time series analysis

The accurate imputation of missing values in time series data is paramount for maintaining the integrity and reliability of analyses and predictions. This article investigates the effica-cy of various missing values imputation methods, encom-passing well-known machine learning and statistical tech-niques. Moreover, for a better understanding, they imple-mented two financial data time series: S&P 500 and Bitcoin markets spanning from 2016 to 2023 on a daily frequency. Initially utilizing complete datasets, controlled missingness was introduced by randomly removing 45 data points. Then, these methods applied multiple imputation strategies for estimating and substituting these missing values. Experi-mental evaluation yielded insightful findings regarding the performance of the different methods. The examined ma-chine learning methods, including k-Nearest Neighbors (k-NN), Random Forest, Deep Learning, and Decision Trees, consistently outperformed their statistical counterparts, such as Mean Imputation, Regression Imputation, Hot-Deck Im-putation, and Expectation-Maximization Imputation. Nota-bly, Random Forest emerged as the most effective method, showcasing superior performance in terms of accuracy and robustness. Conversely, the Mean Imputation method exhibited com-paratively inferior outcomes, suggesting its limited suitabil-ity for financial time series data. This research contributes to the ongoing discourse on data integrity within finance ana-lytics and serves as a comprehensive guide for practitioners seeking optimal missing values imputation methods. The empirical evidence provided herein advances the under-standing of imputation techniques' relative performance and their application in financial data, facilitating enhanced de-cision-making processes and yielding more reliable predic-tions.

Social media platforms are vital repositories of user-generated content, reflecting a range of emotions, interests, and discussions. Among these interactions, hate speech has emerged as a significant issue, influencing user behavior. While prior studies have attempted to analyze user characteristics to understand hate attitudes, they often rely on simple statistical comparisons and lack robust methods for causal effect estimation. This study investigates the causal effects of hate speech on user behavior on Twitter (now known as X) during the COVID-19 pandemic, characterized by heightened online discourse and harmful rhetoric. We focus on users who broadcast hate speech to determine how such expressions affect emotional responses. Using a Bayesian structural time-series modeling approach, we isolate the effects of hate speech from confounding factors, providing a solid framework for causal inference. Our findings indicate a significant shift in user emotions following instances of hate speech, demonstrating a measurable impact on user dynamics. We also analyze hashtag usage during this period, emphasizing their role in shaping online discourse. This study enhances understanding of the relationship between hate speech and user behavior, offering insights crucial for researchers, policymakers, and social media platforms in developing strategies to mitigate the adverse effects of online hate speech.