Introduction

Time series analysis has one of the most fragmented library ecosystems in Python. Depending on whether you need statistical forecasting, machine learning pipelines, deep learning models, or anomaly detection, you'll reach for different tools — each with its own API, data format expectations, and assumptions about your problem.

This article covers seven useful Python libraries that collectively span the full spectrum of time series work, including:

Classical and automated statistical forecasting

Scalable machine learning pipelines and model selection

Deep learning and ensemble-based forecasting

Anomaly detection, outlier analysis, and motif discovery

Let's explore them.

1. Building Statistical Forecasting Models with statsmodels

When interpretability is just as important as predictive power, statsmodels is the first library to reach for.…