Relevant bibliographies by topics / Regression with ARIMA Errors

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Regression with ARIMA Errors'

Author: Grafiati
Published: 9 September 2021
Last updated: 18 February 2022

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Journal articles on the topic "Regression with ARIMA Errors"

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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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Dissertations / Theses on the topic "Regression with ARIMA Errors"

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Heed, Ingrid, and Karl Lindberg. "Forecasting COVID-19 hospitalizations using dynamic regression with ARIMA errors." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446310.

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For more than a year, COVID-19 has changed societies all over the world and put massive strains on its healthcare systems. In an attempt to aid in prioritizing medical resources, this thesis uses dynamic regression with ARIMA errors to forecast the number of hospitalizations related to COVID-19 two weeks ahead in Uppsala County. For this purpose, 100 models are created and their ability to forecast hospitalizations two weeks ahead for weeks 15-17 of 2021 for the different municipalities in Uppsala County is evaluated using root mean squared error (RMSE), mean absolute error (MAE), and mean abs
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Stocker, Toni Clemens. "On the asymptotic properties of the OLS estimator in regression models with fractionally integrated regressors and errors." [S.l. : s.n.], 2008. http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-57370.

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Oleksandra, Shovkun. "Some methods for reducing the total consumption and production prediction errors of electricity: Adaptive Linear Regression of Original Predictions and Modeling of Prediction Errors." Thesis, Linnéuniversitetet, Institutionen för matematik (MA), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-34398.

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Balance between energy consumption and production of electricityis a very important for the electric power system operation and planning. Itprovides a good principle of effective operation, reduces the generation costin a power system and saves money. Two novel approaches to reduce thetotal errors between forecast and real electricity consumption wereproposed. An Adaptive Linear Regression of Original Predictions (ALROP)was constructed to modify the existing predictions by using simple linearregression with estimation by the Ordinary Least Square (OLS) method.The Weighted Least Square (WLS) me
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Nováčková, Monika. "Aplikace analýzy časových řad v prognózování." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-199538.

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This thesis attempts to predict daily number of firefighter incidents in the Central Bohemia Region and in the Region of Hradec Králové to improve firefighter shift planning. The analysis is based on a dataset of firefighter incidents from the period between the years 2008 and 2012. Econometric models, capturing yearly and weekly patterns and weather impact were estimated and used for long-term prediction. The first part of the thesis provides a description of tests applied to residuals and other econometric tests used in this study. Then linear regression is applied to model weather impact an
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Liendeborg, Zaida, and Mattias Karlsson. "Prognostisering av försäljningsvolym med hjälp av omvärldsindikatorer." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-129572.

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Background Forecasts are used as a basis for decision making and they mainly affect decisions at strategic and tactical levels in a company or organization. There are two different methods to perform forecasts. The first one is a qualitative method where a n expert or group of experts tell about the future. The second one is a quantitative method where forecast are produced by mathematical and statistical models. This study used a quantitative method to build a forecast model and took into account external f actors in forecasting the sales volume of Bosch Rexroth’s hydraulic motors. There is a
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Nayeri, Negin. "Option strategies using hybrid Support Vector Regression - ARIMA." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275719.

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In this thesis, the use of machine learning in option strategies is evaluated with focus on the S&P 500 Index. The first part of the thesis focuses on testing the performance power of the Support Vector Regression (SVR) method for the historical realized volatility with a window of 20 days. The prediction window will also be 1-month forward (approximately 20 trading days). The second part of the thesis focuses on creating an ARIMA model that forecasts the error that is based on the difference between the predicted respective true values. This is done in order to create the hybrid SVR-ARIMA
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Nilsson, Fredrik. "Payment Volume Forecasting using Hierarchical Regression with SARIMA Errors : Payment Volume Forecasting using Hierarchical Regression with SARIMA Errors." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-425886.

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When forecasting financial transaction volumes in different markets, different markets often exhibit similar seasonality patterns and public holiday behavior. In this thesis, an attempt is made at utilizing these similarities to improve forecasting accuracy as compared to forecasting each market individually. Bayesian hierarchical regression models with time series errors are used on daily transaction data. When fitting three years of historic data for all markets, no consistent significant improvements in forecasting accuracy was found over a non-hierarchical regression model. When the amount
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Wågberg, Max. "Att förutspå Sveriges bistånd : En jämförelse mellan Support Vector Regression och ARIMA." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36479.

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In recent years, the use of machine learning has increased significantly. Its uses range from making the everyday life easier with voice-guided smart devices to image recognition, or predicting the stock market. Predicting economic values has long been possible by using methods other than machine learning, such as statistical algorithms. These algorithms and machine learning models use time series, which is a set of data points observed constantly over a given time interval, in order to predict data points beyond the original time series. But which of these methods gives the best results? The
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Gillard, Jonathan William. "Errors in variables regression : what is the appropriate model?" Thesis, Cardiff University, 2007. http://orca.cf.ac.uk/54629/.

