Automated Data Correction in Time Series Analysis Using Machine Learning

Abstract

Automated data correction is an important aspect of time series analysis,_x000D_ as errors or inconsistencies in the data can significantly impact the accuracy of_x000D_ the results. In recent years, machine learning techniques have become_x000D_ increasingly popular for addressing this issue. This research aims to explore_x000D_ the application of machine learning algo-rithms for automated data correction_x000D_ in time series analysis. The study will focus on developing a framework for_x000D_ identifying and correcting er-rors in time series data using machine learning_x000D_ methods. The framework will involve preprocessing techniques to clean and_x000D_ transform raw data, feature selection to identify relevant variables, and model_x000D_ building to predict missing values or correct errors. The study will test the_x000D_ effec-tiveness of the framework on various types of time series data, including_x000D_ financial, environmental, and health-related data. The results of the re-search_x000D_ will provide insights into the efficiency and accuracy of machine learningbased_x000D_ techniques for data correction in time series analysis, with potential applications_x000D_ in various industry sectors. Overall, this research has significant implications_x000D_ for improving the reliability and validity of time series analysis, supporting_x000D_ evidence-based decision-making, and ad-vancing the field of data science.