[SOLVED] Time Series Classification Part 1: Feature Creation/Extraction

Time Series Classification Part 1: Feature Creation/Extraction
An interesting task in machine learning is classification of time series. In this problem,
we will classify the activities of humans based on time series obtained by a Wireless
Sensor Network

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(a) Download the ARM data from: https://archive.ics.uci.edu/ml/datasets/
Activity+Recognition+system+basedton+Multisensor+data+fusion+%28AReM
%29. The dataset contains 7 folders that represent seven types of activities. In
each folder, there are multiple files each of which represents an instant of a human
performing an activity.1 Each file contains 6 time series collected from activities
of the same person, which are called avg rssl2, var rssl2, avg rssl3, var rss13,
vg-rss23, and ar-rss23. There are 88 instances in the dataset, each of which con
tains 6 time series and each time series has 480 consecutive values.
(b) Keep datasets 1 and 2 in folders bending and bending 2, as well as datasets 1
2. and 3 in other folders as test data and other datasets as train data.
(c) Feature Extraction
Classification of time series usually needs extracting features from them.
problem, we focus on time-domain features.
i. Research what types of time-domain features are usually used in time series
classification and list them (examples are minimum, maximum, mean, etc).
i. Extract the time-domain features minimum. maximum. mean. median. stan-
dard deviation. first quartile, and third quartile for all of the 6 time series
in each instance. You are free to normalize/standardize features or use them
directly.?
Your new dataset will look like this
Instance mini
Ist quarte
3rd quarte
where, for example, 1st quarts, means the first quartile of the sixth time series
in each of the 88 instances.
ill. Estimate the standard deviation of each of the time-domain features you
extracted from the data. Then, use Pythons bootstrapped or any other
method to build a 90% bootsrap confidence interval for the standard deviation
of each feature.
iv. Use your judgement to select the three most important time-domain features
(one option may be min, mean, and max).

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