Book Image

Hands-On Machine Learning on Google Cloud Platform

By : Giuseppe Ciaburro, V Kishore Ayyadevara, Alexis Perrier
Book Image

Hands-On Machine Learning on Google Cloud Platform

By: Giuseppe Ciaburro, V Kishore Ayyadevara, Alexis Perrier

Overview of this book

Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at scale but also host them in the cloud to make predictions. This book is focused on making the most of the Google Machine Learning Platform for large datasets and complex problems. You will learn from scratch how to create powerful machine learning based applications for a wide variety of problems by leveraging different data services from the Google Cloud Platform. Applications include NLP, Speech to text, Reinforcement learning, Time series, recommender systems, image classification, video content inference and many other. We will implement a wide variety of deep learning use cases and also make extensive use of data related services comprising the Google Cloud Platform ecosystem such as Firebase, Storage APIs, Datalab and so forth. This will enable you to integrate Machine Learning and data processing features into your web and mobile applications. By the end of this book, you will know the main difficulties that you may encounter and get appropriate strategies to overcome these difficulties and build efficient systems.
Table of Contents (18 chapters)
8
Creating ML Applications with Firebase

Classical approach to time series

So far we have dealt with time series according to a classic approach to the topic. In this perspective, the classic models that try to simulate the phenomenon can be of two types:

  • Composition models: The elementary components are known, and, by assuming a certain form of aggregation, the resulting series is obtained
  • Decomposition models: From an observed series is hypothesized the existence of some elementary trends of which we want to establish the characteristics

The decomposition models are the most used in practice, and, for this reason, we will analyze them in detail.

The components of a time series can be aggregated according to different types of methods:

  • Additive method: Y(t) = τ(t) + C(t) + S(t) + r(t)
  • Multiplicative method: Y(t) = τ(t) * C(t) * S(t) * r(t)
  • Mixed method: Y(t) = τ(t) * C(t) + S(t) * r(t)

In these...