Mastering Time Series Analysis and Forecasting with Python
Bridging Theory and Practice Through Insights, Techniques, and Tools for Effective Time Series Analysis in Python
Sulekha Aloorravi
- English
- ePUB (disponibile sull'app)
- Disponibile su iOS e Android
Mastering Time Series Analysis and Forecasting with Python
Bridging Theory and Practice Through Insights, Techniques, and Tools for Effective Time Series Analysis in Python
Sulekha Aloorravi
Informazioni sul libro
Decode the language of time with Python. Discover powerful techniques to analyze, forecast, and innovate.
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.
Table of Contents1. Introduction to Time Series2. Overview of Time Series Libraries in Python3. Visualization of Time Series Data4. Exploratory Analysis of Time Series Data5. Feature Engineering on Time Series6. Time Series Forecasting – ML Approach Part 17. Time Series Forecasting – ML Approach Part 28. Time Series Forecasting - DL Approach9. Multivariate Time Series, Metrics, and Validation Index