Ic3fr0g
commented
7 years ago Follow up question, how does one go calculating metrics like MASE or MAPE using your tool ? Here was my naive approach to reconcile the two packages and to calculate MASE :
m <- prophet(df,yearly.seasonality = T,weekly.seasonality = T)
# m$growth = "linear"
future <- make_future_dataframe(m, periods = 365)
fp <- predict(m, future)[,c('yhat')]
fp_fit <- ts(fp[1:(length(fp)-364)],frequency = 365)
fp_hist <- msts(m$history[,'y'],seasonal.periods = c(7,365.25))
fp_mean <- ts(fp[length(fp)-364:0],frequency = 365,start = end(fp_hist))
fprophet <- list(mean = fp_mean,fitted = fp_fit,x = fp_hist)
class(fprophet) <- "forecast"
accuracy(fprophet)
# ME RMSE MAE MPE MAPE MASE ACF1
#Training set 40.84288 252152.8 186680.3 NaN Inf 0.3441319 0.4190127
# Which is the same thing as doing -
# d - An integer indicating the number of lag-1 differences to be used
# for the denominator in MASE calculation. Default value is 1 for non-seasonal
# series and 0 for seasonal series.
# D - An integer indicating the number of seasonal differences to be used
# for the denominator in MASE calculation. Default value is 0 for non-seasonal
# series and 1 for seasonal series.
D = 1
d = 0
nd <- diff(fp_hist,lag = round(tsp(fp_hist)[3L]), differences = D)
scale <- mean(abs(nd), na.rm = TRUE)
res <- fp_hist - fp_fit
mase <- mean(abs(res/scale), na.rm=TRUE)
# mase = 0.3441319Is this valid ? Or is there another way to validate the accuracy metrics of the prophets models ? I could upload the data for df if it interests you. I suppose it is accurate only for linear growth?
slavakurilyak
bletham
zippeurfou
sriramkraju
AntonioBlago
ivonnics
First off, congratulations on creating a wonderful tool for forecasting. I was wondering if there could be a functionality within prophet that calculates the
accuracyon the lines of Rob J Hyndman'sforecastpackage. I for one would love to see this functionality.