This blog post discusses the importance of time series forecasting in data-driven decision-making and explores a robust time series forecasting model using Amazon SageMaker. It highlights the use of MLOps infrastructure for automating the model development process and explains the steps involved in training and deploying the model. The post also provides an overview of the solution’s architecture and showcases the effectiveness of the Spliced Binned Pareto (SBP) distribution compared to a Gaussian distribution. The article concludes by emphasizing the value of automating MLOps features and offers recommendations for further exploration and customization of the solution.
Time series forecasting plays a crucial role in data-driven decision-making by leveraging historical data patterns to predict future outcomes. Whether it’s for asset risk management, trading, weather prediction, energy demand forecasting, vital sign monitoring, or traffic analysis, accurate forecasting is essential for success.
However, in applications where time series data exhibits heavy-tailed distributions with extreme values, accurate forecasting becomes challenging. These outliers can significantly impact the estimation of the base distribution, making robust forecasting difficult. To address this, robust models are needed, especially in sectors such as finance, energy, weather, and healthcare, where accurate forecasts of infrequent but high-impact events are crucial.
In this review, we explore a robust time series forecasting model trained using Amazon SageMaker. We highlight the importance of establishing an MLOps infrastructure to streamline the model development process, automate data preprocessing, feature engineering, hyperparameter tuning, and model selection. This automation reduces human error, improves reproducibility, and accelerates the model development cycle.
Once the model is trained, deploying it within an endpoint enables real-time prediction capabilities, empowering businesses to make well-informed decisions based on the most recent data. Additionally, deploying the model in an endpoint allows for scalability, as multiple users and applications can access and utilize the model simultaneously.
In summary, this review showcases the power of robust time series forecasting using Amazon SageMaker, emphasizing the importance of MLOps infrastructure, training pipelines, and real-time prediction capabilities. By leveraging these tools, businesses can make accurate forecasts, stay ahead in a rapidly changing environment, and make informed decisions to drive success.
Action items from the meeting notes:
1. Generate a synthetic dataset for training the time series forecasting model.
2. Split the dataset into training, validation, and test sets.
3. Train two TCN models: one using Spliced Binned Pareto (SBP) distribution and the other using Gaussian distribution.
4. Perform hyperparameter tuning for both models using the validation set.
5. Evaluate the models using the test set and calculate the root mean squared error (RMSE).
6. Compare the predicted distributions of SBP and Gaussian models using a probability-probability (P-P) plot.
7. Select the model with the lowest RMSE for improved distribution accuracy.
8. Upload the selected model to the SageMaker Model Registry.
9. Deploy the model using SageMaker hosting services to create an inference endpoint.
10. Clean up any unnecessary AWS resources to avoid unexpected costs.Please assign the tasks to the appropriate team members.
