The course content of Time Series Analysis covers commonly used statistical models for analyzing time series data, inference processes, understanding dynamic relationships, forecasting future trends, and practical applications. It can play a crucial role in promoting the Sustainable Development Goals (SDGs). By examining the trends of key indicators over time (e.g., government economic data, climate change data, industrial production data, etc.), it can provide stakeholders with strong data support for making optimal decisions, assessing the effectiveness of policy interventions, or detecting occurrences of variation. Below are examples illustrating the relationship between time series analysis and SDG 13 (Climate Action): - Time series analysis is an important tool for analyzing climate change trends, including modeling global temperature changes, monitoring long-term trends of sea-level rise and greenhouse gas emissions. The analysis results provide insights into the patterns of climate change, enabling stakeholders to take urgent action to address climate change and its impacts. Techniques applied include ARIMA models and seasonal decomposition to analyze long-term trends and periodic fluctuations in climate data. - Spatiotemporal data analysis is used to predict extreme climate events (e.g., the frequency and intensity of extreme weather events such as heavy rainfall, droughts, and forest fires) to enhance disaster prevention and mitigation capabilities. Techniques applied include spatiotemporal covariance modeling and extreme value theory. - Renewable energy or green electricity generation forecasting, through spatiotemporal analysis of wind and solar data, can assess and predict production (and consumption) trends for renewable energy sources such as solar and wind energy, or optimize the construction and network operation of energy infrastructure. Techniques applied include spatial-temporal state-space models and Kalman filters, suitable for real-time monitoring and energy production forecasting. The Statistics Department offers a course on Time Series Analysis in every academic year's second semester. This course is an elective for the Statistics Department, balancing statistical theory and practical application, and all enrolled students must complete a practical analysis project.