Learn the concepts of time series from traditional to bleeding-edge
techniques. This book uses comprehensive examples to clearly
illustrate statistical approaches and methods of analyzing time
series data and its utilization in the real world. All the code is
available in Jupyter notebooks. You'll begin by reviewing time
series fundamentals, the structure of time series data,
pre-processing, and how to craft the features through data
wrangling. Next, you'll look at traditional time series techniques
like ARMA, SARIMAX, VAR, and VARMA using trending framework like
StatsModels and pmdarima. The book also explains building
classification models using sktime, and covers advanced deep
learning-based techniques like ANN, CNN, RNN, LSTM, GRU and
Autoencoder to solve time series problem using Tensorflow. It
concludes by explaining the popular framework fbprophet for
modeling time series analysis. After reading Hands -On Time Series
Analysis with Python, you'll be able to apply these new techniques
in industries, such as oil and gas, robotics, manufacturing,
government, banking, retail, healthcare, and more. What You'll
Learn:* Explains basics to advanced concepts of time series * How
to design, develop, train, and validate time-series methodologies *
What are smoothing, ARMA, ARIMA, SARIMA,SRIMAX, VAR, VARMA
techniques in time series and how to optimally tune parameters to
yield best results * Learn how to leverage bleeding-edge techniques
such as ANN, CNN, RNN, LSTM, GRU, Autoencoder to solve both
Univariate and multivariate problems by using two types of data
preparation methods for time series. * Univariate and multivariate
problem solving using fbprophet. Who This Book Is For Data
scientists, data analysts, financial analysts, and stock market
researchers
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