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دانلود کتاب Deep Learning for Time Series Data Cookbook
دانلود کتاب کتاب آشپزی Deep Learning for Time Series Data
مشخصات کتاب
Deep Learning for Time Series Data Cookbook
ویرایش:
نویسندگان: Vitor Cerqueira. Luís Roque
سری:
ISBN (شابک) : 9781805129233
ناشر: Packt Publishing
سال نشر: 2024
تعداد صفحات: 274
زبان: English
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 17 مگابایت
قیمت کتاب (تومان) : 76,000
در صورت تبدیل فایل کتاب Deep Learning for Time Series Data Cookbook به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب کتاب آشپزی Deep Learning for Time Series Data نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
توضیحاتی درمورد کتاب به خارجی
فهرست مطالب
Cover Title Page Copyright and Credits Contributors Table of Contents Preface Chapter 1: Getting Started with Time Series Technical requirements Loading a time series using pandas Getting ready How to do it… How it works… Visualizing a time series Getting ready How to do it… How it works… There’s more… Resampling a time series Getting ready How to do it… How it works… There’s more… Dealing with missing values Getting ready How to do it… How it works… There’s more… Decomposing a time series Getting ready How to do it… How it works… There’s more… See also Computing autocorrelation Getting ready How to do it… How it works… There’s more… Detecting stationarity Getting ready How to do it… How it works… There’s more… Dealing with heteroskedasticity Getting ready How to do it… How it works… There’s more… See also Loading and visualizing a multivariate time series Getting ready How to do it… How it works… Resampling a multivariate time series Getting ready How to do it… How it works… Analyzing correlation among pairs of variables Getting ready How to do it… How it works… Chapter 2: Getting Started with PyTorch Technical requirements Installing PyTorch Getting ready How to do it… How it works… Basic operations in PyTorch Getting ready How to do it… How it works… Advanced operations in PyTorch Getting ready How to do it… How it works… Building a simple neural network with PyTorch Getting ready How to do it… There’s more… Training a feedforward neural network Getting ready How to do it… How it works… Training a recurrent neural network Getting ready How to do it… How it works… Training an LSTM neural network Getting ready How to do it… How it works… Training a convolutional neural network Getting ready How to do it… How it works… Chapter 3: Univariate Time Series Forecasting Technical requirements Building simple forecasting models Getting ready How to do it… How it works… There’s more… Univariate forecasting with ARIMA Getting ready How to do it… How it works… There’s more… Preparing a time series for supervised learning Getting ready How to do it… How it works… There’s more… Univariate forecasting with a feedforward neural network Getting ready How to do it… How it works… There’s more… Univariate forecasting with an LSTM Getting ready How to do it… How it works… There’s more… Univariate forecasting with a GRU Getting ready How to do it… How it works… There’s more… Univariate forecasting with a Stacked LSTM Getting ready How to do it… How it works… Combining an LSTM with multiple fully connected layers Getting ready How to do it… How it works… There’s more… Univariate forecasting with a CNN Getting ready How to do it… How it works… There’s more… Handling trend – taking first differences Getting ready How to do it… How it works… There’s more… Handling seasonality – seasonal dummies and Fourier series Getting ready How to do it… How it works… There’s more… Handling seasonality – seasonal differencing Getting ready How to do it… How it works… Handling seasonality – seasonal decomposition Getting ready How to do it… How it works… Handling non-constant variance – log transformation Getting ready How to do it… How it works… Chapter 4: Forecasting with PyTorch Lightning Technical requirements Preparing a multivariate time series for supervised learning Getting ready How to do it… How it works… Training a linear regression model for forecasting with a multivariate time series Getting ready How to do it… How it works… Feedforward neural networks for multivariate time series forecasting Getting ready How to do it… How it works… There’s more… LSTM neural networks for multivariate time series forecasting Getting ready How to do it… How it works… There’s more… Monitoring the training process using Tensorboard Getting ready How to do it… How it works… There’s more… Evaluating deep neural networks for forecasting Getting ready How to do it… How it works… There’s more… Using callbacks – EarlyStopping Getting ready How to do it… How it works… There’s more… Chapter 5: Global Forecasting Models Technical requirements Multi-step forecasting with multivariate time series Getting ready How to do it… How it works… There’s more… Multi-step and multi-output forecasting with multivariate time series Getting ready How to do it… How it works… Preparing multiple time series for a global model Getting ready How to do it… How it works… Training a global LSTM with multiple time series Getting ready How to do it… How it works… Global forecasting models for seasonal time series Getting ready How to do it… How it works… There’s more… Hyperparameter optimization using Ray Tune Getting ready How to do it… How it works… There’s more… Chapter 6: Advanced Deep Learning Architectures for Time Series Forecasting Technical requirements Interpretable forecasting with N-BEATS Getting ready How to do it… How it works… There’s more… Optimizing the learning rate with PyTorch Forecasting Getting ready How to do it… How it works… There’s more… Getting started with GluonTS Getting ready How to do it… How it works… Training a DeepAR model with GluonTS Getting ready How to do it… How it works… There’s more… Training a Transformer model with NeuralForecast Getting ready How to do it… How it works… There’s more… Training a Temporal Fusion Transformer with GluonTS Getting ready How to do it… How it works… There’s more… Training an Informer model with NeuralForecast Getting ready How to do it… How it works… There’s more… Comparing different Transformers with NeuralForecast Getting ready How to do it… How it works… Chapter 7: Probabilistic Time Series Forecasting Technical requirements Introduction to exceedance probability forecasting Getting ready How to do it… How it works… There’s more… Exceedance probability forecasting with an LSTM Getting ready How to do it… How it works… There’s more… Creating prediction intervals using conformal prediction Getting ready How to do it… How it works… Probabilistic forecasting with an LSTM Getting ready How to do it… How it works… Probabilistic forecasting with DeepAR Getting ready How to do it… How it works… Introduction to Gaussian Processes Getting ready How to do it… How it works… Using Prophet for probabilistic forecasting Getting ready How to do it… How it works… There’s more… Chapter 8: Deep Learning for Time Series Classification Technical requirements Tackling TSC with K-nearest neighbors Getting ready How to do it… How it works… There’s more… Building a DataModule class for TSC Getting ready How to do it… How it works… Convolutional neural networks for TSC Getting ready How to do it… How it works… ResNets for TSC Getting ready How to do it… How it works… Tackling TSC problems with sktime Getting ready How to do it… How it works… There’s more… Chapter 9: Deep Learning for Time Series Anomaly Detection Technical requirements Time series anomaly detection with ARIMA Getting ready How to do it… How it works… There’s more… Prediction-based anomaly detection using DL Getting ready How to do it… How it works… There’s more… Anomaly detection using an LSTM AE Getting ready How to do it… How it works… Building an AE using PyOD Getting ready How to do it… How it works… There’s more… Creating a VAE for time series anomaly detection Getting ready How to do it… How it works… There’s more… Using GANs for time series anomaly detection Getting ready… How to do it… How it works… There’s more… Index Other Books You May Enjoy
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