coverpage
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
更新时间：2021-07-08 10:18:47
完结共106章
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