Course overview
This course aims to provide students with a comprehensive understanding of time series data and the statistical methods required to analyse temporal patterns. Students will explore forecasting models such as ARIMA, exponential smoothing, and machine learning techniques tailored to time series data. They will gain practical experience in building models, evaluating their accuracy, and making data-driven decisions based on temporal trends. Furthermore, students will assess the ethical implications of time series data and forecast applications, ensuring they integrate responsible data practices when developing real-world solutions.
Course learning outcomes
- Apply fundamental time series analysis techniques to identify and model trends, seasonality, and noise within temporal data
- Develop and evaluate ARIMA, SARIMA, and exponential smoothing models for forecasting time series data
- Use machine learning models to forecast complex time series data and compare their performance to traditional methods
- Interpret and assess the accuracy of time series models using appropriate evaluation metrics
- Critically evaluate the ethical considerations in time series forecasting, ensuring responsible and unbiased applications of temporal models