Results 11 to 20 of about 412,561 (324)

Adaptive Online Learning for the Autoregressive Integrated Moving Average Models [PDF]

Mathematics, 2021
This paper addresses the problem of predicting time series data using the autoregressive integrated moving average (ARIMA) model in an online manner. Existing algorithms require model selection, which is time consuming and unsuitable for the setting of ...
Weijia Shao, Lukas Friedemann Radke, Fikret Sivrikaya, Sahin Albayrak   +3 more
doaj   +4 more sources

Global Forecasting Confirmed and Fatal Cases of COVID-19 Outbreak Using Autoregressive Integrated Moving Average Model. [PDF]

Front Public Health, 2020
The world health organization (WHO) formally proclaimed the novel coronavirus, called COVID-19, a worldwide pandemic on March 11 2020. In December 2019, COVID-19 was first identified in Wuhan city, China, and now coronavirus has spread across various ...
Dansana D, Kumar R, Das Adhikari J, Mohapatra M, Sharma R, Priyadarshini I, Le DN.   +6 more
europepmc   +2 more sources

Application of Combined Models Based on Empirical Mode Decomposition, Deep Learning, and Autoregressive Integrated Moving Average Model for Short-Term Heating Load Predictions [PDF]

Sustainability, 2022
Short-term building energy consumption prediction is of great significance for the optimized operation of building energy management systems and energy conservation.
Yong Zhou, Lingyu Wang, J. Qian
semanticscholar   +2 more sources

Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving Average (ARIMA) Model.

JMIR Public Health Surveill, 2020
Background The coronavirus disease (COVID-19) pandemic has affected more than 200 countries and has infected more than 2,800,000 people as of April 24, 2020. It was first identified in Wuhan City in China in December 2019. Objective The aim of this study
Singh RK, Rani M, Bhagavathula AS, Sah R, Rodriguez-Morales AJ, Kalita H, Nanda C, Sharma S, Sharma YD, Rabaan AA, Rahmani J, Kumar P.   +11 more
europepmc   +2 more sources

Statistical Analysis of Autoregressive Fractionally Integrated Moving Average Models [PDF]

Computational Statistics (2013), 28(5), 2309-2331, 2012
In practice, several time series exhibit long-range dependence or persistence in their observations, leading to the development of a number of estimation and prediction methodologies to account for the slowly decaying autocorrelations. The autoregressive fractionally integrated moving average (ARFIMA) process is one of the best-known classes of long ...
Contreras Reyes, J., Palma M., Wilfredo
arxiv   +9 more sources

Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models. [PDF]

PLoS One, 2020
Infectious disease outbreaks pose a significant threat to human health worldwide. The outbreak of pandemic coronavirus disease 2019 (COVID-19) has caused a global health emergency.
Pourghasemi HR, Pouyan S, Farajzadeh Z, Sadhasivam N, Heidari B, Babaei S, Tiefenbacher JP.   +6 more
europepmc   +2 more sources

Beta Autoregressive Fractionally Integrated Moving Average Models [PDF]

Journal of Statistical Planning and Inference, 2018
In this work we introduce the class of beta autoregressive fractionally integrated moving average models for continuous random variables taking values in the continuous unit interval $(0,1)$. The proposed model accommodates a set of regressors and a long-range dependent time series structure.
Guilherme Pumi, Marcio Valk, Cleber Bisognin, Fábio Mariano Bayer, Taiane Schaedler Prass   +4 more
arxiv   +5 more sources

Time Series Modeling and Forecasting Using Autoregressive Integrated Moving Average and Seasonal Autoregressive Integrated Moving Average Models

Instrumentation Mesure Métrologie, 2023
Time series analysis is pivotal in discerning temporospatial data patterns and facilitating precise forecasts. This study scrutinizes the cardinal challenges associated with time series modeling, namely stationarity, parsimony, and overfitting, focusing ...
Vignesh Arumugam, Vijayalakshmi Natarajan   +1 more
semanticscholar   +2 more sources

Implementasi Model Autoregressive Integrated Moving Average pada Proyeksi Komoditas Ekspor Timah

Jambura Journal of Mathematics, 2023
The Bangka Belitung Archipelago is a potential area in the mining sector because many soils contain tin minerals and minerals that are spread evenly. Based on this phenomenon, this study uses the ARIMA model to analyze the prediction of the number of tin
Desy Yuliana Dalimunthe, Herman Aldila
doaj   +3 more sources

Time series analysis of electric energy consumption using autoregressive integrated moving average model and Holt Winters model

TELKOMNIKA (Telecommunication Computing Electronics and Control), 2021
With the increasing demand of energy, the energy production is not that much sufficient and that’s why it has become an important issue to make accurate prediction of energy consumption for efficient management of energy.
Nahid Ferdous Aurna, Md. Tanjil Mostafa Rubel, Tanveer Ahmed Siddiqui, Tajbia Karim, Sabrina Saika, Murshedul Arifeen, Tasmima Noushiba Mahbub, S. M. Salim Reza, Habibul Kabir   +8 more
semanticscholar   +3 more sources