Introductory chapter: time series analysis

• Autor(a) principal: Viana, Cláudia
• Data de Publicação: 2024
• Outros Autores: Oliveira, Sandra, Rocha, Jorge
• Idioma: eng
• Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Texto Completo: http://hdl.handle.net/10451/65106
• topic: Time Series Analysis; Statistical analysis of time

Time series, defined as sequentially observed data points over time [1], find applications across diverse domains such as economics and engineering. The statistical analysis of time series is crucial, and Chatfield’s taxonomy identifies six main categories: Economic and Financial Time Series, Physical Time Series, Marketing Time Series, Process Control Data, Binary Processes, and Point Processes. To effectively categorize time series, consideration of features like seasonality, trend, and outliers is essential [1]. Seasonality reflects recurring patterns over time intervals, while trend represents a systematic linear or nonlinear component. Outliers are observations distant from others, often indicating anomalies. The categorization and analysis of time series are pivotal for drawing meaningful inferences from the diverse structures encountered in engineering, science, sociology, and economics [2]. The objectives of time series analysis encompass description, explanation, prediction, and control. Description involves plotting observations over time to reveal patterns, while explanation explores relationships between variables. Prediction focuses on forecasting future values, and control utilizes time series to enhance control over physical or economic systems. Possible applications span from land use-cover [3, 4] and agriculture changes [5, 6], tourism [7, 8], socioeconomic vulnerability [9], epidemiology [10], and health [11]. This chapter delves into advanced approaches for time series analysis.