TimeSeries

TimeSeries / Autocorrelate / 0.2.0

README.md

Autocorrelate

Autocorrelate finds the dataset’s cross-correlation with itself to discover periodic components while filtering out non-periodic noise.


Table of Contents


Inputs

This algorithm has a JSON object input, as well as array input formatting.

JSON Format

{  
   "data":Double[]
}
{  
   "data": [1,2,3,4,3,4,6,7,5,4,2,1,1]
}
  • data - (required) - The timeseries input data you wish to Autocorrelate, this algorithm assumes the values are evenly spaced. (dx(1) == dx(n))

Array Format

There is a single array input format for this algorithm.

Basic format

data: Double[]
[1,2,3,4,3,4,6,7,5,4,2,1,1]
  • data - (required) - The timeseries input data you wish to Autocorrelate, this algorithm assumes the values are evenly spaced. (dx(1) == dx(n))

Outputs

This algorithm has json output for json input, and array output for array input.

JSON Format

{  
   "autoCorrelated": Double[]
}
{  
   "autoCorrelated": [4,3,2]
}
  • autoCorrelated - The autocorrelated result from the partially periodic input data.

Array Format

autoCorrelated: Double[]
[1,2,3,4,3,4,6,7,5,4,2,1,1]
  • autoCorrelated - The autocorrelated result from the partially periodic input data.