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The fitting of a straight line to bivariate data (x,y) is a common procedure. Standard linear regression theory deals with the situation when there is only error in one variable, either x, or y. A procedure known as y on x regression fits a line where the error is assumed to be associated with the y variable, alternatively, x on y regression fits a line when the error is associated with the x variable. The model to describe the scenario when there are errors in both variables is known as an errors in variables model. Errors in variables modelling is fundamentally different from standard regres
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Gunby, James Alexander. "Measurement errors in case-control and related studies." Thesis, University of Oxford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239324.

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Books on the topic "Regression with ARIMA Errors"

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Bagchi, Parthasarathy. Bayesian regression analysis under non-normal errors. University of Toronto, Dept. of Statistics, 1987.

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Robinson, P. M. Large-sample inference for nonparametric regression with dependent errors. Suntory and Toyota International Centres for Economics and Related Disciplines, 1997.

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Stock, James H. Heteroskedasticity-robust standard errors for fixed effects panel data regression. National Bureau of Economic Research, 2006.

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Rahiala, Markku. On the identification and estimation of multiple input transfer function models with autocorrelated errors. Research Institute of the Finnish Economy, 1985.

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Pevehouse, Jon, and Jason D. Brozek. Time‐Series Analysis. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0019.

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This article discusses time-series methods such as simple time-series regressions, ARIMA models, vector autoregression (VAR) models, and unit root and error correction models (ECM). It specifically presents a brief history of time-series analysis before moving to a review of the basic time-series model. It then describes the stationary models in univariate and multivariate analyses. The nonstationary models of each type are addressed. In addition, various issues regarding the analysis of time series including data aggregation and temporal stability are considered. Before concluding, the articl
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Delongchamp, Robert. Analysis of epidemiological data with covariate errors. 1993.

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A. K. Md. Ehsanes Saleh, Mohammad Arashi, and S. M. M. Tabatabaey. Statistical Inference for Models with Multivariate t-Distributed Errors. Wiley, 2014.

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Arashi, Mohammad, A. K. Ehsanes Saleh, and S. M. M. Tabatabaey. Statistical Inference for Models with Multivariate T-Distributed Errors. Wiley & Sons, Incorporated, John, 2014.

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Arashi, Mohammad, A. K. Ehsanes Saleh, and S. M. M. Tabatabaey. Statistical Inference for Models with Multivariate T-Distributed Errors. Wiley & Sons, Incorporated, John, 2014.

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Random regression models with autocorrelated errors: Investigating drug plasma levels and clinical response. 1989.

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Book chapters on the topic "Regression with ARIMA Errors"

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Bianco, A. M., E. J. Martinez, M. Garcia Ben, and V. J. Yohai. "Robust Procedures for Regression Models with ARIMA Errors." In COMPSTAT. Physica-Verlag HD, 1996. http://dx.doi.org/10.1007/978-3-642-46992-3_3.

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Hoffmann, John P. "Measurement Errors." In Linear Regression Models. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003162230-13.

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Shumway, Robert H., and David S. Stoffer. "Time Series Regression and ARIMA Models." In Springer Texts in Statistics. Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4757-3261-0_2.

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Salsburg, David S. "Regression and Big Data." In Errors, Blunders, and Lies. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315379081-8.

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Cysneiros, Francisco José A. "Regression Models with Symmetrical Errors." In International Encyclopedia of Statistical Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_486.

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Liu, Timina, Shuangzhe Liu, and Lei Shi. "Regression Analysis with Autoregressive Errors." In Time Series Analysis Using SAS Enterprise Guide. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0321-4_5.

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Salsburg, David S. "When Multilinear Regression Is Not Adequate." In Errors, Blunders, and Lies. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315379081-6.

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Reinsel, Gregory C., and Raja P. Velu. "Reduced-Rank Regression Model With Autoregressive Errors." In Multivariate Reduced-Rank Regression. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2853-8_4.

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Gujarati, Damodar. "Regression Diagnostic IV: Model Specification Errors." In Econometrics. Macmillan Education UK, 2015. http://dx.doi.org/10.1007/978-1-137-37502-5_7.

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Meister, Alexander. "Nonparametric Regression with Errors-in-Variables." In Deconvolution Problems in Nonparametric Statistics. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87557-4_3.

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Conference papers on the topic "Regression with ARIMA Errors"

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Kemper, Jason J., Mark F. Bielecki, and Thomas L. Acker. "Modeling of Wind Power Production Forecast Errors for Wind Integration Studies." In ASME 2010 4th International Conference on Energy Sustainability. ASMEDC, 2010. http://dx.doi.org/10.1115/es2010-90441.

