15 Topics
10 Days
74 Sessions
Programme Overview
Training Description
Who Should Attend
Session 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.
About the Course
This advanced training equips professionals with the analytical techniques required to interpret historical business data, identify patterns, build forecasting models, and support data-driven decision-making. It focuses on applying time series analytics to sales, revenue, demand, financial performance, inventory, customer activity, and other business indicators.Participants will explore trend and seasonality analysis, stationarity, autocorrelation, forecasting models, model selection, anomaly detection, forecasting accuracy, and scenario analysis. The course also introduces modern machine learning approaches for time series forecasting and emphasizes translating analytical results into practical business strategies.
Curriculum & Topics
15 Topics | 74 Sessions
-
Workshop 1.1:
Fundamentals of time series forecasting with advanced techniques.
-
Workshop 1.2:
Overview of ARIMA, LSTM, and other advanced methods.
-
Workshop 1.3:
Setting up a time series forecasting development environment.
-
Workshop 1.4:
Introduction to time series libraries and tools.
-
Workshop 1.5:
Best practices for advanced forecasting.
-
Workshop 2.1:
Implementing ARIMA models for time series data.
-
Workshop 2.2:
Utilizing ACF and PACF for model selection.
-
Workshop 2.3:
Designing and building ARIMA forecasting pipelines.
-
Workshop 2.4:
Optimizing ARIMA models for prediction accuracy.
-
Workshop 2.5:
Best practices for ARIMA.
-
Workshop 3.1:
Implementing LSTM networks for time series forecasting.
-
Workshop 3.2:
Utilizing sequence data processing for LSTM models.
-
Workshop 3.3:
Designing and building LSTM forecasting models.
-
Workshop 3.4:
Optimizing LSTM models for time series prediction.
-
Workshop 3.5:
Best practices for LSTM.
-
Workshop 4.1:
Implementing Prophet for time series forecasting.
-
Workshop 4.2:
Utilizing SARIMA for seasonal time series data.
-
Workshop 4.3:
Implementing VAR for multivariate time series.
-
Workshop 4.4:
Designing and building hybrid forecasting models.
-
Workshop 4.5:
Best practices for advanced methods.
-
Workshop 5.1:
Designing forecasting models for specific time series data.
-
Workshop 5.2:
Implementing model architectures for various forecasting tasks.
-
Workshop 5.3:
Utilizing feature engineering for time series data.
-
Workshop 5.4:
Optimizing model design for forecasting accuracy.
-
Workshop 5.5:
Best practices for model design.
-
Workshop 6.1:
Optimizing forecasting models for accuracy and performance.
-
Workshop 6.2:
Utilizing hyperparameter tuning techniques.
-
Workshop 6.3:
Implementing model validation and cross-validation.
-
Workshop 6.4:
Designing scalable forecasting solutions.
-
Workshop 6.5:
Best practices for model optimization.
-
Workshop 7.1:
Debugging complex forecasting issues.
-
Workshop 7.2:
Analyzing model performance and errors.
-
Workshop 7.3:
Utilizing troubleshooting techniques for model improvement.
-
Workshop 7.4:
Resolving common forecasting challenges.
-
Workshop 7.5:
Best practices for troubleshooting.
-
Workshop 8.1:
Implementing evaluation metrics for time series forecasting.
-
Workshop 8.2:
Utilizing cross-validation techniques for time series models.
-
Workshop 8.3:
Designing and building model validation pipelines.
-
Workshop 8.4:
Optimizing model evaluation strategies.
-
Workshop 8.5:
Best practices for model evaluation.
-
Workshop 9.1:
Integrating forecasting models into production systems.
-
Workshop 9.2:
Utilizing APIs and deployment tools for forecasting.
-
Workshop 9.3:
Implementing real-time forecasting applications.
-
Workshop 9.4:
Optimizing models for deployment environments.
-
Workshop 9.5:
Best practices for integration.
-
Workshop 10.1:
Utilizing grid search and random search for tuning.
-
Workshop 10.2:
Implementing Bayesian optimization for hyperparameter selection.
-
Workshop 10.3:
Designing and building hyperparameter tuning pipelines.
-
Workshop 10.4:
Optimizing hyperparameters for model performance.
-
Workshop 10.5:
Best practices for hyperparameter tuning.
-
Workshop 11.1:
Implementing feature extraction techniques for time series.
-
Workshop 11.2:
Utilizing time-lagged features and rolling statistics.
-
Workshop 11.3:
Designing and building feature engineering pipelines.
-
Workshop 11.4:
Optimizing feature engineering for forecasting accuracy.
-
Workshop 11.5:
Best practices for feature engineering.
-
Workshop 12.1:
Implementing ARIMA and LSTM for financial forecasting.
-
Workshop 12.2:
Utilizing advanced methods for demand forecasting in retail.
-
Workshop 12.3:
Implementing forecasting models for energy consumption.
-
Workshop 12.4:
Utilizing forecasting for predicting website traffic.
-
Workshop 12.5:
Best practices for real-world applications.
-
Workshop 13.1:
Utilizing statsmodels for ARIMA and statistical forecasting.
-
Workshop 13.2:
Implementing TensorFlow and PyTorch for LSTM models.
-
Workshop 13.3:
Designing and building forecasting pipelines with libraries.
-
Workshop 13.4:
Optimizing library usage for efficient implementation.
-
Workshop 13.5:
Best practices for library implementation.
-
Workshop 14.1:
Implementing model interpretability techniques for time series.
-
Workshop 14.2:
Utilizing visualization tools for model understanding.
-
Workshop 14.3:
Designing and building interpretable forecasting models.
-
Workshop 14.4:
Optimizing model transparency.
-
Workshop 14.5:
Best practices for model interpretability.
-
Workshop 15.1:
Emerging trends in time series forecasting.
-
Workshop 15.2:
Utilizing deep learning for long-range forecasting.
-
Workshop 15.3:
Implementing automated machine learning (AutoML) for time series.
-
Workshop 15.4:
Best practices for future forecasting.
