figure shows the training and validation maes figure training and validation loss on the jena temperature-forecast ing ask with simple convnet the validation mae stays in the syou can' even beat the common-sense baseline using the small convnet againthis is because the convnet looks for patterns anywhere in the input timeseries and has no knowledge of the temporal position of pattern it sees (toward the beginningtoward the endand so onbecause more recent data points should be interpreted differently from older data points in the case of this specific forecasting problemthe convnet fails at producing meaningful results this limitation of convnets isn' an issue with the imdb databecause patterns of keywords associated with positive or negative sentiment are informative independently of where they're found in the input sentences one strategy to combine the speed and lightness of convnets with the order-sensitivity of rnns is to use convnet as preprocessing step before an rnn (see figure this is especially beneficial when you're dealing with sequences that are so long they can' realistically be processed with rnnssuch as sequences with thousands of steps the convrnn net will turn the long input sequence into much shorter (downsampledsequences of higher-level features this sequence of shorter cnn features sequence extracted features then becomes the input to the rnn part of the network this technique isn' seen often in cnn research papers and practical applicationspossibly because it isn' well known it' effective and ought to be more common let' try long sequence it on the temperature-forecasting dataset because this strategy allows you to manipufigure combining convnet and late much longer sequencesyou can either an rnn for processing long sequences licensed to