15 Topics
10 Days
71 Sessions
Programme Overview
Training Description
Who Should Attend
Session Objectives
- Understand the fundamentals of time series analysis.
- Master statistical modeling techniques for time-dependent data.
- Implement various forecasting methods for big data.
- Utilize industry-standard tools and frameworks for time series analysis.
- Develop and evaluate time series models for various applications.
- Optimize time series models for accuracy and performance.
- Deploy time series models for real-world scenarios.
- Troubleshoot and debug time series analysis pipelines.
- Implement data security and access control in time series workflows.
- Integrate time series models with big data platforms.
- Understand how to monitor and maintain time series models.
- Explore advanced time series techniques for large datasets.
- Apply real world use cases for Time Series Analysis in Big Data.
About the Course
This advanced training equips participants with the knowledge and practical techniques required to analyze, model, forecast, and visualize large-scale time series datasets. The course combines statistical time series methods with modern big data technologies to help professionals extract meaningful patterns from high-volume, high-frequency, and continuously generated data.Participants will explore time series decomposition, trend and seasonality analysis, stationarity, autocorrelation, forecasting, anomaly detection, feature engineering, and machine learning approaches. The training also addresses distributed time series processing, scalable forecasting, real-time analytics, and the use of cloud and big data platforms for large-scale time-dependent datasets.
Curriculum & Topics
15 Topics | 71 Sessions
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Workshop 1.1:
Fundamentals of time series data and analysis.
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Workshop 1.2:
Overview of time series components (trend, seasonality, noise).
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Workshop 1.3:
Setting up a development environment for time series analysis.
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Workshop 1.4:
Introduction to time series tools and libraries.
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Workshop 1.5:
Best practices for time series analysis.
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Workshop 2.1:
Autoregressive (AR) models.
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Workshop 2.2:
Moving average (MA) models.
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Workshop 2.3:
Autoregressive moving average (ARMA) models.
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Workshop 2.4:
Autoregressive integrated moving average (ARIMA) models. 1
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Workshop 2.5:
Seasonal ARIMA (SARIMA) models.
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Workshop 3.1:
Exponential smoothing methods (Simple, Holt, Holt-Winters).
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Workshop 3.2:
Decomposition methods (additive, multiplicative).
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Workshop 3.3:
Machine learning approaches (regression, tree-based models).
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Workshop 3.4:
Deep learning approaches (LSTM, GRU).
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Workshop 3.5:
Hybrid forecasting methods.
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Workshop 4.1:
Utilizing Python libraries (Pandas, Statsmodels, Scikit-learn).
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Workshop 4.2:
Using R packages (forecast, tseries).
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Workshop 4.3:
Implementing time series analysis in Spark.
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Workshop 4.4:
Utilizing cloud-based time series services.
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Workshop 4.5:
Best practices for tool selection.
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Workshop 5.1:
Evaluating model performance using various metrics (RMSE, MAE, MAPE).
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Workshop 5.2:
Implementing cross-validation for time series data.
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Workshop 5.3:
Optimizing models for accuracy and computational efficiency.
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Workshop 5.4:
Handling missing data and outliers.
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Workshop 5.5:
Best practices for model evaluation.
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Workshop 6.1:
Creating lagged features and rolling statistics.
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Workshop 6.2:
Utilizing domain knowledge for feature engineering.
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Workshop 6.3:
Handling seasonality and trend components.
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Workshop 6.4:
Feature selection and transformation techniques.
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Workshop 6.5:
Best practices for feature engineering.
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Workshop 7.1:
Deploying time series models in production environments.
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Workshop 7.2:
Utilizing containerization and orchestration tools.
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Workshop 7.3:
Implementing API endpoints for time series services.
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Workshop 7.4:
Monitoring model performance in production.
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Workshop 7.5:
Best practices for model deployment.
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Workshop 8.1:
Debugging time series models and pipelines.
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Workshop 8.2:
Analyzing model errors and performance issues.
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Workshop 8.3:
Utilizing debugging tools and techniques.
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Workshop 8.4:
Identifying and resolving model biases.
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Workshop 8.5:
Best practices for model troubleshooting.
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Workshop 9.1:
Implementing data security in time series workflows.
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Workshop 9.2:
Utilizing authentication and authorization.
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Workshop 9.3:
Implementing data encryption and masking.
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Workshop 9.4:
Auditing and compliance in time series.
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Workshop 9.5:
Best practices for data security.
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Workshop 10.1:
Integrating time series models with Hadoop and Spark.
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Workshop 10.2:
Utilizing cloud-based time series services.
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Workshop 10.3:
Implementing real-time time series pipelines.
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Workshop 10.4:
Best practices for integration.
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Workshop 11.1:
Monitoring model performance and drift.
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Workshop 11.2:
Implementing model retraining and updating.
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Workshop 11.3:
Utilizing model monitoring tools and techniques.
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Workshop 11.4:
Handling model versioning and rollback.
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Workshop 11.5:
Best practices for model maintenance.
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Workshop 12.1:
Multivariate time series analysis.
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Workshop 12.2:
Dynamic time warping (DTW).
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Workshop 12.3:
State space models.
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Workshop 12.4:
Bayesian time series analysis.
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Workshop 12.5:
Advanced techniques for large-scale time series.
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Workshop 13.1:
Utilizing cloud-based time series services.
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Workshop 13.2:
Deploying time series models on AWS, Azure, and GCP.
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Workshop 13.3:
Optimizing cloud resources for time series analysis.
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Workshop 13.4:
Best practices for cloud-based time series.
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Workshop 14.1:
Implementing data governance policies in time series.
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Workshop 14.2:
Utilizing metadata management tools.
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Workshop 14.3:
Implementing data lineage and data dictionary.
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Workshop 14.4:
Best practices for data governance.
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Workshop 15.1:
Emerging trends in time series research and applications.
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Workshop 15.2:
Utilizing AI and automation in time series workflows.
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Workshop 15.3:
Implementing explainable time series models.
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Workshop 15.4:
Best practices for future time series.
