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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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Detecting outliers using a modified z-score

In the Detecting outliers using a z-score recipe, you experienced how simple and intuitive the method is. But it has one major drawback: it assumes your data is normally distributed.

But, what if your data is not normally distributed? Luckily, there is a modified version of the z-score to work with non-normal data. The main difference between the regular z-score and the modified z-score is that we replace the mean with the median:

Where (tilde x) is the median of the dataset, and MAD is the median absolute deviation of the dataset:

The 0.6745 value is the standard deviation unit that corresponds to the 75th percentile (Q3) in a Gaussian distribution and is used as a normalization factor. In other words, it is used to approximate the standard deviation. This way, the units you obtain from this method are measured in standard deviation, similar to how you would interpret the regular...

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