TITLE:
Application of Time Series Analysis in Emotion Fluctuation Research
AUTHORS:
Jiayi Zhao
KEYWORDS:
Time Series Analysis, Emotion Fluctuation, Nonlinear Analysis, Ecological Momentary Assessment, Mental Health
JOURNAL NAME:
Open Journal of Social Sciences,
Vol.12 No.11,
November
28,
2024
ABSTRACT: Research on emotion fluctuation is of significant importance for understanding human behavior and mental health. This study explores the application of time series analysis methods in emotion fluctuation research. The research first reviews the developmental history of emotion dynamics studies, highlighting the limitations of traditional cross-sectional research methods. Subsequently, it introduces the main techniques of time series analysis, including autoregressive models, moving average models, and ARIMA models. Through comparative analysis, the study evaluates the strengths and weaknesses of these methods in capturing patterns, periodicity, and trends of emotional changes. The research demonstrates how time series methods reveal the patterns of emotion fluctuation at both individual and group levels. The study also discusses the application of multivariate time series analysis in investigating the interactions between emotions and other psychological and physiological variables. However, the research also points out the challenges faced by time series analysis in emotion research, such as the continuity of data collection and the handling of measurement errors. To address these issues, the study proposes innovative data collection methods combining ecological momentary assessment and wearable devices. This research provides a new methodological perspective for interdisciplinary studies in psychology and data science, contributing to a deeper understanding of emotion dynamic mechanisms.