solution with space of complicated modelsthe simplewell-performing baseline may be unlearnableeven if it' technically part of the hypothesis space that is pretty significant limitation of machine learning in generalunless the learning algorithm is hardcoded to look for specific kind of simple modelparameter learning can sometimes fail to find simple solution to simple problem first recurrent baseline the first fully connected approach didn' do wellbut that doesn' mean machine learning isn' applicable to this problem the previous approach first flattened the timeserieswhich removed the notion of time from the input data let' instead look at the data as what it isa sequencewhere causality and order matter you'll try recurrent-sequence processing model--it should be the perfect fit for such sequence dataprecisely because it exploits the temporal ordering of data pointsunlike the first approach instead of the lstm layer introduced in the previous sectionyou'll use the gru layerdeveloped by chung et al in gated recurrent unit (grulayers work using the same principle as lstmbut they're somewhat streamlined and thus cheaper to run (although they may not have as much representational power as lstmthis trade-off between computational expensiveness and representational power is seen everywhere in machine learning listing training and evaluat ing gru-based model from keras models import sequential from keras import layers from keras optimizers import rmsprop model sequential(model add(layers gru( input_shape=(nonefloat_data shape[- ]))model add(layers dense( )model compile(optimizer=rmsprop()loss='mae'history model fit_generator(train_gensteps_per_epoch= epochs= validation_data=val_genvalidation_steps=val_stepsfigure shows the results much betteryou can significantly beat the commonsense baselinedemonstrating the value of machine learning as well as the superiority of recurrent networks compared to sequence-flattening dense networks on this type of task junyoung chung et al "empirical evaluation of gated recurrent neural networks on sequence modeling,conference on neural information processing systems ( )licensed to