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Journal articles on the topic 'Regression with ARIMA Errors'

Author: Grafiati

Published: 9 September 2021

Last updated: 29 July 2025

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1

Feng, Jiali, and Baoli Huang. "Forecasting Carbon Emission Using ETS Exponential Smoothing, ARIMA and Regression with ARIMA errors Techniques." International Journal of Engineering and Technology 16, no. 3 (2024): 125–29. http://dx.doi.org/10.7763/ijet.2024.v16.1267.

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Forecasting Carbon Emissions Using Time Series Analysis Global warming is one of the most difficult and complex problems facing the world today, and forecasting carbon emissions has become a worldwide challenge. In this study, we try to use three models Exponential Smoothing (ETS) model, seasonal ARIMA and error regression Autoregressive Integrated Moving Average (ARIMA) model to train the data of carbon dioxide emissions in a region of the United States from 1990 to 2015, to simulate and forecast the carbon emissions in the United States, and to find out the optimal forecasting model.

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Ugoh, Christogonus Ifeanyichukwu, Udochukwu Victor Echebiri, Gabriel Olawale Temisan, Johnpaul Kenechukwu Iwuchukwu, and ,. Emwinloghosa Kenneth Guobadia. "On Forecasting Nigeria’s GDP: A Comparative Performance of Regression with ARIMA Errors and ARIMA Method." International Journal of Mathematics and Statistics Studies 10, no. 4 (2022): 48–64. http://dx.doi.org/10.37745/ijmss.13/vol10n44864.

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This paper examines the application of autoregressive integrated moving average (ARIMA) model and regression model with ARIMA errors for forecasting Nigeria’s GDP. The data used in this study are collected from the official website of World Bank for the period 1990-2019. A response variable (GDP) and four predictor variables are used for the study. The ARIMA model is fitted only to the response variable, while regression with ARIMA errors is fitted on the data as a whole. The Akaike Information Criterion Corrected (AICc) was used to select the best model among the selected ARIMA models, while

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Kok, Mun Ng, Sandin Nahar Ravenny, and IbneReaz Mamun. "Linear regression models with autoregressive integrated moving average errors for measurements from real time kinematics-global navigation satellite system during dynamic test." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (2023): 770–80. https://doi.org/10.11591/ijece.v13i1.pp770-780.

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The autoregressive integrated moving average (ARIMA) method has been used to model global navigation satellite systems (GNSS) measurement errors. Most ARIMA error models describe time series data of static GNSS receivers. Its application for modeling of GNSS under dynamic tests is not evident. In this paper, we aim to describe real time kinematic-GNSS (RTKGNSS) errors during dynamic tests using linear regression with ARIMA errors to establish a proof of concept via simulation that measurement errors along a trajectory logged by the RTK-GNSS can be “filtered”, which will result in i

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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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Ng, Kok Mun, Ravenny Sandin Nahar, and Mamun IbneReaz. "Linear regression models with autoregressive integrated moving average errors for measurements from real time kinematics-global navigation satellite system during dynamic test." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (2023): 770. http://dx.doi.org/10.11591/ijece.v13i1.pp770-780.

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<span lang="EN-US">The autoregressive integrated moving average (ARIMA) method has been used to model global navigation satellite systems (GNSS) measurement errors. Most ARIMA error models describe time series data of static GNSS receivers. Its application for modeling of GNSS under dynamic tests is not evident. In this paper, we aim to describe real time kinematic-GNSS (RTK-GNSS) errors during dynamic tests using linear regression with ARIMA errors to establish a proof of concept via simulation that measurement errors along a trajectory logged by the RTK-GNSS can be “filtered”, which wi

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Nahar, Ravenny Sandin, Kok Mun Ng, Fadhlan Hafizhelmi Kamaruzaman, Noorfadzli Abdul Razak, and Juliana Johari. "Modelling and estimating trajectory points from RTK-GNSS based on an integrated modelling approach." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 1 (2024): 162. http://dx.doi.org/10.11591/ijeecs.v34.i1.pp162-172.

