Functional Cluster Analysis of Time Series Data in Food Traceability

A general scheme of time series data in the food supply chain is proposed. By using functional cluster analysis, the time series data are treated as a complete object of a time function, rather than a simple arrangement of individual observations. In this scheme, the discrete data are transformed into functional data while the distance of the origin function is replaced with the distance of the expansion coefficients' vector of the base function. In this way, the system can reduce the large number of numerical integration and simplify the calculation. Experiments indicate that the availability of the traceability data is enhanced significantly after discrete data are clustered into sequential information.