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Time Series Analysis with Python Cookbook

Time Series Analysis with Python Cookbook

By : Tarek A. Atwan
4.8 (11)
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Time Series Analysis with Python Cookbook

Time Series Analysis with Python Cookbook

4.8 (11)
By: Tarek A. Atwan

Overview of this book

Time series data is everywhere, available at a high frequency and volume. It is complex and can contain noise, irregularities, and multiple patterns, making it crucial to be well-versed with the techniques covered in this book for data preparation, analysis, and forecasting. This book covers practical techniques for working with time series data, starting with ingesting time series data from various sources and formats, whether in private cloud storage, relational databases, non-relational databases, or specialized time series databases such as InfluxDB. Next, you’ll learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods, followed by more advanced unsupervised ML models. The book will also explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR. The recipes will present practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Later, you’ll work with ML and DL models using TensorFlow and PyTorch. Finally, you’ll learn how to evaluate, compare, optimize models, and more using the recipes covered in the book.
Table of Contents (18 chapters)
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Forecasting with a GRU using PyTorch

In this recipe, you will use the same train_model_pt function from the previous Forecasting with an RNN using PyTorch recipe. The function trains the model, captures loss function scores, evaluates the model, makes a forecast using the test set, and finally, produces plots for further evaluation.

You will still need to define a new class for the GRU model.

How to do it...

  1. Create a GRU class that will inherit from Module class. The setup will be similar to the RNN class, but unlike the LSTM class, you only handle one state (the hidden state):
    class GRU(nn.Module):
        def __init__(self, input_size, output_size, n_features, n_layers):
            super(GRU, self).__init__()
            self.n_layers = n_layers
            self.hidden_dim = n_features
            self.gru...

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