نتایج جستجو برای: auto regressive moving average time series
تعداد نتایج: 2475685 فیلتر نتایج به سال:
Introduction: Air pollutants and respiratory diseases have a significant relationship and cause major health problems. Low attention has been paid to the daily hospital admissions due to the presence of pollutants in desert cities such as Yazd city, Iran. Therefore, this study aimed to investigate the short-term relationship between air pollution and daily hospital admissions due to respiratory...
abstract-due to the important role productivity plays in future decision making and programming, the productivity indexes should have accurate quantities. in this study, auto-regressive distributed lag (ardl) and genetic algorithm (ga) methods are applied to time series of 1978-2008 to accurately measure total factor productivity (tfp) in the agricultural sector of iran. the comparison of these...
OBJECTIVE To analyze the behavior temporal of road-traffic injuries (RTI) in Valledupar, Colombia from January 2008 to December 2012. METHODOLOGY An observational study was conducted based on records from the Colombian National Legal Medicine and Forensic Sciences Institute regional office in Valledupar. Different variables were analyzed, such as the injured person's sex, age, education level...
The integration of renewable energy resources into smart grids has become increasingly important to address the challenges managing and forecasting production in fourth revolution. To this end, artificial intelligence (AI) emerged as a powerful tool for improving control management. This study investigates application machine learning techniques, specifically ARIMA (auto-regressive integrated m...
Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Fuzzy autoregressive integrated moving average (FARIMA) models are the fuzzy improved version of the autoregressive integrated moving average (ARIMA) models, proposed in order to overcome limitations of the traditional ARIMA models; especially data limitation, and yield...
Ocean wave height is one of the critical factors to decide efficiency ocean energy conversion system. Usually, only when resonate occurs between (ocean speed in vertical direction) and system, can from into electric be maximized. Therefore, this paper proposes two predication methods predict future 1.5–2.5 s. Firstly, data fitting real achieved by polynomial method, which beneficial height. Sec...
To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed. The ARMA model parameters are employed as state arguments. Unknown time-varying estimators of observation noise are used to achieve the estimated mean and variance of the observatio...
Zoonosis refers to the transmission of infectious diseases from animal to human. The increasing number of zoonosis incidence makes the great losses to lives, including humans and animals, and also the impact in social economic. It motivates development of a system that can predict the future number of zoonosis occurrences in human. This paper analyses and presents the use of Seasonal Autoregres...
Dataset with Outliers causes poor accuracy in future analysis of data mining tasks. To improve the performance of mining task, it is necessary to detect and revamp of outliers which are there in the dataset. Existing techniques like ARMA (Auto-Regressive Moving Average), ARIMA (AutoRegressive Integrated Moving Average) and Multivariate Linear Gaussian state space model don't consider the p...
water quality is a worldwide problem which affects human beings lives fundamentally. water scarcity is intensified in result of quality deterioration. different factors such as population increase, economic development and water pollution could be considered as the origins of the problem. the study and forecasting of water quality is necessary to prevent serious water quality deteriorations in ...
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