I have the following dataframe containing training data that I have been using to perform a regression task using CNN + FC :
fileName var_t+15m var_t+30m var_t+45m var_t+60m var_t+90m var_t+120m var_t+180m var_t+240
id
2016-10-15 15:00:00 201610151500.jpg 211.00 197.80 170.80 66.90 34.2000 10.120000 0.000867 0.001267
2016-10-15 15:15:00 201610151515.jpg 197.80 170.80 66.90 71.75 20.1600 2.120000 0.001534 0.000534
2016-10-15 15:30:00 201610151530.jpg 170.80 66.90 71.75 34.20 10.1200 0.206200 0.001000 0.001067
2016-10-15 15:45:00 201610151545.jpg 66.90 71.75 34.20 20.16 2.1200 0.012270 0.000400 0.000733
2016-10-15 16:00:00 201610151600.jpg 71.75 34.20 20.16 10.12 0.2062 0.000867 0.001267 0.000934
The task consists in predicting a certain variable at t+X where X goes from 15 minutes up to 240 minutes. So this is a regression task where my training input consists in timestamped picture.
In order to work with these data, I was until now using the .flow_from_dataframe method from Keras in order to perform data augmentation/pre-processing easily and to avoid loading the entire training set consisting of pictures inside the memory.
Up until now I did not leverage the time information and to do so I would like to try the convLSTM model available in Keras. Howevever I am very unfamilar with working with time series.
Has someone used the Keras convLSTM layer combined with the .flow_from_dataframe function ? I am unsure how to structure my data for this setup (convLSTM + .flow_from_dataframe) and I could not find an example on the internet.
