Perform time series analysis and forecasting confidently with this
Python code bank and reference manual Key Features Explore
forecasting and anomaly detection techniques using statistical,
machine learning, and deep learning algorithms Learn different
techniques for evaluating, diagnosing, and optimizing your models
Work with a variety of complex data with trends, multiple seasonal
patterns, and irregularities Book DescriptionTime 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. What you will learn Understand what makes time
series data different from other data Apply various imputation and
interpolation strategies for missing data Implement different
models for univariate and multivariate time series Use different
deep learning libraries such as TensorFlow, Keras, and PyTorch Plot
interactive time series visualizations using hvPlot Explore
state-space models and the unobserved components model (UCM) Detect
anomalies using statistical and machine learning methods Forecast
complex time series with multiple seasonal patterns Who this book
is forThis book is for data analysts, business analysts, data
scientists, data engineers, or Python developers who want practical
Python recipes for time series analysis and forecasting techniques.
Fundamental knowledge of Python programming is required. Although
having a basic math and statistics background will be beneficial,
it is not necessary. Prior experience working with time series data
to solve business problems will also help you to better utilize and
apply the different recipes in this book.
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