This book explains the concepts of time series from traditional to bleeding-edge techniqueswith full-fledged examples. The book begins by covering time series fundamentals and its characteristics, the structureof time series data, pre-processing, and ways of crafting the features through datawrangling. Next, it covers the traditional time series techniques like Smoothing methods, ARMA, ARIMA, SARIMA, SARIMAX, VAR, VARMA using trending framework likeStatsModels, pmdarima. Further, Book explains the building classification models usingsktime, and covers how to leverage advance deep learning-based techniques like ANN, CNN, RNN, LSTM, GRU and Autoencoder to solve time series problem usingTensorflow. It finally concludes by explaining the popular framework fbprophet formodeling time series analysis. After completion of the book, the reader will have a good understanding of working withdifferent techniques of time series methods. All the codes presented in this notebook areavailable in Jupyter notebooks, which allows readers to do hands-on and enhance them inexciting ways. What will you learn? What are the basics and advanced concepts of time series?How to design, develop, train, and validate time-series methodologies. What are smoothing, ARMA, ARIMA, SARIMA, SRIMAX, VAR, VARMA techniques in time series. Learn how to leverage bleeding-edge techniques such as ANN, CNN, RNN, LSTM, GRU, Autoencoder and fbprophet. Who this book is for? Data scientist, Data Analyst, Financial Analysts, and Stock Market Researchers | Hands-on Time Series Analysis With Python by B V Vishwas, Paperback | Indigo Chapters