This book, filled with industry-tested tips and tricks, takes you beyond commonly used classical statistical methods such as ARIMA and introduces to you the latest techniques from the world of ML. This is a comprehensive guide to analyzing, ...

Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and ...

About the book Time Series Forecasting in Python teaches you how to get immediate, meaningful predictions from time-based data such as logs, customer analytics, and other event streams.

Time series forecasting is different from other machine learning problems.

This book aims to deepen your understanding of time series by providing a comprehensive overview of popular Python time-series packages and help you build better predictive systems.

Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality.

This book demystifies the technique, providing readers with little or no time series or machine learning experience the fundamental tools required to create and evaluate time series models.

Everything you need to get started is contained within this book. Deep Time series Forecasting with Python is your very own hands on practical, tactical, easy to follow guide to mastery. Buy this book today and accelerate your progress!

This book brings together all of the important new results on the state space framework for exponential smoothing.

This book covers practical techniques for working with time series data, starting with ingesting time series data from various sources and formats, whether in private cloud storage, relational databases, non-relational databases, or ...