blob: 8eb155810b5f10a7dd9d079df34f86f4b9309a47 [file]
#-------------------------------------------------------------
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# 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
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
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#
#-------------------------------------------------------------
# Returns the modified 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
#
# INPUT:
# --------------------------------------------------------------------------------
# X First Matrix to compare
# Y Second Matrix to compare
# --------------------------------------------------------------------------------
#
# OUTPUT:
# -----------------------------------------------------------------------------------------------
# Z The modified symmetric mean absolute percentage error
# -----------------------------------------------------------------------------------------------
m_msmape = function(Matrix[Double] X, Matrix[Double] Y, Matrix[Double] P = matrix(0, rows=0, cols=0),
Double eps = 1e-10) return (Matrix[Double] Z, Matrix[Double] Q) {
top = abs(X - Y)
denom_1 = abs(X) + abs(Y) + eps
denom_2 = 0.5 + eps
denom = max(denom_1, denom_2) / 2
MSAPE = top / denom
Z = as.matrix(mean(MSAPE))
Q = flattenQuantile(MSAPE, P)
}