Hands-on Time Series Analysis With Python: From Basics To Bleeding Edge Techniques

Table of Contents......Page 4
About the Authors......Page 11
About the Technical Reviewer......Page 13
Acknowledgments......Page 14
Introduction......Page 15
Chapter 1: Time-Series Characteristics......Page 16
Time-Series Data......Page 17
Panel Data/Longitudinal Data......Page 19
Detecting Trend Using a Hodrick-Prescott Filter......Page 21
Detrending a Time Series......Page 22
Detrending Using Pandas Differencing......Page 23
Detrending Using a SciPy Signal......Page 24
Detrend Using an HP Filter......Page 25
Seasonality......Page 26
Multiple Box Plots......Page 27
Autocorrelation Plot......Page 28
Deseasoning of Time-Series Data......Page 29
Seasonal Decomposition......Page 30
Cyclic Variations......Page 31
Detecting Cyclical Variations......Page 32
Decomposing a Time Series into Its Components......Page 33
Summary......Page 36
Chapter 2: Data Wrangling and Preparation for Time Series......Page 37
Loading Data Using CSV......Page 38
Loading Data Using JSON......Page 39
Loading Data from a URL......Page 40
Selecting the Top Five Records......Page 41
Applying a Filter......Page 42
Distinct (Unique)......Page 43
IN......Page 44
NOT IN......Page 46
Ascending Data Order......Page 47
Descending Data Order......Page 48
Aggregation......Page 49
GROUP BY......Page 50
GROUP BY with Aggregation......Page 52
Join (Merge)......Page 53
INNER JOIN......Page 55
LEFT JOIN......Page 57
RIGHT JOIN......Page 60
OUTER JOIN......Page 62
Summary of the DataFrame......Page 65
Resampling......Page 66
Resampling by Year......Page 67
Resampling by Week......Page 68
Windowing Function......Page 69
Rolling Window......Page 70
Exponentially Weighted Moving Window......Page 71
Shifting......Page 72
Handling Missing Data......Page 74
FFILL......Page 76
FILLNA......Page 77
Summary......Page 78
Chapter 3: Smoothing Methods......Page 79
Introduction to Simple Exponential Smoothing......Page 80
Simple Exponential Smoothing in Action......Page 82
Introduction to Double Exponential Smoothing......Page 90
Double Exponential Smoothing in Action......Page 92
Introduction to Triple Exponential Smoothing......Page 100
Triple Exponential Smoothing in Action......Page 101
Summary......Page 111
Types of Stationary Behavior in a Time Series......Page 112
Using Summary Statistics......Page 114
Using Statistics Unit Root Tests......Page 115
Augmented Dickey-Fuller Test......Page 117
Kwiatkowski-Phillips-Schmidt-Shin Test......Page 118
Differencing......Page 119
Random Walk......Page 120
First-Order Differencing (Trend Differencing)......Page 121
Second-Order Differencing (Trend Differencing)......Page 122
Second-Order Differencing for Seasonal Data......Page 123
Autoregressive Models......Page 124
Moving Average......Page 126
Autocorrelation and Partial Autocorrelation Functions......Page 129
Introduction to ARMA......Page 130
Autoregressive Model......Page 131
Introduction to Autoregressive Integrated Moving Average......Page 132
The Integration (I)......Page 134
ARIMA in Action......Page 135
Introduction to Seasonal ARIMA......Page 142
SARIMA in Action......Page 144
Introduction to SARIMAX......Page 156
SARIMAX in Action......Page 157
Introduction to Vector Autoregression......Page 167
VAR in Action......Page 168
VARMA in Action......Page 185
Summary......Page 197
Introduction to Neural Networks......Page 198
Perceptron......Page 199
Binary Step Function......Page 201
Linear Activation Function......Page 202
Nonlinear Activation Function......Page 203
Sigmoid......Page 204
Rectified Linear Unit......Page 205
Parametric ReLU......Page 206
Softmax......Page 207
Forward Propagation......Page 208
Backward Propagation......Page 209
Learning Rate vs. Gradient Descent Optimizers......Page 212
Recurrent Neural Networks......Page 215
Feed-Forward Recurrent Neural Network......Page 217
Backpropagation Through Time in RNN......Page 219
Long Short-Term Memory......Page 223
Step-by-Step Explanation of LSTM......Page 225
Peephole LSTM......Page 227
Peephole Convolutional LSTM......Page 228
Gated Recurrent Units......Page 229
Convolution Neural Networks......Page 232
Generalized CNN Formula......Page 235
One-Dimensional CNNs......Page 236
Auto-encoders......Page 237
Summary......Page 239
Single-Step Data Preparation for  Time-Series Forecasting......Page 240
Horizon-Style Data Preparation for  Time-Series Forecasting......Page 242
LSTM Univariate Single-Step Style in Action......Page 243
LSTM Univariate Horizon Style in Action......Page 255
Bidirectional LSTM Univariate Single-Step Style in Action......Page 266
Bidirectional LSTM Univariate Horizon Style in Action......Page 275
GRU Univariate Single-Step Style in Action......Page 284
GRU Univariate Horizon Style in Action......Page 292
Auto-encoder LSTM Univariate Single-Step Style in Action......Page 301
Auto-encoder LSTM Univariate Horizon Style in Action......Page 310
CNN Univariate Single-Step Style in Action......Page 319
CNN Univariate Horizon Style in Action......Page 328
Summary......Page 337
LSTM Multivariate Horizon Style in Action......Page 338
Bidirectional LSTM Multivariate Horizon Style in Action......Page 350
Auto-encoder LSTM Multivariate Horizon Style in Action......Page 359
GRU Multivariate Horizon Style in Action......Page 369
CNN Multivariate Horizon Style in Action......Page 378
Summary......Page 387
The Prophet Model......Page 388
Implementing Prophet......Page 389
Adding Log Transformation......Page 394
Adding Built-in Country Holidays......Page 399
Adding Exogenous variables using add_regressors(function)......Page 402
Summary......Page 407
Index......Page 408