanced Statistics and Probability 3, no. 1 (2015): 115. http://dx.doi.org/10.14419/ijasp.v3i1.4635.

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<p>The main purpose of this study is to select an appropriate forecast model for Natural Gas Consumption and Transmission System. For ARIMA model, Box-Jenkins Approach (1976) has been adopted i.e. Stationarity of the series has been checked for each data set, correlogram has been estimated for identification of order of ARIMA model and a class of models has been estimated. Then, most adequate and appropriate model is selected by analyzing diagnostics checks. Later on, by comparing values of Akaike Information Criterion (AIC), Schwarz Information Criterion (SIC), and Standard Error (S.E.)

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Yan, Xu, and Lan Shuangting. "Time Series and Multiple Linear Regression Calibration Model for CO Monitoring Data." E3S Web of Conferences 214 (2020): 03039. http://dx.doi.org/10.1051/e3sconf/202021403039.

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CO is a kind of air pollutant with the largest amount and the widest distribution in the atmosphere produced by combustion of carbon containing substances. Real-time monitoring of the concentration of CO can grasp the air quality in time and take corresponding measures to the pollution sources. Monitoring data may be affected by the internal factors and the external factors. ARIMA was used for the internal factor as A. Meteorological factors were taken as external factors, and the difference of CO between the standard data and monitoring data was taken as dependent variable. Multivariate linea

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Prendin, Francesco, Simone Del Favero, Martina Vettoretti, Giovanni Sparacino, and Andrea Facchinetti. "Forecasting of Glucose Levels and Hypoglycemic Events: Head-to-Head Comparison of Linear and Nonlinear Data-Driven Algorithms Based on Continuous Glucose Monitoring Data Only." Sensors 21, no. 5 (2021): 1647. http://dx.doi.org/10.3390/s21051647.

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In type 1 diabetes management, the availability of algorithms capable of accurately forecasting future blood glucose (BG) concentrations and hypoglycemic episodes could enable proactive therapeutic actions, e.g., the consumption of carbohydrates to mitigate, or even avoid, an impending critical event. The only input of this kind of algorithm is often continuous glucose monitoring (CGM) sensor data, because other signals (such as injected insulin, ingested carbs, and physical activity) are frequently unavailable. Several predictive algorithms fed by CGM data only have been proposed in the liter

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Lee, Hoon Ja, and Tae Jin Ahn. "Goodness-of-Fit of Time Series Model for the Storage of Water in Agricultural Reservoir." Key Engineering Materials 277-279 (January 2005): 200–205. http://dx.doi.org/10.4028/www.scientific.net/kem.277-279.200.

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The efficient management of the agricultural reservoir may well supply stable water for irrigation. In this article, time series analysis has been used for analyzing the storage of water data in Kihung agricultural reservoir that is located in Yongin City, Korea. For analyzing the storage of water data, three models, the ARIMA model, the autoregressive error model, and the dynamic regression model have been used. The result shows that the autoregressive error model is best suited for describing the storage of water data.

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Yang, Stephanie, Hsueh-Chih Chen, Chih-Hsien Wu, Meng-Ni Wu, and Cheng-Hong Yang. "Forecasting of the Prevalence of Dementia Using the LSTM Neural Network in Taiwan." Mathematics 9, no. 5 (2021): 488. http://dx.doi.org/10.3390/math9050488.

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The World Health Organization has urged countries to prioritize dementia in their public health policies. Dementia poses a tremendous socioeconomic burden, and the accurate prediction of the annual increase in prevalence is essential for establishing strategies to cope with its effects. The present study established a model based on the architecture of the long short-term memory (LSTM) neural network for predicting the number of dementia cases in Taiwan, which considers the effects of age and sex on the prevalence of dementia. The LSTM network is a variant of recurrent neural networks (RNNs),

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Ramli, Nor Azuana, and Mohd Fairuz Abdul Hamid. "Analysis of energy efficiency and energy consumption costs: a case study for regional wastewater treatment plant in Malaysia." Journal of Water Reuse and Desalination 7, no. 1 (2016): 103–10. http://dx.doi.org/10.2166/wrd.2016.196.

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The objective of this study is to analyze the possibilities of increasing energy efficiency in the central region wastewater treatment plant by focusing on two aspects: biogas production and prediction of energy production. The analysis is based on one of the biggest central region wastewater treatment plants in Malaysia. After studying the energy efficiency, which consists of optimization of energy consumption and enhancing gas generation, the prediction of power consumption is performed using an autoregressive integrated moving average (ARIMA) model. The prediction results are compared with

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Cyprich, Ondrej, Vladimír Konečný, and Katarína Kiliánová. "Short-Term Passenger Demand Forecasting Using Univariate Time Series Theory." PROMET - Traffic&Transportation 25, no. 6 (2013): 533–41. http://dx.doi.org/10.7307/ptt.v25i6.338.

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The purpose of the paper is to identify and analyse the forecasting performance of the model of passenger demand for suburban bus transport time series, which satisfies the statistical significance of its parameters and randomness of its residuals. Box-Jenkins, exponential smoothing and multiple linear regression models are used in order to design a more accurate and reliable model compared the ones used nowadays. Forecasting accuracy of the models is evaluated by comparative analysis of the calculated mean absolute percent errors of different approaches to forecasting. In accordance with the

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