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The sparse Gaussian process regression (GPR) has been used to model trajectory data from Real time kinematics-global navigation satellite system (RTK-GNSS). However, upon scrutinizing the model residuals; the sparse GPR model poorly fits the data and exhibits presence of correlated noise. This work attempts to address these issues by proposing an integrated modeling approach called GPR-LR-ARIMA where the sparse GPR was integrated with the linear regression with autoregressive integrated moving average errors (LR-ARIMA) to further enhance the description of the trajectory data. In this integrat

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Nahar, Ravenny Sandin, Kok Mun Ng, Fadhlan Hafizhelmi Kamaruzaman, Noorfadzli Abdul Razak, and Juliana Johari. "Modelling and estimating trajectory points from RTK-GNSS based on an integrated modelling approach." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 1 (2024): 162–72. https://doi.org/10.11591/ijeecs.v34.i1.pp162-172.

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The sparse Gaussian process regression (GPR) has been used to model trajectory data from Real time kinematics-global navigation satellite system (RTK-GNSS). However, upon scrutinizing the model residuals; the sparse GPR model poorly fits the data and exhibits presence of correlated noise. This work attempts to address these issues by proposing an integrated modeling approach called GPR-LR-ARIMA where the sparse GPR was integrated with the linear regression with autoregressive integrated moving average errors (LR-ARIMA) to further enhance the description of the trajectory data. In this integrat

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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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9

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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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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Cai, Zeyi. "Comparison of ARIMA and ARIMA Error Regression Models: Evidence from the Russian Consumer Price Index." Advances in Economics, Management and Political Sciences 51, no. 1 (2023): 278–88. http://dx.doi.org/10.54254/2754-1169/51/20230675.

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Consumer Price Index (CPI) is regarded as a common approach for measuring inflation. The present study examines the Russia CPI in the context of political and social upheavals, especially under the wars and the COVID-19. Due to the unstable political situation, the inflation rate in Russia sharply grow in 2014 and 2022 which creates two of the largest increase over the past decade after the Crimean war and the Ukrainian war were announced, thus it is crutial to make both long-term and short-term trends prediction. The paper aims to choose the best fitting model to estimate the future value of

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Tarigan, Enita Dewi, Maulida Yanti, Citra Dewi Hasibuan, Yan Batara Putra Siringoringo, and Erwin Erwin. "INVESTMENT GOLD DURING THE COVID-19 PANDEMIC WITH LINEAR REGRESSION, NONLINEAR REGRESSION AND ARIMA." Journal of Mathematics, Computations and Statistics 8, no. 1 (2025): 80–92. https://doi.org/10.35580/jmathcos.v8i1.6832.

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As a result of the COVID-19 pandemic, many individuals have turned to low-risk investment options. Common choices include gold, stocks, deposits, and foreign currencies, with gold emerging as a particularly popular investment. This study aims to forecast gold prices using linear regression, nonlinear regression, and ARIMA models, with the most accurate model determined by the lowest Mean Absolute Percentage Error (MAPE). Gold price data was sourced from www.kitco.com. The MAPE for the Linear Regression model was 4.362, the Nonlinear Regression model 3.3428, and the Time Series (ARIMA) model 2.

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Huang, Xuemin, Xiaoliang Zhuang, Fangyuan Tian, et al. "A Hybrid ARIMA-LSTM-XGBoost Model with Linear Regression Stacking for Transformer Oil Temperature Prediction." Energies 18, no. 6 (2025): 1432. https://doi.org/10.3390/en18061432.

