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
Continuing work on the paper, we decided to demonstrate our results by including a heat-map that displayed how error changed when we modified both the length of the series and the value of k, using values from 5 to N, increasing by 5. This was not very telling when we viewed each and every value, but a trendline became apparent when we excluded the bottom 3 k values and bottom 3 sequence lengths. It appeared that the best results were obtained when both sequence length and k were low and when both were high. This is a result that we want to look into more in the future.
This week will be spent writing and editing the paper. Once we've submitted it, we will regroup and establish our goals for the final three weeks of the summer.
This week will be spent writing and editing the paper. Once we've submitted it, we will regroup and establish our goals for the final three weeks of the summer.
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