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
This week, I performed experiments with the first and second phase data together and varied different factors. These were various model types from sklearn, length of the sequence (How many periods precede the one that we are attempting to predict), number of nearby sequences to use and subgroupings (gender, phase, injury type, experiment group). The primary aspect of this that we didn't get to experiment with when we were finishing our final report was testing out how different models worked. My model for the report was a from-scratch k-Nearest-Neighbor Regressor, and I took an equal-weighted approach for calculating my final predictions, using all of the similar active/inactive minute values and taking the average. Using the sklearn's kNN model takes into account the distance of each series from the series that we are attempting to predict and weight closer sequences higher than farther ones. Other models that we intend to test out include a linear SVM, decision trees (Alexa