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Transformers are essential for voltage regulation and power distribution in electrical systems, and monitoring their top-oil temperature is crucial for detecting potential faults. High oil temperatures are directly linked to insulation degradation, a primary cause of transformer failures. Therefore, accurate oil temperature prediction is important for proactive maintenance and preventing failures. This paper proposes a hybrid time series forecasting model combining ARIMA, LSTM, and XGBoost to predict transformer oil temperature. ARIMA captures linear components of the data, while LSTM models c

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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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Editya, Dea Avega. "DETERMINING FACTORS FOR 10-YEAR LOCAL CURRENCY SOVEREIGN BONDS YIELD WITH DYNAMIC REGRESSION MODEL." Jurnal BPPK : Badan Pendidikan dan Pelatihan Keuangan 15, no. 1 (2022): 35–48. http://dx.doi.org/10.48108/jurnalbppk.v15i1.715.

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Sovereign bonds, particularly Local Currency Bonds (LCB) of 10-year tenure, had a strategic role in economy, thus understanding factors affecting its yield’s movement would help government to maintain economic stability. This paper empirically study Indonesia’s 10-year LCB and its relationship with several factors; US Treasury (UST) yield, credit default swap (CDS), foreign ownership, central bank's policy rate (policy rate), exchange rate, volatility index (VIX) and primary dealers' trading behavior. The relationship was modelled using Dynamic Regression Model (DRM), with ARIMA errors t

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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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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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Br. Sinulingga, Wita Oktaviana, Ronsen Purba, and Muhammad Fermi Pasha. "Combination of Regression and ARIMA Methods ( Reg – ARIMA ) Stock Price Prediction Model." Journal of Computer Networks, Architecture and High Performance Computing 7, no. 1 (2025): 329–40. https://doi.org/10.47709/cnahpc.v7i1.5474.

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This research is motivated by the limitations of the ARIMA method, which is only suitable for short-term forecasting and specific periods. Therefore, a combination of Regression and ARIMA methods (Reg- ARIMA) is introduced to predict stock prices over a longer period. The purpose of this study is to implement a combination of Regression and ARIMA methods to build a stock price prediction model. The research methodology involves using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) to measure the accuracy of the generated prediction model. The study results indicate sign

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Zulu, Julius, and Gardner Mwansa. "Modelling and Forecasting Foreign Debt Using ARIMA Model: The Zambian Case from 2022 to 2035." International Journal of Research and Innovation in Social Science 06, no. 11 (2022): 590–97. http://dx.doi.org/10.47772/ijriss.2022.61127.

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The study sought to model and forecast Zambian Government foreign debt from 2022 to 2035 using Autoregressive Integrated Moving Average Model. The secondary data of time series during the period of 1973 to 2021 on Zambia’s foreign debt are used as the basis of forecasting for the next 15 years by using ARIMA (Autogressive Integrated Moving Average) Model. The ARIMA (1, 1, 2) model was used due to its accuracy, mathematical soundness, and flexibility, thanks to the inclusion of AR and MA terms over a regression analysis. The results showed that ARIMA (1, 1, 2) is an adequate model which best fi

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Tao, Tianyou, Peng Shi, Hao Wang, Lin Yuan, and Sheng Wang. "Performance Evaluation of Linear and Nonlinear Models for Short-Term Forecasting of Tropical-Storm Winds." Applied Sciences 11, no. 20 (2021): 9441. http://dx.doi.org/10.3390/app11209441.

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Wind-sensitive structures usually suffer from violent vibrations or severe damages under the action of tropical storms. It is of great significance to forecast tropical-storm winds in advance for the sake of reducing or avoiding consequent losses. The model used for forecasting becomes a primary concern in engineering applications. This paper presents a performance evaluation of linear and nonlinear models for the short-term forecasting of tropical storms. Five extensively employed models are adopted to forecast wind speeds using measured samples from the tropical storm Rumbia, which facilitat

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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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Gao, Xiaohan, Hongyi Zhang, and Sirui Zhang. "Impact of the COVID-19 Pandemic on Retail Sales of Health and Personal Care Stores in the United States." BCP Business & Management 44 (April 27, 2023): 1–12. http://dx.doi.org/10.54691/bcpbm.v44i.4786.

