Part 6/8:
Imputation and Anomaly Detection: These tasks can be performed using the VQV without the need for downstream modeling since the learned representations provide completeness.
Translation and Classification: By utilizing token sequences pertaining to various sensor data, the model determines the missing sensor data or classifies activities from complete inputs.
Forecasting: Tokenized input data combined with a look-back length facilitates predictions, supported by Transformer encoders to extrapolate future data points effectively.
During testing phases, both in-domain and zero-shot scenarios illustrate the robustness of the model in adapting to unfamiliar datasets.