Time Series Forecasting With Advanced Techniques Training Course: Arima & Lstm Mastery

Introduction

Predict future trends with precision using our Time Series Forecasting with Advanced Techniques Training Course. This program is designed to provide you with in-depth knowledge and practical skills in exploring ARIMA, LSTM, and other advanced forecasting methods, enabling you to build accurate predictive models for time-dependent data. In today's data-driven world, mastering advanced time series forecasting is crucial for making informed decisions across various industries, from finance to supply chain management. Our time series forecasting training course offers hands-on experience and expert guidance, empowering you to implement state-of-the-art forecasting techniques.

This ARIMA & LSTM forecasting training delves into the core concepts of advanced time series analysis, covering topics such as ARIMA models for statistical forecasting, LSTM networks for deep learning forecasting, and hybrid forecasting approaches. You'll gain expertise in using industry-standard libraries and tools to explore ARIMA, LSTM, and other advanced forecasting methods, meeting the demands of modern data science projects. Whether you're a data scientist, forecasting analyst, or researcher, this Time Series Forecasting with Advanced Techniques course will empower you to build powerful predictive models.

Target Audience:

• Data Scientists
• Forecasting Analysts
• Researchers
• Data Analysts
• Financial Analysts
• Supply Chain Managers
• Anyone needing advanced time series forecasting skills

Course Objectives:

• Understand the fundamentals of time series forecasting with advanced techniques.
• Master ARIMA models for statistical time series forecasting.
• Utilize LSTM networks for deep learning time series forecasting.
• Implement advanced forecasting methods (e.g., Prophet, SARIMA, VAR).
• Design and build forecasting models for various time series data.
• Optimize forecasting models for accuracy and performance.
• Troubleshoot and address complex time series forecasting challenges.
• Implement model evaluation and validation techniques for time series.
• Integrate forecasting models into real-world applications.
• Understand how to tune hyperparameters for optimal forecasting.
• Explore advanced feature engineering techniques for time series.
• Apply real world use cases for ARIMA and LSTM forecasting.
• Leverage time series libraries for efficient model implementation.

Duration

10 Days

Course content

Module 1: Introduction to Time Series Forecasting with Advanced Techniques

• Fundamentals of time series forecasting with advanced techniques.
• Overview of ARIMA, LSTM, and other advanced methods.
• Setting up a time series forecasting development environment.
• Introduction to time series libraries and tools.
• Best practices for advanced forecasting.

Module 2: ARIMA Models

• Implementing ARIMA models for time series data.
• Utilizing ACF and PACF for model selection.
• Designing and building ARIMA forecasting pipelines.
• Optimizing ARIMA models for prediction accuracy.
• Best practices for ARIMA.

Module 3: LSTM Networks for Time Series

• Implementing LSTM networks for time series forecasting.
• Utilizing sequence data processing for LSTM models.
• Designing and building LSTM forecasting models.
• Optimizing LSTM models for time series prediction.
• Best practices for LSTM.

Module 4: Advanced Forecasting Methods

• Implementing Prophet for time series forecasting.
• Utilizing SARIMA for seasonal time series data.
• Implementing VAR for multivariate time series.
• Designing and building hybrid forecasting models.
• Best practices for advanced methods.

Module 5: Forecasting Model Design

• Designing forecasting models for specific time series data.
• Implementing model architectures for various forecasting tasks.
• Utilizing feature engineering for time series data.
• Optimizing model design for forecasting accuracy.
• Best practices for model design.

Module 6: Model Optimization and Performance

• Optimizing forecasting models for accuracy and performance.
• Utilizing hyperparameter tuning techniques.
• Implementing model validation and cross-validation.
• Designing scalable forecasting solutions.
• Best practices for model optimization.

Module 7: Troubleshooting Forecasting Challenges

• Debugging complex forecasting issues.
• Analyzing model performance and errors.
• Utilizing troubleshooting techniques for model improvement.
• Resolving common forecasting challenges.
• Best practices for troubleshooting.

Module 8: Model Evaluation and Validation

• Implementing evaluation metrics for time series forecasting.
• Utilizing cross-validation techniques for time series models.
• Designing and building model validation pipelines.
• Optimizing model evaluation strategies.
• Best practices for model evaluation.

Module 9: Integration with Real-World Applications

• Integrating forecasting models into production systems.
• Utilizing APIs and deployment tools for forecasting.
• Implementing real-time forecasting applications.
• Optimizing models for deployment environments.
• Best practices for integration.

Module 10: Hyperparameter Tuning

• Utilizing grid search and random search for tuning.
• Implementing Bayesian optimization for hyperparameter selection.
• Designing and building hyperparameter tuning pipelines.
• Optimizing hyperparameters for model performance.
• Best practices for hyperparameter tuning.

Module 11: Advanced Feature Engineering

• Implementing feature extraction techniques for time series.
• Utilizing time-lagged features and rolling statistics.
• Designing and building feature engineering pipelines.
• Optimizing feature engineering for forecasting accuracy.
• Best practices for feature engineering.

Module 12: Real-World Use Cases

• Implementing ARIMA and LSTM for financial forecasting.
• Utilizing advanced methods for demand forecasting in retail.
• Implementing forecasting models for energy consumption.
• Utilizing forecasting for predicting website traffic.
• Best practices for real-world applications.

Module 13: Time Series Libraries Implementation

• Utilizing statsmodels for ARIMA and statistical forecasting.
• Implementing TensorFlow and PyTorch for LSTM models.
• Designing and building forecasting pipelines with libraries.
• Optimizing library usage for efficient implementation.
• Best practices for library implementation.

Module 14: Model Interpretability

• Implementing model interpretability techniques for time series.
• Utilizing visualization tools for model understanding.
• Designing and building interpretable forecasting models.
• Optimizing model transparency.
• Best practices for model interpretability.

Module 15: Future Trends in Time Series Forecasting

• Emerging trends in time series forecasting.
• Utilizing deep learning for long-range forecasting.
• Implementing automated machine learning (AutoML) for time series.
• Best practices for future 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 7 working days before commencement of the training.