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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 LSTM using Keras

There are a few shortcomings in using RNNs – for example, an RNN's memory is short term and does not do well when persisting a longer-term memory.

In the previous recipe, you trained a small RNN architecture with one hidden layer. In a deep RNN, with multiple hidden layers, the network will suffer from the vanishing gradient problem – that is, during backpropagation, as the weights get adjusted, it will be unable to change the weights of much earlier layers, reducing its ability to learn. Because of this, the output becomes influenced by the closer layers (nodes).

In other words, any memory of earlier layers decays through time, hence the term vanishing. This is an issue if you have a very long sequence – for example, a long paragraph or long sentence – and you want to predict the next word. In time series data, how problematic the lack of long-term memory is will vary, depending on your goal and the data you...

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