Ultimate Enterprise Data Analysis and Forecasting using Python 9788119416448

Table of contents :
Cover Page
Title Page
Copyright Page
Dedication Page
About the Author
About the Technical Reviewers
Preface
Errata
Table of Contents
1. Introduction to Python and its key packages for DS and ML Projects
Introduction
Structure
Introduction to Python programming language
Key features of Python
Python programming IDEs and comparisons
Jupyter Notebook
PyCharm
Spyder
Installing Jupyter notebook
Python libraries
Pandas
Panel + Data = Pandas
Reshaping DataFrame
Combining DataFrame
Working with categorical data
Encoding
Date and time data
Converting data types
NumPy
Python statistics libraries
Working with various files in Python
Conclusion
Points to remember
2. Python for Time Series Data Analysis
Introduction
Structure
Data analysis and its benefits
Benefits
Advanced Analytics
Python - the best choice for data analytics
Time series data
Time series data management
Data lifecycle management (DLM)
Data acquisition or collection
Ingesting data
Transforming data
Storing data
Actionable information
Data remediation
Data cleansing and preparation
Handling missing and duplicate data
Handling uniform format
Handling categorical columns
Transformation of data
Handling time series data
Exploratory data analysis (EDA)
EDA for time series
Conclusion
Points to remember
3. Time Series Analysis and its Components
Introduction
Structure
Time series data analysis
Significance of time series data
Trend
Seasonality
Components of time series data
Stationarity versus non-stationarity
Augmented Dickey-Fuller (ADF)
Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test
Converting non-stationary data into stationary
Conclusion
Points to remember
4. Time Series Analysis and Forecasting Opportunities in Various Industries
Introduction
Structure
Opportunity and scope of TSA&F
Scope of price prediction
Scope of forecasting in healthcare for diagnosis
Scope of predictive maintenance
Challenges with TSA&F
Case studies
Price prediction in retail and use case
EDA and Stationarity Analysis
Forecasting in healthcare for diagnosis and use case
Predictive maintenance and anomaly detection use case
Conclusion
Points to remember
5. Exploring various aspects of Time Series Analysis and Forecasting
Introduction
Structure
Understanding time series analysis (TSA)
Statistical analysis
The measure of central tendency
Measure of variability
Boxplots
Histogram
Inferential Statistics
Regression analysis
Linear model
Hypothesis testing
Confidence intervals
Confidence intervals assessment at the speed of the motor
Study the shapes of Time Series
Transforms for TSA&F
Box-Cox transformation
Overview of Naive forecasting
Conclusion
Points to remember
6. Exploring Time Series Models - AR, MA, ARMA, and ARIMA
Introduction
Structure
Overview and time series models
Statistical models for TSA&F
Autoregressive (AR) Model
Autoregressive (AR) Implementation
Analysis of the P-value
Auto-regression model for TSA&F with Python
Moving Average (MA) Model
Moving Average (MA) Implementation
ARMA Model
Auto-Regressive Integrated Moving Average (ARIMA)
Time Series Analysis and Forecasting Process Workflow
Pertinency of the model
Conclusion
Points to remember
7. Understanding Exponential Smoothing and ETS Methods in TSA
Introduction
Structure
Understanding Exponential Smoothing
Exponential Smoothing implementation using Excel
Exponential Smoothing types
Triple Exponential Smoothing Model for TSA&F using Excel
SES and DES Model implementation using Python
Error - Trend-Seasonality (ETS)
Exponentially Weighted Moving Averages (EWMA)
Benefits of EWMA
Limitations of EWMA
EWMA Model for TSA&F with Excel (simple method)
EWMA Model implementation using Python
Conclusion
Points to remember
8. Exploring Vector Autoregression and its Subsets (VAR, VMA, and VARMA)
Introduction
Structure
Understanding Vector Autoregression
VAR implementation using Python
1. Analyze the time series data and its characteristics
2. Test for data stationarity using the ADF method
Augmented Dickey-Fuller (ADF) test
3. Train-test split
4. Re-run the ADF test
5. Apply the VAR algorithm
6. Optimal order (p) selection process
7. Analysis of Serial Correlation of Residuals [ScoR]
8. Building forecast VAR model
9. Model evaluation
Vector Autoregression Moving - Average (VARMA)
VARMA implementation using Python
Vector Autoregression Moving-Average with Exogenous Regressors (VARMAX)
VARMAX implementation using Python
Seasonal Autoregressive Integrated Moving-Average (SARIMA)
SARIMA implementation using Python
SARIMAX implementation using Python
Fractional-Autoregressive-Integrated-Moving Average model (FARIMA)
Conclusion
Points to remember
9. Deep Learning for Time Series Analysis and Forecasting
Introduction
Structure
Deep learning in time series analysis
Neural Networks
Artificial neural networks (ANN)
Long short-term memory (LSTM)
Convolutional neural networks (CNN)
Recurrent Neural Network (RNN)
Backpropagation through time (BPTT)
Conclusion
Points to remember
10. Azure Time Series Insights
Introduction
Structure
Prerequisites
Understanding Azure - Time Series Insights (Azure-TSI) Gen2 component
Components of Azure TSI and its major jobs
Azure TSI – versions (Gen 1 and Gen 2)
Azure TSI – Capabilities
Exploring Azure TSI Data Storage
High-level architecture of Azure TSI
Creating Azure IoT hub instance
Creating Azure TSI Gen2 environment
Exploring Azure TSI Explorer
Conclusion
Points to remember
11. AWS Forecast
Introduction
Structure
Prerequisites
Understanding Amazon Forecast Service (AFS)
Workflow for Amazon Forecast Service
Data Preparations
Dataset Guidelines for Forecast
Quality of Data
Importing data
Training data
Forecast creation and selection
Retrieve the Forecast
Orchestration of Amazon Forecast
Conclusion
Points to remember
Index