Azure Data Scientists Associate Practice Exam

Which of the following algorithms is strictly suited for time series forecasting and cannot be exported to ONNX models?

1. SVC algorithm

2. Random forest

3. Auto-ARIMA

4. Decision tree

The correct answer is: Auto-ARIMA

The Auto-ARIMA algorithm is specifically designed for time series forecasting. It automatically selects the best ARIMA model parameters based on the characteristics of the time series data, such as trend and seasonality. This capability makes it particularly suited for capturing dynamic patterns and predicting future values based on past observations. One key aspect that aligns with the question is that Auto-ARIMA, as a specialized statistical method for time series forecasting, often relies on certain statistical assumptions and methodologies that may not always translate well into the Open Neural Network Exchange (ONNX) format, which is primarily designed for machine learning models. ONNX is more concerned with generalizing the deployment of neural network models and certain machine learning workflows, making it less compatible with purely statistical algorithms like Auto-ARIMA. In contrast, other algorithms mentioned, such as support vector classifiers (SVC), random forests, and decision trees, primarily focus on predictive modeling for classification and regression tasks. These models are generally more adaptable to different formats, including ONNX, as their structure is conducive to representation in a way that can be executed in a variety of environments and platforms.