Summary

Time series analysis plays a pivotal role in extracting meaningful insights and making informed decisions in a wide range of industries. As technology advances, sophisticated time series techniques will become increasingly integral to understanding complex temporal patterns and trends. Whether in finance, healthcare, or transportation, the ability to analyze and forecast time-dependent data empowers organizations to adapt, optimize, and make strategic decisions in an ever-evolving landscape.

In this chapter, we covered techniques for handling missing values and outliers, differencing methods, and feature engineering in time series analysis. We learned how to use ffill and bfill for missing values and compared their effects on stock price data. Differencing techniques, including first, second, and seasonal differencing, were applied to achieve stationarity and were evaluated using ADF tests. We also explored lagged features for capturing temporal dependencies and assessed...