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In recent years, as a result of the Covid-19 pandemic, the health and personal care stores have been remarkably affected in a wide range of aspects. This became one of the reasons why medical staff felt pressure, leading to quite a number of staffs resigning, and meanwhile the management pattern of medical employees changes and seems to be more strict. Furthermore, the prevalence of Covid-19 pandemic also causes some mental diseases such as sleeping disturbance, anxiety as well as skin problems whether for adults or younger people. As a consequence, the health and personal care industry has be

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Andi Alifa, Andi Amran Asriadi, Firmansyah, and Nailah Husain. "Trend of Big Chili Prices in Makassar City Using the Arimax Method." JURNAL AGRI-TEK : Jurnal Penelitian Ilmu-Ilmu Eksakta 25, no. 2 (2024): 19–29. https://doi.org/10.33319/agtek.v25i2.132.

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The aims of the study were (1) to analyze the price forecasting of large chilies based on Arima, (2) to analyze the parameter estimates in the best arima model which can be used to select the best Arima in Makassar City. The type of data used is quantitative data or secondary data. Secondary data in this study were obtained from the official website of the National Strategic Food Price Information Center. The data analysis used is an analysis of the ARIMA forecasting model using EViews12 Statistics. The results of the research are large chili price forecasting based on Arima, the results of th

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Saputri, Tri Aristi, Allien Moetiara Rachma Ajiz, and Dani Febritama. "Comparative Study of the ARIMA Method and Multiple Linear Regression in Metro City Population Growth Projections." Journal of Applied Informatics and Computing 9, no. 2 (2025): 542–46. https://doi.org/10.30871/jaic.v9i2.9097.

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This study aims to compare the effectiveness of the ARIMA (Autoregressive Integrated Moving Average) method and multiple linear regression in projecting population growth in Metro City, Lampung. The analysis utilizes population data from 2010 to 2022, sourced from the Central Statistics Agency and the Population and Civil Registration Office. The methodologies employed include ARIMA modelling and multiple linear regression, with model evaluation conducted using metrics such as Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The findings indicate that the multiple linea

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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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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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Alam, Md Juber, and Arijit Majumder. "A Comparative Analysis of ARIMA and other Statistical Techniques in Rainfall Forecasting: A Case Study in Kolkata (KMC), West Bengal." Current World Environment 18, no. 3 (2024): 1384–98. http://dx.doi.org/10.12944/cwe.18.3.37.

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Rainfall forecasting in urban areas is a significant consideration for city planners due to its connection with urban water management. In this study, the ARIMA (auto-regressive integrated moving average) model, as well as several regression approaches such as simple linear and second to sixth-degree polynomial regression equations, have been used to forecast the annual rainfall based on 120 years of monthly and annual rainfall from 1901 to 2020 in Kolkata Municipal Corporation (KMC), West Bengal. This study compares the performance of ARIMA and other regression techniques in forecasting rainf

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Borrero, Juan D., and Juan-Diego Borrero-Domínguez. "Enhancing Short-Term Berry Yield Prediction for Small Growers Using a Novel Hybrid Machine Learning Model." Horticulturae 9, no. 5 (2023): 549. http://dx.doi.org/10.3390/horticulturae9050549.

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This study presents a novel hybrid model that combines two different algorithms to increase the accuracy of short-term berry yield prediction using only previous yield data. The model integrates both autoregressive integrated moving average (ARIMA) with Kalman filter refinement and neural network techniques, specifically support vector regression (SVR), and nonlinear autoregressive (NAR) neural networks, to improve prediction accuracy by correcting the errors generated by the system. In order to enhance the prediction performance of the ARIMA model, an innovative method is introduced that redu

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Niharika, Katnapally, Chandra Rao Chinta Purna, Kumar Routhu Kishan, Velaga Vasu, Bodepudi Varun, and Murthy Karaka Laxmana. "Big Data-Driven Predictive Analytics in the Financial Sector: A Stock Market Forecasting Approach." International Journal of Recent Innovations in Academic Research 5, no. 12 (2021): 88–99. https://doi.org/10.5281/zenodo.14961172.

