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
Alexa is not going to continue working on the project throughout the summer, but Gina and I intend to branch out based on what we've done so far. In order to do so, we have set a few objectives for ourselves:
1. Tie up some loose ends from Alexa and I's initial project
2. Complete 1-2 papers, since we were not able to get our research during the year to that point
3. Push the "Nearest Sequences" concept farther by associating data from farther back with a given night or day period we are predicting - as far back as we can go without having to use baseline days in the experiment while not losing any data. 3. Explore the built-in sleep detection methods that are automatically classified by the Actigraph's software, and how they compare to Dr. Skornyakov's sleep/wake algorithm and the inactive minutes method we've used previously.
One of the variables we addressed when deciding to proceed with the "k-nearest sequences" approach was the value of k. Since it is usually fairly arbitrary in choosing, we hoped to be able to find something more concrete to help us find out what the optimal groupings that produced the best results may be, without overfitting our model. One potential we saw was using Agglomerative Hierarchical Clustering to find "best clusters".
1. Tie up some loose ends from Alexa and I's initial project
2. Complete 1-2 papers, since we were not able to get our research during the year to that point
3. Push the "Nearest Sequences" concept farther by associating data from farther back with a given night or day period we are predicting - as far back as we can go without having to use baseline days in the experiment while not losing any data. 3. Explore the built-in sleep detection methods that are automatically classified by the Actigraph's software, and how they compare to Dr. Skornyakov's sleep/wake algorithm and the inactive minutes method we've used previously.
One of the variables we addressed when deciding to proceed with the "k-nearest sequences" approach was the value of k. Since it is usually fairly arbitrary in choosing, we hoped to be able to find something more concrete to help us find out what the optimal groupings that produced the best results may be, without overfitting our model. One potential we saw was using Agglomerative Hierarchical Clustering to find "best clusters".
Comments
Post a Comment