  Recently, a hybrid Deep Neural Network (DNN) algorithm, TreNet was proposed
for predicting trends in time series data. While TreNet was shown to have
superior performance for trend prediction to other DNN and traditional ML
approaches, the validation method used did not take into account the sequential
nature of time series data sets and did not deal with model update. In this
research we replicated the TreNet experiments on the same data sets using a
walk-forward validation method and tested our optimal model over multiple
independent runs to evaluate model stability. We compared the performance of
the hybrid TreNet algorithm, on four data sets to vanilla DNN algorithms that
take in point data, and also to traditional ML algorithms. We found that in
general TreNet still performs better than the vanilla DNN models, but not on
all data sets as reported in the original TreNet study. This study highlights
the importance of using an appropriate validation method and evaluating model
stability for evaluating and developing machine learning models for trend
prediction in time series data.
