What prediction method is suitable for synchronous or real-time predictions?

The suitable prediction method for synchronous or real-time predictions is online prediction. This method allows for immediate responses to incoming data, making it ideal for scenarios where users or applications require fast feedback. Online prediction continuously processes data and outputs prediction results in a timely manner, which is essential in applications such as fraud detection, real-time recommendations, or chatbots.

In contrast, batch processing involves collecting and processing data in bulk at scheduled intervals, which is not suitable for real-time needs as there can be significant delays between data collection and prediction output. Asynchronous prediction typically involves systems that can handle requests in a non-blocking way, but it might not guarantee immediate feedback to the user, depending on the implementation. Progressive prediction refers to methods that dynamically update predictions as new data arrives but may not offer the immediacy required for synchronous real-time interactions.

Overall, online prediction is the optimal choice for applications that necessitate prompt decision-making and immediate response to user interactions or real-time data streams.