Towards The Inductive Acquisition of Temporal Knowledge
This work addresses the need for symbolic prediction in time series analysis, offering a novel approach for domains where knowledge is better expressed qualitatively, though it appears incremental as it builds on existing inductive inference techniques.
The paper tackles the problem of making qualitative predictions from symbolic temporal data by introducing TIM, a domain-independent methodology for discovering uncertain temporal patterns from real-time observations using inductive inference.
The ability to predict the future in a given domain can be acquired by discovering empirically from experience certain temporal patterns that tend to repeat unerringly. Previous works in time series analysis allow one to make quantitative predictions on the likely values of certain linear variables. Since certain types of knowledge are better expressed in symbolic forms, making qualitative predictions based on symbolic representations require a different approach. A domain independent methodology called TIM (Time based Inductive Machine) for discovering potentially uncertain temporal patterns from real time observations using the technique of inductive inference is described here.