Mastering Time Series Analysis and Forecasting with Python: Bridging Theory and Practice Through Insights, Techniques, and Tools for Effective Time . Analytics Specialist (Python) — Focused Path)

Aloorravi, Sulekha; AVA, Orange

ISBN 10: 8196815107 / ISBN 13: 9788196815103

Edité par Orange Education Pvt Ltd, 2024

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N° de réf. du vendeur ria9788196815103_new

Détails bibliographiques

• Titre: Mastering Time Series Analysis and Forecasting with Python: Bridging Theory and Practice Through Insights, Techniques, and Tools for Effective Time . Analytics Specialist (Python) ? Focused Path)
• Éditeur: Orange Education Pvt Ltd
• Date de parution: 2024
• Langue: anglais
• ISBN à 10 chiffres: 8196815107
• ISBN à 13 chiffres: 9788196815103
• Reliure: Couverture souple
• État de l'article: New

A propos de ce titre

Synopsis

Decode the language of time with Python. Discover powerful techniques to analyze, forecast, and innovate.

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

Who Should Read This Book?

• 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

Why Choose This Guide?

• 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

Start mastering time series forecasting and data analysis with Python—achieve reliable predictions for real-world impact today!

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