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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 univariate time series data with non-seasonal ARIMA

In this recipe, you will explore non-seasonal ARIMA and use the implementation in the statsmodels package. ARIMA stands for Autoregressive Integrated Moving Average, which combines three main components: the autoregressive or AR(p) model, the moving average or MA(q) model, and an integrated (differencing) factor or I(d).

An ARIMA model can be defined by the p, d, and q parameters, so for a non-seasonal time series, it is described as ARIMA(p, d, q). The p and q parameters are called orders; for example, in AR of order p and MA of order q. They can also be called lags since they represent the number of periods we need to lag for. You may also come across another reference for p and q, namely polynomial degree.

ARIMA models can handle non-stationary time series data through differencing, a time series transformation technique, to make a non-stationary time series stationary. The integration or order of differencing...

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