I want to analyse a time series that in genereal seems to follow a linear trend but at the same time seems to be influenced from some kind of multiplicative effects. A simple example would be a time series generated by the following code:

x <- ts(1:30)
x[c(7, 14, 21, 28)] <- 0.5 * x[c(7, 14, 21, 28)]
x <- x + rnorm(30, mean = 0, sd = 0.05 * 1:30)
plot(x, type = "l")

My question is how to best estimate this time series within a regression framework? Obviously a simple linear model would underestimate the multiplicative effect in recent days while a simple log-linear model would estimate a exponential growth instead of a linear one. Is there a simple way to combine both effects within a single regression approach or do I have to do some kind of stepwise estimation?

I would appretiate any thoughts / comments!


1 Answer 1


Your error term is a bit strange because it is multiplicative to time and not to seasonality and trend. I would reconsider if this should be expected in your real data. However, what you have seems close enough to a multiplicative time series to get at least decent estimates:

$Y_t = T_t \cdot S_t \cdot e_t$

Such a time series can be decomposed easily if you define an appropriate frequency for the time series. With stats::decompose:

x <- ts(1:30, frequency = 7) #note the frequency
x[c(7, 14, 21, 28)] <- 0.5 * x[c(7, 14, 21, 28)]
x <- x + rnorm(30, mean = 0, sd = 0.05 * 1:30)

y <- decompose(x, type = "multiplicative")

resulting plot

fit_trend <- lm(y$trend ~ seq_along(y$trend))
#    Estimate   Std. Error      t value     Pr(>|t|) 
#9.182801e-01 1.060280e-02 8.660734e+01 2.253371e-29 

You could also check out the forecast package which offers more flexible and automated decomposition of time series.

  • $\begingroup$ The error was only incorporated for sampling purposes and does not mean anything special. I think decomposition would not work for me because the "drops" are due to public holidays which are not regular but can be given as an external regressor. $\endgroup$ May 22, 2018 at 13:39
  • $\begingroup$ There are decomposition methods that consider moving holidays. $\endgroup$
    – Roland
    May 22, 2018 at 14:36

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