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Advanced Forecasting with Python : With State-Of-the-Art-Models Including LSTMs, Facebook's Prophet, and Amazon's DeepAR
| Advanced Forecasting with Python : With State-Of-the-Art-Models Including LSTMs, Facebook's Prophet, and Amazon's DeepAR |
| Autore | Korstanje Joos |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress L. P., , 2021 |
| Descrizione fisica | 1 online resource (294 pages) |
| Disciplina | 006.31 |
| Soggetto non controllato | Science |
| ISBN | 1-4842-7150-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 -- 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. |
| Altri titoli varianti | Advanced Forecasting with Python |
| Record Nr. | UNINA-9910488729003321 |
Korstanje Joos
|
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| Berkeley, CA : , : Apress L. P., , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine learning on geographical data using Python : introduction into geodata with applications and use cases / / Joos Korstanje
| Machine learning on geographical data using Python : introduction into geodata with applications and use cases / / Joos Korstanje |
| Autore | Korstanje Joos |
| Pubbl/distr/stampa | New York, New York : , : Apress, , [2022] |
| Descrizione fisica | 1 online resource (314 pages) |
| Disciplina | 005.133 |
| Soggetto topico |
Machine learning
Python (Computer program language) Geodatabases |
| ISBN | 1-4842-8287-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Introduction -- Part I: General Introduction -- Chapter 1: Introduction to Geodata -- Reading Guide for This Book -- Geodata Definitions -- Cartesian Coordinates -- Polar Coordinates and Degrees -- The Difference with Reality -- Geographic Information Systems and Common Tools -- What Are Geographic Information Systems -- ArcGIS -- QGIS and Other Open Source ArcGIS Alternatives -- Python/R Programming -- Standard Formats of Geodata -- Shapefile -- Google KML File -- GeoJSON -- TIFF/JPEG/PNG -- CSV/TXT/Excel -- Overview of Python Tools for Geodata -- Key Takeaways -- Chapter 2: Coordinate Systems and Projections -- Coordinate Systems -- Geographic Coordinate Systems -- Latitude and Longitude -- WGS 1984 Geographic Coordinate System -- Other Geographic Coordinate Systems -- Projected Coordinate Systems -- X and Y Coordinates -- Four Types of Projected Coordinate Systems -- Equal Area Projections -- Example 1: Mollweide Projection -- Example 2: Albers Equal Area Conic -- Conformal Projections -- Mercator -- Lambert Conformal Conic -- Equidistant Projections -- Azimuthal Equidistant Projection -- Equidistant Conic Projection -- True Direction or Azimuthal Projections -- Lambert Equal Area Azimuthal -- Two-Point Equidistant Projection -- Local Coordinate Systems -- Which Coordinate System to Choose -- Playing Around with Some Maps -- Example: Working with Own Data -- Step 1: Make Your Own Dataset on Google My Maps -- Step 2: Add Some Features on Your Map -- Step 3: Export Your Map As a .KML -- Step 4: Import the .KML in Python -- Step 5: Plot the Map -- Step 6: Change the Coordinate System -- Step 7: Plot the Map Again -- Key Takeaways -- Chapter 3: Geodata Data Types -- Vector vs. Raster Data -- Dealing with Attributes in Vector and Raster -- Points.
