AI module for gap-filling TreeNet time series

In a recent WSL research project (deepT - internal grant no. 202011N2099), a machine learning model was developed for gap-filling multi-channel time series data. The goal is to incorporate this model into the automated near real-time TreeNet data acquisition infrastructure. This addition will allow TreeNet dendrometer data users to fill time series gaps automatically using artificial intelligence. The existing model needs refinement with new data, programming in R (the native language of TreeNet software), integration into the pipeline, and adaptation of data to the model's input and output requirements.