Retraction: Intelligent Monitoring and Forecasting Using Machine Learning Techniques (J. Phys.: Conf. Ser. 1916 012175)
نویسندگان
چکیده
This article (and all articles in the proceedings volume relating to same conference) has been retracted by IOP Publishing following an extensive investigation line with COPE guidelines. uncovered evidence of systematic manipulation publication process and considerable citation manipulation. respectfully requests that readers consider work within this potentially unreliable, as not through a credible peer review process. regrets our usual quality checks did identify these issues before publication, have since put additional measures place try prevent from reoccurring. wishes credit anonymous whistleblowers Problematic Paper Screener [1] for bringing some above attention, prompting us investigate further. Cabanac G, Labbé C Magazinov A 2021 arXiv: 2107.06751v1 Retraction published: 23 February 2022
منابع مشابه
Electricity Load Forecasting Using Machine Learning Techniques
Electricity load forecasting has become increasingly important due to the strong impact on the operational efficiency of the power system. However, the accurate load prediction remains a challenging task due to several issues such as the nonlinear character of the time series or the seasonal patterns it exhibits. A large variety of techniques have been proposed to this aim, such as statistical ...
متن کاملElectricity Load Forecasting Using Machine Learning Techniques
Electricity load forecasting has become increasingly important due to the strong impact on the operational efficiency of the power system. However, the accurate load prediction remains a challenging task due to several issues such as the nonlinear character of the time series or the seasonal patterns it exhibits. A large variety of techniques have been proposed to this aim, such as statistical ...
متن کاملElectricity Load Forecasting Using Machine Learning Techniques
Electricity load forecasting has become increasingly important due to the strong impact on the operational efficiency of the power system. However, the accurate load prediction remains a challenging task due to several issues such as the nonlinear character of the time series or the seasonal patterns it exhibits. A large variety of techniques have been proposed to this aim, such as statistical ...
متن کاملUsing Machine Learning Techniques to Combine Forecasting Methods
We present here an original work that uses machine learning techniques to combine time series forecasts. In this proposal, a machine learning technique uses features of the series at hand to define adequate weights for the individual forecasting methods being combined. The combined forecasts are the weighted average of the forecasts provided by the individual methods. In order to evaluate this ...
متن کاملMachine Learning Models for Housing Prices Forecasting using Registration Data
This article has been compiled to identify the best model of housing price forecasting using machine learning methods with maximum accuracy and minimum error. Five important machine learning algorithms are used to predict housing prices, including Nearest Neighbor Regression Algorithm (KNNR), Support Vector Regression Algorithm (SVR), Random Forest Regression Algorithm (RFR), Extreme Gradient B...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Physics: Conference Series
سال: 2021
ISSN: ['1742-6588', '1742-6596']
DOI: https://doi.org/10.1088/1742-6596/1916/1/012412