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.

Osoby które kupowały "Time Series Analysis with Python Cookbook", wybierały także:

• Python dla ka 199,00 zÅ‚, (59,70 zÅ‚ -70%)
• Algorytmy i struktury danych. Kurs video. Java, JavaScript, Python 89,00 zÅ‚, (26,70 zÅ‚ -70%)
• Data Science w Pythonie. Kurs video. Algorytmy uczenia maszynowego 179,00 zÅ‚, (53,70 zÅ‚ -70%)
• Machine Learning i j 199,00 zÅ‚, (59,70 zÅ‚ -70%)
• Data Science w Pythonie. Kurs video. Przetwarzanie i analiza danych 149,00 zÅ‚, (44,70 zÅ‚ -70%)

Spis treści