Mastering Time Series Analysis and Forecasting with Python: Bridging Theory and Practice Through Insights, Techniques, and Tools for Effective Time Series Analysis in Python (English Edition)
Author: Sulekha Aloorravi (Author)
Publisher finelybook 出版社: Orange Education Pvt Ltd
Publication Date 出版日期: 2024-03-26
Language 语言: English
Print Length 页数: 321 pages
ISBN-10: 8196815107
ISBN-13: 9788196815103
Book Description
By finelybook
Decode the language of time with Python. Discover powerful techniques to analyze, forecast, and innovate.
Book Description
By finelybook
“Mastering Time Series Analysis and Forecasting with Python” is an essential handbook tailored for those seeking to harness the power of time series data in their work.
The book begins with foundational concepts and seamlessly guides readers through Python libraries such as Pandas, NumPy, and Plotly for effective data manipulation, visualization, and exploration. Offering pragmatic insights, it enables adept visualization, pattern recognition, and anomaly detection.
Advanced discussions cover feature engineering and a spectrum of forecasting methodologies, including machine learning and deep learning techniques such as ARIMA, LSTM, and CNN. Additionally, the book covers multivariate and multiple time series forecasting, providing readers with a comprehensive understanding of advanced modeling techniques and their applications across diverse domains.
Readers develop expertise in crafting precise predictive models and addressing real-world complexities. Complete with illustrative examples, code snippets, and hands-on exercises, this manual empowers readers to excel, make informed decisions, and derive optimal value from time series data.
Table of Contents
1. Introduction to Time Series
2. Overview of Time Series Libraries in Python
3. Visualization of Time Series Data
4. Exploratory Analysis of Time Series Data
5. Feature Engineering on Time Series
6. Time Series Forecasting – ML Approach Part 1
7. Time Series Forecasting – ML Approach Part 2
8. Time Series Forecasting – DL Approach
9. Multivariate Time Series, Metrics, and Validation
Index