Definition of a Point -- Importing an Example Point Dataset in Python -- Some Basic Operations with Points -- Filter Morning vs. Afternoon -- Lines -- Definition of a Line -- An Example Line Dataset in Python -- Polygons -- Definition of a Polygon -- An Example Polygon Dataset in Python -- Some Simple Operations with Polygons -- Rasters/Grids -- Definition of a Grid or Raster -- Importing a Raster Dataset in Python -- Key Takeaways -- Chapter 4: Creating Maps -- Mapping Using Geopandas and Matplotlib -- Getting a Dataset into Python -- Making a Basic Plot -- Plot Title -- Plot Legend -- Mapping a Point Dataset with Geopandas and Matplotlib -- Concluding on Mapping with Geopandas and Matplotlib -- Making a Map with Cartopy -- Concluding on Mapping with Cartopy -- Making a Map with Plotly -- Concluding on Mapping with Plotly -- Making a Map with Folium -- Concluding on Mapping with Folium -- Key Takeaways -- Part II: GIS Operations -- Chapter 5: Clipping and Intersecting -- What Is Clipping? -- A Schematic Example of Clipping -- What Happens in Practice When Clipping? -- Clipping in Python -- What Is Intersecting? -- What Happens in Practice When Intersecting? -- Conceptual Examples of Intersecting Geodata -- Intersecting in Python -- Difference Between Clipping and Intersecting -- Key Takeaways -- Chapter 6: Buffers -- What Are Buffers? -- A Schematic Example of Buffering -- What Happens in Practice When Buffering? -- Buffers for Point Data -- Buffers for Line Data -- Buffers for Polygon Data -- Creating Buffers in Python -- Creating Buffers Around Points in Python -- Creating Buffers Around Lines in Python -- Creating Buffers Around Polygons in Python -- Combining Buffers and Set Operations -- Key Takeaways -- Chapter 7: Merge and Dissolve -- The Merge Operation -- What Is a Merge? -- A Schematic Example of Merging -- Different Definitions of Merging. Merging in Python -- Row-Wise Merging in Python -- Attribute Join in Python -- Spatial Join in Python -- The Dissolve Operation -- What Is the Dissolve Operation? -- Schematic Overview of the Dissolve Operation -- The Dissolve Operation in Python -- Key Takeaways -- Chapter 8: Erase -- The Erase Operation -- Schematic Overview of Spatially Erasing Points -- Schematic Overview of Spatially Erasing Lines -- Schematic Overview of Spatially Erasing Polygons -- Erase vs. Other Operations -- Erase vs. Deleting a Feature -- Erase vs. Clip -- Erase vs. Overlay -- Erasing in Python -- Erasing Portugal from Iberia to Obtain Spain -- Erasing Points in Portugal from the Dataset -- Cutting Lines to Be Only in Spain -- Key Takeaways -- Part III: Machine Learning and Mathematics -- Chapter 9: Interpolation -- What Is Interpolation? -- Different Types of Interpolation -- Linear Interpolation -- Polynomial Interpolation -- Piecewise Polynomial or Spline -- Nearest Neighbor Interpolation -- From One-Dimensional to Spatial Interpolation -- Spatial Interpolation in Python -- Linear Interpolation Using Scipy Interp2d -- Kriging -- Linear Ordinary Kriging -- Gaussian Ordinary Kriging -- Exponential Ordinary Kriging -- Conclusion on Interpolation Methods -- Key Takeaways -- Chapter 10: Classification -- Quick Intro to Machine Learning -- Quick Intro to Classification -- Spatial Classification Use Case -- Feature Engineering with Additional Data -- Importing and Inspecting the Data -- Spatial Operations for Feature Engineering -- Reorganizing and Standardizing the Data -- Modeling -- Model Benchmarking -- Key Takeaways -- Chapter 11: Regression -- Introduction to Regression -- Spatial Regression Use Case -- Importing and Preparing Data -- Iteration 1 of Data Exploration -- Iteration 1 of the Model -- Interpretation of Iteration 1 Model -- Iteration 2 of Data Exploration. Iteration 2 of the Model -- Iteration 3 of the Model -- Iteration 4 of the Model -- Interpretation of Iteration 4 Model -- Key Takeaways -- Chapter 12: Clustering -- Introduction to Unsupervised Modeling -- Introduction to Clustering -- Different Clustering Models -- Spatial Clustering Use Case -- Importing and Inspecting the Data -- Cluster Model for One Person -- Tuning the Clustering Model -- Applying the Model to All Data -- Key Takeaways -- Chapter 13: Conclusion -- What You Should Remember from This Book -- Recap of Chapter 1 - Introduction to Geodata -- Recap of Chapter 2 - Coordinate Systems and Projections -- Recap of Chapter 3 - Geodata Data Types -- Recap of Chapter 4 - Creating Maps -- Recap of Chapter 5 - Clipping and Intersecting -- Recap of Chapter 6 - Buffers -- Recap of Chapter 7 - Merge and Dissolve -- Recap of Chapter 8 - Erase -- Recap of Chapter 9 - Interpolation -- Recap of Chapter 10 - Classification -- Recap of Chapter 11 - Regression -- Recap of Chapter 12 - Clustering -- Further Learning Path -- Going into Specialized GIS -- Specializing in Machine Learning -- Remote Sensing and Image Treatment -- Other Specialties -- Key Takeaways -- Index. |
| Record Nr. | UNINA-9910585767903321 |
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Korstanje Joos
|
||
| New York, New York : , : Apress, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||