- eBook:Hands-on Time Series Analysis with Python: From Basics to Bleeding Edge Techniques
- Author:B V Vishwas, ASHISH PATEL
- Data:September 9, 2020
- Pages:424 pages
- Format:PDF, ePUB
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, test 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 prepration methods for time series.
- Univariate and multivariate problem solving using fbprophet.
Data scientists, data analysts, financial analysts, and stock market researchers
Chapter 2: Data Wrangling and Preparation for Time Series
Chapter 3: Smoothing Methods
Chapter 4: Regression Extension Techniques for Time-Series Data
Chapter 5: Bleeding-Edge Techniques
Chapter 6: Bleeding-Edge Techniques for Univariate Time Series
Chapter 7: Bleeding-Edge Techniques for Multivariate Time Series
Chapter 8: Prophet