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In wind integration studies, accurate representations of the wind power output from potential wind power plants and corresponding representations of wind power forecasts are needed, and typically used in a production cost simulation. Two methods for generating “synthetic” wind power forecasts that capture the statistical trends and characteristics found in commercial forecasting techniques are presented. These two methods are based on auto-regressive moving average (ARMA) models and the Markov random walk method. Statistical criteria are suggested for evaluation of wind power forecast performa
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Maraval, Augustín. "Automatic Identification of Regression-ARIMA Models with Program TSW." In 23rd European Conference on Modelling and Simulation. ECMS, 2009. http://dx.doi.org/10.7148/2009-0005-0008.

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Böhme, Marcel, and Abhik Roychoudhury. "CoREBench: studying complexity of regression errors." In the 2014 International Symposium. ACM Press, 2014. http://dx.doi.org/10.1145/2610384.2628058.

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Böhme, Marcel, Bruno C. d. S. Oliveira, and Abhik Roychoudhury. "Regression tests to expose change interaction errors." In the 2013 9th Joint Meeting. ACM Press, 2013. http://dx.doi.org/10.1145/2491411.2491430.

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Chen, Xing-Min, and Chao Gao. "Recursive nonparametric regression with errors in variables." In 2015 34th Chinese Control Conference (CCC). IEEE, 2015. http://dx.doi.org/10.1109/chicc.2015.7259956.

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Darapaneni, Narayana, Deepali Nikam, Anagha Lomate, et al. "Coronavirus Outburst Prediction in India using SEIRD, Logistic Regression and ARIMA Model." In 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). IEEE, 2020. http://dx.doi.org/10.1109/uemcon51285.2020.9298097.

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Yan, Xu, and Xu Enhua. "ARIMA and Multiple Regression Additive Models for PM2.5 Based on Linear Interpolation." In 2020 International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2020. http://dx.doi.org/10.1109/icbase51474.2020.00062.

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Alhamide, A. A., K. Ibrahim, and M. T. Alodat. "Multiple linear regression estimators with skew normal errors." In THE 2015 UKM FST POSTGRADUATE COLLOQUIUM: Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2015 Postgraduate Colloquium. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4931340.

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Mehr, M. Nikouei, F. Famil Samavati, and M. Jeihoonian. "Annual energy demand estimation of Iran industrial sector by Fuzzy regression and ARIMA." In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011). IEEE, 2011. http://dx.doi.org/10.1109/fskd.2011.6019565.

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Rozliman, Nur Aainaa, Adriana Irawati Nur Ibrahim, and Rossita Mohammad Yunus. "Bayesian approach to errors-in-variables in regression models." In THE 3RD ISM INTERNATIONAL STATISTICAL CONFERENCE 2016 (ISM-III): Bringing Professionalism and Prestige in Statistics. Author(s), 2017. http://dx.doi.org/10.1063/1.4982856.

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Reports on the topic "Regression with ARIMA Errors"

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Hausman, Jerry, Haoyang Liu, Ye Luo, and Christopher Palmer. Errors in the Dependent Variable of Quantile Regression Models. National Bureau of Economic Research, 2019. http://dx.doi.org/10.3386/w25819.

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Stock, James, and Mark Watson. Heteroskedasticity-Robust Standard Errors for Fixed Effects Panel Data Regression. National Bureau of Economic Research, 2006. http://dx.doi.org/10.3386/t0323.

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Wu, Yuehua. On Strongly Consistent Estimates of Regression Coefficients when the Errors are not Independently and Identically Distributed. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada170076.

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Carroll, Raymond J., P. Gallo, and L. J. Gleser. Comparisons of Least Squares and Errors-in-Variables Regression, with Special Reference to Randomized Analysis of Covariance. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada160967.

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Bartel, Thomas W. Implementation of Errors-in-Variables Regression and Monte Carlo Uncertainty Evaluation into Force Calibration Reporting at NIST. National Institute of Standards and Technology, 2016. http://dx.doi.org/10.6028/nist.tn.1942.

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Frydman, Roman, and Joshua Stillwagon. Market Participants Neither Commit Predictable Errors nor Conform to REH: Evidence from Survey Data of Inflation Forecasts. Institute for New Economic Thinking Working Paper Series, 2021. http://dx.doi.org/10.36687/inetwp163.

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We develop a novel characterization of participants’ forecasts with a mixture of normal variables arising from a Markov component. Using this characterization, we formulate five behavioral specifications, including four implied by the diagnostic expectations approach, as well as three implied by REH, and derive several new predictions for Coibion and Gorodnichenko.s regression of forecast errors on forecast revisions. Predictions of all eight specifications are inconsistent with the observed instability of individual CG regressions’ coefficients, based on inflation forecasts from 24 profession
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Arhin, Stephen, Babin Manandhar, Hamdiat Baba Adam, and Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.1943.

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Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automati
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