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Stock investing is a rigorous monetary technique that relies on demand. As a result, the study of stock projections, or more specifically, the predicting of stock prices, is critical to the stock market. Mistakes in projecting share prices have a huge influence on global finance, necessitating an effective way of anticipating share price movements. ML is one method that may be used to forecast stock values. This research examines how ML and DL models, especially Long Short-Term Memory (LSTM), ARIMA, and Linear Regression (LiR), forecast Google stock prices employing historical data from 2013 t

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Su, Shiwei. "Nonlinear ARIMA Models with Feedback SVR in Financial Market Forecasting." Journal of Mathematics 2021 (November 11, 2021): 1–11. http://dx.doi.org/10.1155/2021/1519019.

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In recent years, as global financial markets have become increasingly connected, the degree of correlation between financial assets has become closer, and technological advances have made the transmission of information faster and faster, and information networks have integrated capital markets into one, making it easier for single financial market risk problems to form systemic risk through a high degree of market linkage effects. Based on the characteristics of financial markets containing both linear and nonlinear components, this paper chooses to use Autoregressive Integrated Moving Averag

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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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Li, Zihan. "A Study on the Combination Prediction of Anhui Residents’ Consumption Level Based on the IOWA Operator." Asian Journal of Probability and Statistics 25, no. 1 (2023): 66–77. http://dx.doi.org/10.9734/ajpas/2023/v25i1540.

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Aims: In order to study the future trend of Anhui residents’ consumption level and predict the consumption level of Anhui residents in the next three years (2022-2024), this paper constructs a combination prediction model based on the induced ordered weighted averaging (IOWA) operator. Study Design: This paper selects the national resident consumption level in Anhui province from 2000 to 2021, which covers a period of 21 years. Based on the data, an IOWA operator combination prediction model is constructed using a multiple regression model, ARIMA (2,2,0) model, and machine learning decis

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Ramadani, Kartika, Sri Wahyuningsih, and Memi Nor Hayati. "Forecasting Stock Price PT. Telkom Using Hybrid Time Series Regression Linear– Autoregressive Integrated Moving Average Model." Jurnal Matematika, Statistika dan Komputasi 18, no. 2 (2022): 293–307. http://dx.doi.org/10.20956/j.v18i2.18837.

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The hybrid method is a method of combining two forecasting models. Hybrid method is used to improve forecasting accuracy. In this study, the Time Series Regression (TSR) linear model will be combined with the Autoregressive Integrated Moving Average (ARIMA) model. The TSR linear model is used to obtain the model and residual value, then the residual value of the TSR linear model will be modeled by the ARIMA model. This combination method will produce a hybrid TSR linear-ARIMA model. The case study in this research is stock closing price (daily) of PT. Telkom Indonesia Tbk. The stock closing pr

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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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Kumar, Neeraj, and Ritu Chauhan. "Speculation of Stock Marketing Using Advanced Recursive Techniques." International Journal of Business Data Communications and Networking 19, no. 1 (2024): 1–18. http://dx.doi.org/10.4018/ijbdcn.339890.

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In the current scenario, the economic status of countries is dependent on stock markets. However, predicting the future prices of any stock is a multifaceted task, as the nature of data is complex and unstructured in nature, which is difficult understand. The focus of the study relies on applying deep neural techniques with regression-based application to discover knowledge from financial databases. The authors have applied LSTM, an advanced version of RNN, and regression-based methods such as ARIMA for predicting future prices of stocks. The study was supported by implementing the techniques

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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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Zhu, Qimian. "Forecasting the US Dollar/Euro Exchange Rate Based on ARIMA Model." Advances in Economics, Management and Political Sciences 15, no. 1 (2023): 369–78. http://dx.doi.org/10.54254/2754-1169/15/20230951.

