Time Series Analysis is the study of data collected sequentially over time, with the aim of understanding dependencies, extracting meaningful patterns, and making predictions about future observations.
Time series methods are essential tools in economics, finance, climate science, and many other fields where understanding temporal dynamics is crucial.
The field has its modern foundations in the work of Box and Jenkins (1970), and has since then been shaped through intensive research e.g. by the Nobel Prize-winning contributions on autoregressive conditional heteroskedasticity and cointegration (Engle and Granger, 2003) and vector autoregressions for causal inference (Sims, 2011). Modern advances in computational power together with big and new data have expanded the scope of time series analysis, enabling researchers to better model complex phenomena such as non-linear relationships and structural breaks, often through machine learning techniques.