The data set consists of 166 nodes (pipe junctions) and measurement of the Chlorine concentration level at all these nodes during 15 days (one measurement for every 5 minutes, a total of 4310 time ticks).
The variables are as follows:
data.frame with the following variables:
class: Corresponding class level of “ChlorineConcentration” curves with 3 classes with 1000, 1000 and 2307 observations per class respectively.
sample:Factor variable. In TSC database, the first 467 values (
sample=train) are used for training sample and the rest of 3840 (
sample=test) for testing.
fdata class object with with n=930 curves (per row) in 930 discretization points (per column).
This dataset was defined in a PhD thesis by Lei Li (Carnegie Mellon University). It was produced by EPANET that models the hydraulic and water quality behavior of water distribution piping systems. EPANET can track, in a given water network, the water level and pressure in each tank, the water flow in the pipes and the concentration of a chemical species (Chlorine in this case) throughout the network within a simulated duration.
Li, L. (2011). Fast algorithms for mining co-evolving time series (No. CMU-CS-11-127). CARNEGIE INST OF TECH PITTSBURGH PA DEPT OF COMPUTER SCIENCE. http://www.cs.cmu.edu/~leili/pubs/leili-thesis.pdf
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