This week, I am working to get the dynamic time warping functionality into my program. The process of doing so includes re-processing the features to include the time series, putting each series back together when we construct sequences, and then performing the DTW to generate a number that will be used to compute the kNN of each sequence which can then be used for predictions with the models. The processing time of these activities has gone up significantly since we have been using five different metrics with each of the F phase datasets. I am returning to school next week, and once I've completed the DTW processing all that will remain before we put together our second paper (The date for the reach journal we would like to submit it to is October 1), I am hoping I will have time to look again into the Agglomerative Hierarchical Clustering concept, which I did not successfully complete when we explored it earlier in the summer and then changed focus to the paper. We heard back
With our paper out of the way, I began looking at making
predictions based on data from multiple days preceding an instance. This would
be helpful, because it would give us more features and take into account more
than only the two periods preceding the period to predict. We can then compare
the effectiveness of this approach to the initial one, and if we find that
epochs with more historical data can generate more accurate results, we can
conclude that we should look more into the patterns leading up to the period we
want to predict.
Additionally, I want to add a new feature to each epoch called
“DAYS_SINCE_EVENT” which contains the number of days that have passed since the
patient had either a TBI or Stroke.
We will refer to the number of periods before an instance as
P. So P1 will be a set of epochs containing only features from a
nighttime period being used to predict the following daytime activity or
features from a daytime period being used to predict the following nighttime
inactivity. The heights that this number can reach will be limited since we
are excluding baseline data from the model and some patients only have seven
days of data in total.
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