Mastering Time Series Analysis and Forecasting with Python Bridging Theory and Practice Through Insights, Techniques, an

[book24h]

Free Download Mastering Time Series Analysis and Forecasting with Python
by Sulekha Aloorravi;
English | 2024 | ISBN: 8196815107 | 322 pages | True PDF | 40.26 MB

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 Descriptionly 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



Buy Premium From My Links To Get Resumable Support,Max Speed & Support Me

A kódrészlet megtekintéséhez be kell jelentkezned, vagy nincs jogosultságod a tartalom megtekintéséhez.

Links are Interchangeable - Single Extraction