Advanced Forecasting with Python : With State-Of-the-Art-Models Including LSTMs, Facebook's Prophet, and Amazon's DeepAR.

Format:

Book

Author/Creator:

Korstanje, Joos.

Language:

English

Physical Description:

1 online resource (294 pages)

Other Title:

Advanced Forecasting with Python

Place of Publication:

Berkeley, CA : Apress L. P., 2021.

Summary:

Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook's open-source Prophet model, and Amazon's DeepAR model. Rather than focus on a specific set of models, this book presents an exhaustive overview of all the techniques relevant to practitioners of forecasting. It begins by explaining the different categories of models that are relevant for forecasting in a high-level language. Next, it covers univariate and multivariate time series models followed by advanced machine learning and deep learning models. It concludes with reflections on model selection such as benchmark scores vs. understandability of models vs. compute time, and automated retraining and updating of models. Each of the models presented in this book is covered in depth, with an intuitive simple explanation of the model, a mathematical transcription of the idea, and Python code that applies the model to an example data set. Reading this book will add a competitive edge to your current forecasting skillset. The book is also adapted to those who have recently started working on forecasting tasks and are looking for an exhaustive book that allows them to start with traditional models and gradually move into more and more advanced models. What You Will Learn Carry out forecasting with Python Mathematically and intuitively understand traditional forecasting models and state-of-the-art machine learning techniques Gain the basics of forecasting and machine learning, including evaluation of models, cross-validation, and back testing Select the right model for the right use case Who This Book Is For The advanced nature of the later chapters makes the book relevant for applied experts working in the domain of forecasting, as the models covered have been published only recently. Experts working in the domain will want to update their skills as traditional models are regularly being outperformed by newer models.

Contents:

Intro

Table of Contents

About the Author

About the Technical Reviewer

Introduction

Part I: Machine Learning for Forecasting

Chapter 1: Models for Forecasting

Reading Guide for This Book

Machine Learning Landscape

Univariate Time Series Models

A Quick Example of the Time Series Approach

Supervised Machine Learning Models

A Quick Example of the Supervised Machine Learning Approach

Correlation Coefficient

Other Distinctions in Machine Learning Models

Supervised vs. Unsupervised Models

Classification vs. Regression Models

Univariate vs. Multivariate Models

Key Takeaways

Chapter 2: Model Evaluation for Forecasting

Evaluation with an Example Forecast

Model Quality Metrics

Metric 1: MSE

Metric 2: RMSE

Metric 3: MAE

Metric 4: MAPE

Metric 5: R2

Model Evaluation Strategies

Overfit and the Out-of-Sample Error

Strategy 1: Train-Test Split

Strategy 2: Train-Validation-Test Split

Strategy 3: Cross-Validation for Forecasting

K-Fold Cross-Validation

Time Series Cross-Validation

Rolling Time Series Cross-Validation

Backtesting

Which Strategy to Use for Safe Forecasts?

Final Considerations on Model Evaluation

Part II: Univariate Time Series Models

Chapter 3: The AR Model

Autocorrelation: The Past Influences the Present

Compute Autocorrelation in Earthquake Counts

Positive and Negative Autocorrelation

Stationarity and the ADF Test

Differencing a Time Series

Lags in Autocorrelation

Partial Autocorrelation

How Many Lags to Include?

AR Model Definition

Estimating the AR Using Yule-Walker Equations

The Yule-Walker Method

Train-Test Evaluation and Tuning

Chapter 4: The MA Model

The Model Definition

Fitting the MA Model

Stationarity

Choosing Between an AR and an MA Model.

