This book is about an introduction to time series analysis using Python. We aim to give you a clear overview of the basic concepts of the discipline and describe useful techniques that would be applicable for commonly-found analytics use cases in the industry. With too many projects requiring trend analytics and forecasting based on past data, time series analysis is an important tool in the knowledge arsenal of any modern data scientist. This book will equip you with tools and techniques, which will let you confidently think through a problem and come up with its solution in time series forecasting.
Why Python? Python is rapidly becoming a first choice for data science projects across different industry sectors. Most state-of-the art machine learning and deep learning libraries have a Python API. As a result, many data scientists prefer Python to implement the entire project pipeline that consists of data wrangling, model building, and model validation. Besides, Python provides easy-to-use APIs to process, model, and visualize time series data. Additionally, Python has been a popular language for the development of backend for web applications and hence has an appeal to a wider base of software professionals.
Now, let's see what you can expect to learn from every chapter this book.
