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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.

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