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

ISBN-13: 9781484289778 / Angielski / Miękka / 2022 / 174 str.

Akshay Kulkarni; Adarsha Shivananda; Anoosh Kulkarni

Opis

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.

 

After finishing this book, you will have a foundational understanding of various concepts relating to time series and its implementation in Python.

 

What You Will Learn

• Implement various techniques in time series analysis using Python.
• Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average),  ARMA (autoregressive moving-average) and ARIMA (autoregressive  integrated moving-average) for time series forecasting 
• Understand univariate and multivariate modeling for time series forecasting
• Forecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory)

 

Who This Book Is For

Data Scientists, Machine Learning Engineers, and software developers interested in time series analysis.

Spis treści

Chapter 1: Getting Started with Time Series.

Chapter Goal: Exploring and analyzing the timeseries data, and preprocessing it, which includes feature engineering for model building.

No of pages: 25

Sub - Topics

1 Reading time series data

2 Data cleaning

3 EDA

4 Trend

5 Noise

6 Seasonality

7 Cyclicity

8 Feature Engineering

9 Stationarity

Chapter 2: Statistical Univariate Modelling

Chapter Goal: The fundamentals of time series forecasting with the use of statistical modelling methods like AR, MA, ARMA, ARIMA, etc.

 No of pages: 25

Sub - Topics

1 AR

2 MA

3 ARMA

4 ARIMA

5 SARIMA

6 AUTO ARIMA

7 FBProphet

Chapter 3: Statistical Multivariate Modelling

Chapter Goal: implementing multivariate modelling techniques like HoltsWinter and SARIMAX.

No of pages: 25

Sub - Topics: 

1 HoltsWinter 

2 ARIMAX

3 SARIMAX

Chapter 4: Machine Learning Regression-Based Forecasting.

Chapter Goal: Building and comparing multiple classical ML Regression algorithms for timeseries forecasting.

No of pages: 25

Sub - Topics: 

1 Random Forest

2 Decision Tree

3 Light GBM

4 XGBoost

5 SVM

Chapter 5: Forecasting Using Deep Learning.

Chapter Goal: Implementing advanced concepts like deep learning for time series forecasting from scratch.

No of pages: 25

Sub - Topics: 

1 LSTM 

2 ANN

3 MLP

O autorze

Akshay Kulkarni is an AI and machine learning (ML) evangelist and a thought leader. He has consulted several Fortune 500 and global enterprises to drive AI and data science-led strategic transformations. He has been honoured as Google Developer Expert, and is an Author and a regular speaker at top AI and data science conferences (including Strata, O’Reilly AI Conf, and GIDS). He is a visiting faculty member for some of the top graduate institutes in India. In 2019, he has been also featured as one of the top 40 under 40 Data Scientists in India. In his spare time, he enjoys reading, writing, coding, and helping aspiring data scientists. He lives in Bangalore with his family.

Adarsha Shivananda is a Data science and MLOps Leader. He is working on creating worldclass MLOps capabilities to ensure continuous value delivery from AI. He aims to build a pool of exceptional data scientists within and outside of the organization to solve problems through training programs, and always wants to stay ahead of the curve. He has worked extensively in the pharma, healthcare, CPG, retail, and marketing domains. He lives in Bangalore and loves to read and teach data science.

Anoosh Kulkarni is a data scientist and a Senior AI consultant. He has worked with global clients across multiple domains and helped them solve their business problems using machine learning (ML), natural language processing (NLP), and deep learning.. Anoosh is passionate about guiding and mentoring people in their data science journey. He leads data science/machine learning meet-ups  and helps aspiring data scientists navigate their careers. He also conducts ML/AI workshops at universities and is actively involved in conducting webinars, talks, and sessions on AI and data science. He lives in Bangalore with his family.

V Adithya Krishnan is a data scientist and ML Ops Engineer. He has worked with various global clients across multiple domains and helped them to solve their business problems extensively using advanced Machine learning (ML) applications. He has experience across multiple fields of AI-ML, including, Time-series forecasting, Deep Learning, NLP, ML Operations, Image processing, and data analytics. Presently, he is working on  a state-of-the-art value observability suite for models in production, which includes continuous model and data monitoring along with the business value realized. He also published a paper at an IEEE conference, “Deep Learning Based Approach for Range Estimation," written in collaboration with the DRDO. He lives in Chennai with his family.

Blurb

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.

 

After finishing this book, you will have a foundational understanding of various concepts relating to time series and its implementation in Python.

 

You will:

• Implement various techniques in time series analysis using Python.
• Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average),  ARMA (autoregressive moving-average) and ARIMA (autoregressive  integrated moving-average) for time series forecasting 
• Understand univariate and multivariate modeling for time series forecasting
• Forecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory)