 Название: Mastering Time Series Analysis and Forecasting with Python: Bridging Theory and Practice Through Insights, Techniques, and Tools for Effective Time Series Analysis in Python
Автор: Sulekha Aloorravi
Издательство: Orange Education Pvt Ltd, AVA
Год: 2024
Страниц: 310
Язык: английский
Формат: epub (true)
Размер: 10.1 MB
Decode the language of time with Python. Discover powerful techniques to analyze, forecast, and innovate.
"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.
The field of time series analysis encompasses numerous methods, algorithms, and techniques tailored to uncover patterns, trends, and insights within sequential data. Through a hands-on, practical approach, we delve into the fundamental concepts of time series analysis, explore state-of-the-art methodologies, and provide step-by-step tutorials to implement these techniques using Python libraries such as Pandas, NumPy, Matplotlib, Statsmodels, and more.
As you journey through the pages of this book, you will learn how to visualize time series data, extract meaningful features, build predictive models, and evaluate their performance. From classical methods like ARIMA and exponential smoothing to modern approaches like machine learning and deep learning, we cover a diverse array of techniques to suit various data scenarios and business requirements.
Each chapter is crafted to provide both theoretical foundations and practical applications, ensuring that you not only understand the underlying principles but also gain the skills to apply them effectively in real-world projects. Along the way, you will encounter Python code examples, illustrative plots, and hands-on exercises to reinforce your learning and deepen your understanding.
This book comprises 9 chapters, each a complete module in itself, serving as your comprehensive guide to mastering time series in Python. Whether you are analyzing financial data, forecasting sales, predicting demand, or studying sensor readings, the techniques presented in this book will equip you with the tools and knowledge to tackle a wide range of time series challenges. I invite you to embark on this journey with me and discover the fascinating world of time series analysis with Python.
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.
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
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