Practical Time Series Analysis by Pal, Avishek -- Read -- Imperial Library of Trantor

Index

Title Page Copyright

Practical Time Series Analysis

Credits About the Authors About the Reviewer www.PacktPub.com

Why subscribe?

Customer Feedback Preface

What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support

Downloading the example code Errata Piracy Questions

Introduction to Time Series

Different types of data

Cross-sectional data Time series data Panel data

Internal structures of time series

General trend Seasonality

Run sequence plot Seasonal sub series plot Multiple box plots

Cyclical changes Unexpected variations

Models for time series analysis

Zero mean models Random walk Trend models Seasonality models

Autocorrelation and Partial autocorrelation Summary

Understanding Time Series Data

Advanced processing and visualization of time series data Resampling time series data

Group wise aggregation Moving statistics

Stationary processes

Differencing

First-order differencing Second-order differencing Seasonal differencing

Augmented Dickey-Fuller test

Time series decomposition

Moving averages

Moving averages and their smoothing effect Seasonal adjustment using moving average Weighted moving average

Time series decomposition using moving averages Time series decomposition using statsmodels.tsa

Summary

Exponential Smoothing based Methods

Introduction to time series smoothing First order exponential smoothing Second order exponential smoothing Modeling higher-order exponential smoothing Summary

Auto-Regressive Models

Auto-regressive models Moving average models

Building datasets with ARMA ARIMA Confidence interval

Summary

Deep Learning for Time Series Forecasting

Multi-layer perceptrons

Training MLPs MLPs for time series forecasting

Recurrent neural networks

Bi-directional recurrent neural networks Deep recurrent neural networks Training recurrent neural networks Solving the long-range dependency problem

Long Short Term Memory Gated Recurrent Units Which one to use - LSTM or GRU?

Recurrent neural networks for time series forecasting

Convolutional neural networks

2D convolutions 1D convolution 1D convolution for time series forecasting

Summary

Getting Started with Python

Installation

Python installers Running the examples

Basic data types

List, tuple, and set Strings Maps

Keywords and functions Iterators, iterables, and generators

Iterators Iterables Generators

Classes and objects Summary

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