Prediction With Uncertainty: A Novel Framework for Analyzing Sensor Data Streams

摘要

In this paper, we present a novel framework to predict events through time-series analysis of sensor data streams. The framework is capable of producing and visualizing event prediction probabilities, uncertainties around the predictions, and the actual decision being taken based on the prediction. We have tested the analytical framework on predicting closure events in shellfish farms in Tasmania. Reasonably high prediction accuracy is achieved. The visualization was able to capture prediction, uncertainty, and actual decision being taken (i.e., three-in-one).