Application of the MA Model

Multistep Forecasting with Model Retraining

Grid Search to Find the Best MA Order

Chapter 5: The ARMA Model

The Idea Behind the ARMA Model

The Mathematical Definition of the ARMA Model

An Example: Predicting Sunspots Using ARMA

Fitting an ARMA(1,1) Model

More Model Evaluation KPIs

Automated Hyperparameter Tuning

Grid Search: Tuning for Predictive Performance

Chapter 6: The ARIMA Model

ARIMA Model Definition

Model Definition

ARIMA on the CO2 Example

Chapter 7: The SARIMA Model

Univariate Time Series Model Breakdown

The SARIMA Model Definition

Example: SARIMA on Walmart Sales

Part III: Multivariate Time Series Models

Chapter 8: The SARIMAX Model

Time Series Building Blocks

Supervised Models vs. SARIMAX

Example of SARIMAX on the Walmart Dataset

Chapter 9: The VAR Model

Order: Only One Hyperparameter

Estimation of the VAR Coefficients

One Multivariate Model vs. Multiple Univariate Models

An Example: VAR for Forecasting Walmart Sales

Chapter 10: The VARMAX Model

Multiple Time Series with Exogenous Variables

Part IV: Supervised Machine Learning Models

Chapter 11: The Linear Regression

The Idea Behind Linear Regression

Example: Linear Model to Forecast CO2 Levels

Chapter 12: The Decision Tree Model

Mathematics

Splitting

Pruning and Reducing Complexity

Example

Chapter 13: The kNN Model

Intuitive Explanation

Mathematical Definition of Nearest Neighbors

Combining k Neighbors into One Forecast

Deciding on the Number of Neighbors k.

Predicting Traffic Using kNN

Grid Search on kNN

Random Search: An Alternative to Grid Search

Chapter 14: The Random Forest

Intuitive Idea Behind Random Forests

Random Forest Concept 1: Ensemble Learning

Bagging Concept 1: Bootstrap

Bagging Concept 2: Aggregation

Random Forest Concept 2: Variable Subsets

Predicting Sunspots Using a Random Forest

Grid Search on the Two Main Hyperparameters of the Random Forest

Random Search CV Using Distributions

Distribution for max_features

Distribution for n_estimators

Fitting the RandomizedSearchCV

Interpretation of Random Forests: Feature Importance

Chapter 15: Gradient Boosting with XGBoost and LightGBM

Boosting: A Different Way of Ensemble Learning

The Gradient in Gradient Boosting

Gradient Boosting Algorithms

The Difference Between XGBoost and LightGBM

Forecasting Traffic Volume with XGBoost

Forecasting Traffic Volume with LightGBM

Hyperparameter Tuning Using Bayesian Optimization

The Theory of Bayesian Optimization

Bayesian Optimization Using scikit-optimize

Conclusion

Part V: Advanced Machine and Deep Learning Models

Chapter 16: Neural Networks

Fully Connected Neural Networks

Activation Functions

The Weights: Backpropagation

Optimizers

Learning Rate of the Optimizer

Hyperparameters at Play in Developing a NN

Introducing the Example Data

Specific Data Prep Needs for a NN

Scaling and Standardization

Principal Component Analysis (PCA)

The Neural Network Using Keras

Chapter 17: RNNs Using SimpleRNN and GRU

What Are RNNs: Architecture

Inside the SimpleRNN Unit

The Example

Predicting a Sequence Rather Than a Value

Univariate Model Rather Than Multivariable

Preparing the Data

A Simple SimpleRNN.

SimpleRNN with Hidden Layers

Simple GRU

GRU with Hidden Layers

Chapter 18: LSTM RNNs

What Is LSTM

The LSTM Cell

LSTM with One Layer of 8

LSTM with Three Layers of 64

Chapter 19: The Prophet Model

The Prophet Data Format

The Basic Prophet Model

Adding Monthly Seasonality to Prophet

Adding Holiday Data to Basic Prophet

Adding an Extra Regressor to Prophet

Tuning Hyperparameters Using Grid Search

Chapter 20: The DeepAR Model

About DeepAR

Model Training with DeepAR

Predictions with DeepAR

Probability Predictions with DeepAR

Adding Extra Regressors to DeepAR

Hyperparameters of the DeepAR

Benchmark and Conclusion

Chapter 21: Model Selection

Model Selection Based on Metrics

Model Structure and Inputs

One-Step Forecasts vs. Multistep Forecasts

Model Complexity vs. Gain

Model Complexity vs. Interpretability

Model Stability and Variation

Index.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9781484271506

1484271505

OCLC:

1259625412