ts object not recognised in hybridModel of forecastHybrid package

Question

Data is something like this:

df <- tribble(
    ~y,~timestamp

    18.74682, 1500256800,

    19.00424, 1500260400,

    18.86993, 1500264000,

    18.74960, 1500267600,

    18.99854, 1500271200,

    18.85443, 1500274800,

    18.78031, 1500278400,

    18.97948, 1500282000,

    18.86576, 1500285600,

    18.55633, 1500289200,

    18.79052, 1500292800,

    18.74790, 1500296400,

    18.62743, 1500300000,

    19.04696, 1500303600,

    18.97851, 1500307200,

    18.70956, 1500310800,

    18.92302, 1500314400,

    18.91465, 1500318000,

    18.61556, 1500321600,

    19.03535, 1500325200 )

I'm trying to apply hybridModel on timeseries data to perform ensemble.Below is my code:

library(tidyquant)

library(forecast)

library(timetk)

library(sweep)

library(forecastHybrid)

df <- mutate(df, timestamp = as_datetime(timestamp))

tk_ts_df <- tk_ts(df, start = 1, freq = 3600, silent = TRUE)

fit <- hybridModel(tk_ts_df)

On fitting timeseries object tk_ts_df (ts object) to hybridModel; it's giving error : "The time series must be numeric and may not be a matrix or dataframe object."

But on link: https://cran.r-project.org/web/packages/forecastHybrid/vignettes/forecastHybrid.html

It's clearly mentioned : The workhorse function of the package is hybridModel(), a function that combines several component models from the “forecast” package. At a minimum, the user must supply a ts or numeric vector for y

Please suggest what I'm doing wrong.

Answer 1

The "forecastHybrid" requires that the input timeseries is a numeric vector or ts type. While the "timekit" package does return a ts object, it also adds additional attributes that are not in regular ts objects so input checks failed. See discussion here. and the fixing commit here.

The latest version from Github incorporating the fix can be downloaded with devtools::install_github("ellisp/forecastHybrid/pkg")