Introducing Time Series
Welcome to Advanced Time Series Analysis Using Python! This book is intended for data scientists or machine learning (ML) engineers who want to level up their time series analysis skills by learning new and advanced techniques from the ML world. Time series analysis is something that is commonly overlooked in regular ML books, courses, and so on. They typically start with classification, touch upon regression, and then move on. But it is also something that is immensely valuable and ubiquitous in business. As long as time is one of the four dimensions in the world we live in, time series data is all-pervasive.
Analyzing time series data unlocks a lot of value for a business. Time series analysis isn't new—it's been around since the 1920s and 1930s. But in the current age of data, the time series that are collected by businesses are growing larger and wider by the minute. Combined with an explosion in the quantum of data collected and the renewed interest in ML, the landscape of time series analysis also changed considerably. This book attempts to take you beyond classical statistical methods such as AutoRegressive Integrated Moving Average (ARIMA) and introduce to you the latest techniques from the ML world in time series analysis.
We are going to start with the basics and quickly scale up to more complex topics. In this chapter, we're going to cover the following main topics:
- What is a time series?
- Data-generating process (DGP)
- What can we forecast?
- Forecasting terminology and notation
