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
We've submitted the paper, and now we are looking to move forward into the second paper. There are a few experiments we'd like to run:
1. Using Dynamic Time Warping to find the nearest 0/1 sleep sequences and then use features from similar series in predictions
2. Using Agglomerative Hierarchical clustering with #1
3. Bringing a third "Phase" of Fitbit activity data to see if we can apply our inactive-minutes concept into
4. Experiment to see if we can find a single model that works well across all three phases.
5. As a stretch goal: introduce an ANN model and see if it can outperform our previous best models, which were random forest.
Going forward, we are going to refer to the first "Phase" as "A", after the Actilogger devices used to collect them, "P" to refer to the second series, collected with Phillips devices, and "F" to refer to the new Fitbit data being incorporated.
1. Using Dynamic Time Warping to find the nearest 0/1 sleep sequences and then use features from similar series in predictions
2. Using Agglomerative Hierarchical clustering with #1
3. Bringing a third "Phase" of Fitbit activity data to see if we can apply our inactive-minutes concept into
4. Experiment to see if we can find a single model that works well across all three phases.
5. As a stretch goal: introduce an ANN model and see if it can outperform our previous best models, which were random forest.
Going forward, we are going to refer to the first "Phase" as "A", after the Actilogger devices used to collect them, "P" to refer to the second series, collected with Phillips devices, and "F" to refer to the new Fitbit data being incorporated.
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