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The inclinometer function of the AG classifies time as sitting, lying and upright.
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When shapeDTW is used as a distance measure in a nearest neighbor classifier (NN-shapeDTW) to classify time series, it beats DTW on 64 out of 84 UCR time series datasets, with significantly improved classification accuracies.
They classify time using three temporal groupings; peak times; off-peak daytime; and, off-peak night-time hours.
The elements used by pastoralists to classify time (pastoral calendar), grazing lands (pastoral units) and the herd management rules associated with each of these elements also were characterized.
The AP and CAM classify time as sitting/lying, standing and activity.
Percentages of correctly classified time were not distributed normally; therefore, median values and 25th and 75th percentile were calculated.
However, we are not aware of any method that used these models for classifying time series data.
It is important to point out that the number of correctly classified time series increases as the time series length increases.
We classified time points into four seasons as follows; spring (March to May summer June to to August); autumn (September to November); and winter (December to February).
Future work is needed to refine the models so that they can classify time activity patterns in a wide range of built environments.
The reason for the popularity of functional clustering is that it can classify time series data into different classes without requiring a priori knowledge of data.
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