Predictive Analytics & Time Series Forecasting: Master Data-driven Forecasting

Introduction:

Predictive analytics and time series forecasting are essential tools for organizations seeking to anticipate future trends and make data-driven decisions. This course on Predictive Analytics & Time Series Forecasting equips participants with the specialized knowledge and skills to build and deploy accurate forecasting models. Participants will learn how to utilize statistical methods, machine learning algorithms, and time series techniques to predict future outcomes. This course bridges the gap between historical data and future insights, empowering professionals to make proactive decisions and optimize business strategies.

Target Audience:

This course is designed for data professionals seeking to enhance their forecasting skills, including:

• Data Scientists
• Data Analysts
• Business Analysts
• Statisticians
• Financial Analysts
• Anyone involved in forecasting and predictive modeling

Course Objectives:

Upon completion of this Predictive Analytics & Time Series Forecasting course, participants will be able to:

• Understand the principles and methodologies of predictive analytics and time series forecasting.
• Utilize statistical methods and machine learning algorithms for forecasting.
• Implement time series decomposition and analysis techniques.
• Build and evaluate forecasting models using various metrics.
• Understand and apply different forecasting models (ARIMA, Exponential Smoothing, etc.).
• Implement feature engineering for forecasting tasks.
• Understand the challenges and limitations of forecasting.
• Apply forecasting techniques to real-world scenarios.
• Develop strategies for model selection and optimization.
• Implement forecasting for various applications (sales, demand, finance, etc.).
• Enhance their ability to build accurate and reliable forecasting models.
• Improve their organization's forecasting capabilities.
• Contribute to improved data-driven decision-making.
• Stay up-to-date with the latest trends and best practices in predictive analytics and time series forecasting.
• Become a more knowledgeable and effective forecasting professional.
• Understand ethical considerations in predictive modeling.
• Learn how to use forecasting tools and platforms effectively.

DURATION

10 Days

COURSE CONTENT

Module 1: Introduction to Predictive Analytics and Time Series Forecasting

• Understanding the concepts and applications of predictive analytics.
• Introduction to time series data and its characteristics.
• Understanding the importance of forecasting in decision-making.
• Overview of forecasting methodologies and techniques.
• Setting up the development environment (Python, R, etc.).

Module 2: Fundamentals of Time Series Analysis

• Understanding time series decomposition (trend, seasonality, residuals).
• Identifying patterns and anomalies in time series data.
• Understanding stationarity and its importance.
• Utilizing time series plots and visualizations.
• Understanding autocorrelation and partial autocorrelation.

Module 3: Statistical Methods for Time Series Forecasting

• Understanding moving averages and smoothing techniques.
• Implementing exponential smoothing methods (simple, Holt, Holt-Winters).
• Understanding ARIMA models (AR, MA, ARMA, ARIMA).
• Implementing ARIMA model selection and parameter tuning.
• Evaluating statistical forecasting models.

Module 4: Machine Learning for Time Series Forecasting

• Understanding machine learning concepts and algorithms for forecasting.
• Utilizing regression models (linear regression, polynomial regression).
• Implementing tree-based models (random forest, gradient boosting).
• Utilizing neural networks for time series forecasting.
• Evaluating machine learning forecasting models.

Module 5: Feature Engineering for Time Series Forecasting

• Understanding the importance of feature engineering in forecasting.
• Creating time-based features (lags, rolling averages, seasonality indicators).
• Utilizing external data and features.
• Implementing feature selection and dimensionality reduction.
• Understanding feature scaling and normalization.

Module 6: Time Series Decomposition and Analysis Techniques

• Implementing additive and multiplicative decomposition.
• Utilizing seasonal decomposition of time series (STL).
• Understanding cyclical patterns and their impact.
• Implementing frequency domain analysis (Fourier transforms).
• Utilizing wavelet analysis for time series decomposition.

Module 7: Forecasting Model Evaluation and Selection

• Understanding different forecasting evaluation metrics (MAE, RMSE, MAPE).
• Implementing cross-validation techniques for time series data.
• Utilizing information criteria (AIC, BIC) for model selection.
• Understanding the bias-variance tradeoff in forecasting.
• Implementing model diagnostics and residual analysis.

Module 8: Advanced Time Series Models

• Understanding SARIMA models (seasonal ARIMA).
• Implementing vector autoregression (VAR) models.
• Utilizing state space models (Kalman filters).
• Implementing Prophet for time series forecasting.
• Understanding deep learning models for time series forecasting.

Module 9: Forecasting for Specific Applications (Sales, Demand, Finance)

• Understanding forecasting challenges in different domains.
• Implementing sales and demand forecasting techniques.
• Utilizing forecasting for financial analysis and risk management.
• Implementing forecasting for supply chain management.
• Understanding forecasting for marketing and customer behavior.

Module 10: Handling Missing Data and Outliers in Time Series

• Understanding the impact of missing data and outliers on forecasting.
• Implementing imputation techniques for missing data.
• Utilizing outlier detection and treatment methods.
• Understanding the challenges of handling irregular time series data.
• Implementing robust forecasting techniques.

Module 11: Forecasting with External Factors and Events

• Understanding the impact of external factors and events on forecasting.
• Implementing regression models with external regressors.
• Utilizing event calendars and holiday effects.
• Understanding the challenges of incorporating exogenous variables.
• Implementing dynamic regression models.

Module 12: Forecasting Model Deployment and Monitoring

• Understanding forecasting model deployment strategies.
• Implementing model versioning and management.
• Utilizing cloud-based forecasting platforms.
• Implementing real-time forecasting and monitoring.
• Understanding the importance of model retraining and updating.

Module 13: Ensemble Forecasting and Model Stacking

• Understanding ensemble forecasting techniques.
• Implementing model averaging and weighted averaging.
• Utilizing model stacking and blending.
• Understanding the benefits of ensemble forecasting.
• Implementing ensemble forecasting for improved accuracy.

Module 14: Forecasting in Cloud Environments and Big Data

• Understanding forecasting challenges in cloud and big data environments.
• Utilizing cloud-based forecasting services.
• Implementing distributed forecasting with Spark and other platforms.
• Understanding the impact of big data on forecasting accuracy.
• Implementing scalable forecasting solutions.

Module 15: Best Practices and Future Trends in Forecasting

• Understanding best practices for building and deploying forecasting models.
• Implementing forecasting governance and compliance.
• Exploring emerging trends in forecasting (deep learning, AI-driven forecasting).
• Understanding the impact of forecasting on business strategy.
• Continuous learning and professional development in forecasting.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 5 working days before commencement of the training.