[ ] "Manual" way of doing it: Get time of day distributions from large but low sample rate datasets, use that to generate infinite amounts of training data by combining tracebase waveforms
Could try to build single system which can cope with many different sample rates from kHz to hourly data. Top few layers are common (and hence, if they are recurrent, might need to output at a sample rate which is the lowest common sample rate across all datasets, say 2 minutes) . But replace bottom layers for different sample rates. I suppose this is a form of ‘transfer learning’ except I want bi-directional transfer. Maybe we need to concurrently train multiple parallel lower layers (each for a different ‘type’ of input, e.g. kHz, 1 second, 10 second, 1 minute, 2 minute, 15 minute, with or without reactive power etc) all connected to a single upper layer. Training would be interleaved. This would perhaps force the upper layer to learn the common properties. NIPS 2014 paper on multimodal deep learning
Could try to build single system which can cope with many different sample rates from kHz to hourly data. Top few layers are common (and hence, if they are recurrent, might need to output at a sample rate which is the lowest common sample rate across all datasets, say 2 minutes) . But replace bottom layers for different sample rates. I suppose this is a form of ‘transfer learning’ except I want bi-directional transfer. Maybe we need to concurrently train multiple parallel lower layers (each for a different ‘type’ of input, e.g. kHz, 1 second, 10 second, 1 minute, 2 minute, 15 minute, with or without reactive power etc) all connected to a single upper layer. Training would be interleaved. This would perhaps force the upper layer to learn the common properties. NIPS 2014 paper on multimodal deep learning