Chapter 1, Introduction to Time Series, starts with a discussion of the three different types of datasets—cross-section, time series, and panel. The transition from cross-sectional to time series and the added complexity of data analysis is discussed. Special mathematical properties that make time series data special are described. Several examples demonstrate how exploratory data analysis can be used to visualize these properties.

Chapter 2, Understanding Time Series Data, covers three topics, advanced preprocessing and visualization of time series data through resampling, group-by, and calculation of moving averages; stationarity and statistical hypothesis testing to detect stationarity in a time series; and various methods of time series decomposition for stationarizing a non-stationary time series.

Chapter 3, Exponential Smoothing based Methods, covers smoothing-based models using the Holt-Winters approach for first order to capture levels, second order to smoothen levels and trend, and higher order smoothing is illustrated, which captures level, trend, and seasonality within a time series dataset.

Chapter 4, Auto-Regressive Models, discusses autoregressive models for forecasting. The chapter covers a detailed implementation for moving average (MA), autoregressive (AR), Auto Regressive Moving Average (ARMA), and Auto Regressive Integrated Moving Average (ARIMA) to capture different levels of nuisance within time series data during forecasting.

Chapter 5, Deep Learning for Time Series Forecasting, discusses recent deep learning algorithms that can be directly adapted to develop forecasting models for time series data. Recurrent Neural Networks (RNNs) are a natural choice for modeling sequence in data. In this chapter, different RNNs such as Vanilla RNN, Gated Recurrent Units, and Long Short Term Memory units are described to develop forecasting models on time series data. The mathematical formulations involved in developing these RNNs are conceptually discussed. Case studies are solved using the ‘keras’ deep learning library of Python.

Appendix, Getting Started with Python, you will find a quick and easy introduction to Python. If you are new to Python or looking for how to get started with the programming language, reading this appendix will help you get through the initial hurdles.

You will need the Anaconda Python Distribution to run the examples in this book and write your own Python programs for time series analysis. This is freely downloadable from https://www.continuum.io/downloads.

The code samples of this book have been written using the Jupyter Notebook development environment. To run the Jupyter Notebooks, you need to install Anaconda Python Distribution, which has the Python language essentials, interpreter, packages used to develop the examples, and the Jupyter Notebook server.

The topics in this book are expected to be useful for the following people:

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

A block of code is set as follows:

import os
import pandas as pd
%matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns

In-text code is highlighted in font and color as here: pandas.DataFrame. File and folder names are also shown in the same style, for example,  Chapter_1_Models_for_Time_Series_Analysis.ipynb or datasets/DJIA_Jan2016_Dec2016.xlsx

At several places in the book, we have referred to external URLs to cite source of datasets or other information. A URL would appear in the following text style: http://finance.yahoo.com

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "In order to download new modules, we will go to Files | Settings | Project Name | Project Interpreter."

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