Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python 1st Edition

Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python 1st Edition – Ebook Instant Download/Delivery ISBN(s): 9781484289778,1484289773,9781484289785, 1484289781

Product details:

• ISBN-10: 1484289781
• ISBN-13: 9781484289785
• Author: Akshay R Kulkarni; Adarsha Shivananda; Anoosh Kulkarni; V Adithya Krishnan

This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average). Next, you’ll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You’ll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations.

Table contents:

1. Getting Started with Time Series
2. Statistical Univariate Modeling
3. Advanced Univariate and Statistical Multivariate Modeling
4. Machine Learning Regression–based Forecasting
5. Deep Learning–based Time Series Forecasting

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