Ultimate Enterprise Data Analysis and Forecasting using Python

Practical Approaches to Time Series Analysis and Forecasting using Python for Informed Decision-Making Book Description Embark on a transformative journey through the intricacies of time series analysis and forecasting with this comprehensive handbook. Beginning with the essential packages for data science and machine learning projects you will delve into Python's prowess for efficient time series data analysis, exploring the core components and real-world applications across various industries through compelling use-case studies. From understanding classical models like AR, MA, ARMA, and ARIMA to exploring advanced techniques such as exponential smoothing and ETS methods, this guide ensures a deep understanding of the subject. It will help you navigate the complexities of vector autoregression (VAR, VMA, VARMA) and elevate your skills with a deep dive into deep learning techniques for time series analysis. By the end of this book, you will be able to harness the capabilities of Azure Time Series Insights and explore the cutting-edge AWS Forecast components, unlocking the cloud's power for advanced and scalable time series forecasting. 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