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The exchange rate is an important indicator for investors and governments as well as a verdict on economic prospects. The euro reached its lowest point in 20 years on August 22, 2022, falling below parity with the dollar and terminating a one-to-one exchange rate with the American currency. The event's great significance provides paramount motivation for us to construct a suitable model capable to forecast it. Numerous modeling methods are proposed to predict and analysis the exchange rate. This paper discussed the applicability of a univariate ARIMA model. Moreover, a multivariable ARIMA mode

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Çatuk, Cüneyt. "Comparative analysis of prediction models for Turkey's sunflower oil imports." Business & Management Studies: An International Journal 13, no. 1 (2025): 406–19. https://doi.org/10.15295/bmij.v13i1.2503.

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This study examines the comparative performance of traditional statistical and machine learning (ML) techniques in forecasting Turkey's sunflower oil imports. The analysis includes Seasonal ARIMA (SARIMA), ARIMAX, Random Forest Regression (RFR), Support Vector Machines (SVM), and Multiple Linear Regression (MLR). The performance of the models is evaluated across short-, medium-, and long-term horizons using a dataset spanning 19 years (2004–2023) and performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Theil's U-statistic (THEIL). Results reveal that RFR co

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Adedotun, Adedayo F. "Hybrid Neural Network Prediction for Time Series Analysis of COVID-19 Cases in Nigeria." Journal of Intelligent Management Decision 1, no. 1 (2022): 46–55. http://dx.doi.org/10.56578/jimd010106.

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The lethal coronavirus illness (COVID-19) has evoked worldwide discussion. This contagious, sometimes fatal illness, is caused by the severe acute respiratory syndrome coronavirus 2. So far, COVID-19 has quickly spread to other countries, sickening millions across the globe. To predict the future occurrences of the disease, it is important to develop mathematical models with the fewest errors. In this study, classification and regression tree (CART) models and autoregressive integrated moving averages (ARIMAs) are employed to model and forecast the one-month confirmed COVID-19 cases in Nigeria

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Agbenyega, Diana Ayorkor, John Andoh, Samuel Iddi, and Louis Asiedu. "Modelling Customs Revenue in Ghana Using Novel Time Series Methods." Applied Computational Intelligence and Soft Computing 2022 (April 18, 2022): 1–8. http://dx.doi.org/10.1155/2022/2111587.

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Governments across the world rely on their Customs Administration to provide functions that include border security, intellectual property rights protection, environmental protection, and revenue mobilisation amongst others. Analyzing the trends in revenue being collected from Customs is necessary to direct government policies and decisions. Models that can capture the trends being purported from the nominal (nonreal) tax values with respect to the trade volumes (value) over the period are indispensable. Predominant amongst the existing models are the econometric models (the GDP-based model, t

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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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Hua, Tianhao. "Regression Forecast of Hong Kong Passenger Flow Based on Baidu Index." Highlights in Business, Economics and Management 45 (December 24, 2024): 635–43. https://doi.org/10.54097/w5xj3381.

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Accurate forecasting of tourist arrivals is crucial for rational planning and resource allocation in the urban tourism industry. In recent years, scholars have found that web search data is correlated with tourism demand, providing new opportunities for such forecasting. This study aims to improve the accuracy of forecasting Hong Kong tourist arrivals by utilizing web search data and advanced machine learning methods. First, this paper employs large-scale web crawling techniques to collect text data related to Hong Kong tourism and applies the Latent Dirichlet Allocation (LDA) model to extract

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Owusu-Ansah, Prince, A. R. Abdul-Aziz, Abena Agyeiwaa Obiri-Yeboah, Adwoa Sarpong Amoah, Saviour Kwame Woangbah, and Ebenezer Adusei. "Modelling road fatalities from tricycle crashes in Ashanti Region, Ghana: An application of regression with ARIMA errors." Transportation Research Interdisciplinary Perspectives 26 (July 2024): 101180. http://dx.doi.org/10.1016/j.trip.2024.101180.

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S, Dhamodharavadhani, and Rathipriya R. "Vaccine rate forecast for COVID-19 in Africa using hybrid forecasting models." African Health Sciences 23, no. 1 (2023): 93–103. http://dx.doi.org/10.4314/ahs.v23i1.11.

