Hands-on Time Series Analysis with Python: From Basics to Bleeding Edge Techniques

Time-dependent data calls for more than a standard regression approach. This practical Python guide builds from the characteristics and preparation of time series to established forecasting models and neural-network techniques, giving readers a clear route through a broad modeling toolkit.

B V Vishwas and Ashish Patel begin with the shape of time-series data—trend, seasonality, cycles, and residual variation—before turning to hands-on preparation and analysis. The emphasis is on understanding the data and the method together, with examples and code available in Jupyter notebooks.

Start with the signal, not just the model

Early chapters cover data wrangling, resampling, smoothing, and techniques for examining or addressing stationarity. Readers encounter exponential smoothing, differencing, unit-root tests, and the patterns explored through autocorrelation and partial autocorrelation. These foundations set the context for choosing and interpreting forecasting methods.

Classical forecasting, clearly mapped

The book moves into autoregressive methods and models including ARIMA, seasonal ARIMA, SARIMAX, and vector autoregression. Rather than focusing on one family alone, it surveys approaches for different time-series settings and shows how traditional statistical methods fit into a wider analysis workflow.

Neural networks for univariate and multivariate data

Later chapters introduce neural-network fundamentals and explore recurrent networks, LSTMs, GRUs, convolutional neural networks, and autoencoders. Dedicated sections address both univariate and multivariate time series, including different ways to prepare data for single-step and horizon-style forecasting.

From core methods to newer frameworks

A final chapter turns to Prophet, extending the book’s range beyond the earlier statistical and deep-learning methods. The overall progression makes this a useful reference for readers comparing modeling approaches and looking to implement time-series analysis in Python.

For analysts and data practitioners

Data scientists, data analysts, financial analysts, and stock-market researchers will find coverage that spans foundational concepts through advanced forecasting techniques. Bring curiosity about your data—and a willingness to test models against it.