blob: 4396d62df7f8c5f6d707fe53b3437ca433740a0e [file]
#-------------------------------------------------------------
#
# Licensed to the Apache Software Foundation (ASF) under one
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# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
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# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
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#
#-------------------------------------------------------------
# Returns the symmetric means absolute percentage error between the two inputs
#
# Monash Time Series Forecasting Archive
# Rakshitha Godahewaa, Christoph Bergmeira, Geoffrey I. Webba, Rob J. Hyndmanb,
# Pablo Montero-Mansoc
#
# Another Look at Measures of Forecast Accuracy, R. J. Hyndman and A. B. Koehler, 2006.
#
# INPUT:
# --------------------------------------------------------------------------------
# X First Matrix to compare
# Y Second Matrix to compare
# P Quantiles to extract as well if empty matrix not calculated
# --------------------------------------------------------------------------------
#
# OUTPUT:
# -----------------------------------------------------------------------------------------------
# Z The symmetric mean absolute percentage error
# Q Quantiles calculated
# -----------------------------------------------------------------------------------------------
m_smape = function(Matrix[Double] X, Matrix[Double] Y, Matrix[Double] P = matrix(0, rows=0, cols=0))
return (Matrix[Double] Z, Matrix[Double] Q) {
denom = (abs(X) + abs(Y)) / 2
SAPE = abs(X - Y) / denom
Z = as.matrix( mean(SAPE))
Q = flattenQuantile(SAPE, P)
}