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Background: The public health sectors can use the forecasting applications to determine vaccine stock requirements to avoid excess or shortage stock. This prediction will ensure that immunization protection for COVID- 19 is well-distributed among African citizens. Objective: The aim of this study is to forecast vaccination rate for COVID-19 in Africa Methods: The method used to estimate predictions is the hybrid forecasting models which predicts the COVID-19 vaccination rate (CVR). HARIMA is a hybrid of ARIMA and the Linear Regression model and HGRNN is a hybrid of Generalized Regr

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K.G., Emwinloghosa, Pamela O.O., Paschal N.I., Eloho S.O., and Agu C. "Modeling and Forecasting Inflation in Nigeria: A Time Series Regression with ARIMA Method." African Journal of Economics and Sustainable Development 6, no. 3 (2023): 42–53. http://dx.doi.org/10.52589/ajesd-hfyc2bnw.

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This study uses time series regression with autoregressive integrated moving average (ARIMA) modeling to establish a model for forecasting inflation in Nigeria for the period 1981-2020. Akaike Information Criterion Corrected (AICC) and Bayesian Information Criterion (BIC) were used to select the best model among competing models. Through these methods, regression with ARIMA (0,0,1) error was selected as the most parsimonious model for inflation forecasting in Nigeria. The results of the out-sample-forecast show that a high inflation rate will be experienced by the end of 2023, and between 2024

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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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Gao, Youyang, Dechun Yin, Xiaoliang Zhao, Yu Wang, and Yan Huang. "Prediction of Telecommunication Network Fraud Crime Based on Regression-LSTM Model." Wireless Communications and Mobile Computing 2022 (August 8, 2022): 1–16. http://dx.doi.org/10.1155/2022/3151563.

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Telecommunication network fraud crimes frequently occur in China. Predicting the number and trend of telecommunication network fraud will be of great significance to combating crimes and protecting the legal property of citizens. This paper proposes a combined model of predicting telecommunication network fraud crimes based on the Regression-LSTM model. First, we find that there is a strong correlation between privacy data illegally sold on the dark web and telecommunication network fraud data. Hence, this paper constructs a Linear Regression model using the privacy data illegally sold on the

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Khan, Muhammad Shahbaz, Mir Ghulam Hyder Talpur, and Muhammad Aslam. "Comparative Analysis of Time Series Forecasting using ARIMA, and GRNNs Models: A Case Study of Death Rate of Diabetic Mellitus in Canada." VFAST Transactions on Mathematics 12, no. 1 (2024): 415–23. http://dx.doi.org/10.21015/vtm.v12i1.1894.

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This research aims to compare ARIMA and GRNN models alone. For this comparison the death rate for diabetes mellitus time series data of Canada is used. Autoregressive Integrated Moving Average (ARIMA), and Generalized Regression Neural Networks (GRNN) models were applied for time series prediction of the death rate for diabetes mellitus—trained data for two models from 2000 to 2015. Test data was used to compare the precision through data from 2016 to 2021. The ARIMA model was applied using the auto-command through R package which provided the least BIC and AIC values. The mean absolute deviat

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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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Balkan, Dursun, and Goknur Arzu Akyuz. "LABOUR PRODUCTIVITY ANALYSIS OF MANUFACTURING SECTOR IN TURKEY AGAINST EU." Journal of Business Economics and Management 24, no. 2 (2023): 245–73. http://dx.doi.org/10.3846/jbem.2023.19059.

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This study offers an in-depth analysis of labour productivity of manufacturing sector in Turkey and provides a comparison with EU27 and EA19 countries utilizing Eurostat time series data of 63 quarters covering 2005/first quarter-2020/third quarter time interval. Productivity trends are identified and interpreted by relating them with the key macroeconomic events and factors. Multiple linear and non-linear regression equations, and ARIMA model with different parameters are applied to the time series data considering the periods with and without covid effect. Future projections are made for the

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