Machine Learning for Time-Series with Python

The term time-series analysis (TSA) refers to the statistical approach to time-series or the analysis of trend and seasonality. It is often an ad hoc exploration and analysis that usually involves visualizing distributions, trends, cyclic patterns, and relationships between features, and between features and the target(s). More generally, we can say TSA is roughly exploratory data analysis (EDA) that's specific to time-series data. This comparison can be misleading however since TSA can include both descriptive and exploratory elements. Let's see quickly the differences between descriptive and exploratory analysis: Therefore, TSA is the initial investigation of a dataset with the goal of discovering patterns, especially trend and seasonality, and obtaining initial insights, testing hypotheses, and extracting meaningful summary statistics.