پیش‌بینی پیشرفته با پایتون: با مدل‌های پیشرفته شامل LSTMها، پروفیت فیسبوک و DeepAR آمازون

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. 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 Key Takeaways 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 Key Takeaways 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 Key Takeaways 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 Key Takeaways Chapter 6: The ARIMA Model ARIMA Model Definition Model Definition ARIMA on the CO2 Example Key Takeaways Chapter 7: The SARIMA Model Univariate Time Series Model Breakdown The SARIMA Model Definition Example: SARIMA on Walmart Sales Key Takeaways Part III: Multivariate Time Series Models Chapter 8: The SARIMAX Model Time Series Building Blocks Model Definition Supervised Models vs. SARIMAX Example of SARIMAX on the Walmart Dataset Key Takeaways Chapter 9: The VAR Model The Model Definition Order: Only One Hyperparameter Stationarity Estimation of the VAR Coefficients One Multivariate Model vs. Multiple Univariate Models An Example: VAR for Forecasting Walmart Sales Key Takeaways Chapter 10: The VARMAX Model Model Definition Multiple Time Series with Exogenous Variables Key Takeaways Part IV: Supervised Machine Learning Models Chapter 11: The Linear Regression The Idea Behind Linear Regression Model Definition Example: Linear Model to Forecast CO2 Levels Key Takeaways Chapter 12: The Decision Tree Model Mathematics Splitting Pruning and Reducing Complexity Example Key Takeaways 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 Key Takeaways 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 Key Takeaways 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 Key Takeaways 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 Conclusion Key Takeaways 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 Key Takeaways Chapter 18: LSTM RNNs What Is LSTM The LSTM Cell Example LSTM with One Layer of 8 LSTM with Three Layers of 64 Conclusion Key Takeaways Chapter 19: The Prophet Model The Example 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 Key Takeaways 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 Key Takeaways 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 Conclusion Key Takeaways Index