Mastering Time Series Analysis and Forecasting with Python
Language: English
Published by Orange Education Pvt Ltd, 2024
Series: Book 1 of 1 - Predictive Analytics Specialist (Python) — Focused Path
- Softcover
- New
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
AbeBooks seller since January 19, 2007
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- Title
- Mastering Time Series Analysis and Forecasting with Python
- Author
- Aloorravi, Sulekha; AVA, Orange
- Publisher
- Orange Education Pvt Ltd
- Publication year
- 2024
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 8196815107
- ISBN 13
- 9788196815103
- Series
- Book 1 of 1: Predictive Analytics Specialist (Python) — Focused Path
Unlock expert guidance for mastering time series analysis and forecasting with Python. This book walks you through forecasting with Python using practical solutions for machine learning forecasting and deep learning forecasting—from building ARIMA, LSTM, and CNN models to applying feature engineering for time series data.
Book Description
"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.
What You’ll Learn Inside:
- Step-by-step time series modeling in Python using multivariate and multiple time series forecasting projects
- Clear coverage of predictive modeling Python for real-world datasets, business applications, and anomaly detection
- Effective data visualization and exploratory analysis for time series to spot trends and patterns
- Comprehensive methods for model evaluation and validation for time series predictions and accuracy
- Hands-on time series forecasting projects using Python for business impact and actionable insights
- Advanced approaches for ARIMA, LSTM, and CNN time series models
- Explanation of anomaly detection and pattern recognition in time series using modern Python techniques
- Data analysts, students, and professionals wanting a comprehensive Python time series book for forecasting and data analysis
- Anyone interested in feature engineering Python, business forecasting, and working with real-world data analysis
- Teams needing practical guidance in multivariate time series modeling and advanced predictive analytics
- Authentic, accurate, and policy-compliant content—no unauthorized names, brands, or characters
- Content naturally optimized for Amazon search using established keywords for best ranking and conversion
- Easy-to-read, practical tone for learners and professionals at any level
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
"Synopsis" may belong to another edition of this title.
Majestic Books
Hounslow, United Kingdom
AbeBooks seller since January 19, 2007
Shipping rates from United Kingdom to U.S.A.
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