I'm not sure if this is intended behaviour. Consider the following snippet of code -
library(forecast)
x <- c(
0, 0, 0, 0, 0.00217764964493354, 0.00339032724317772, 0.00357374918778428,
0.00282328811130057, 0.00272679331678393, 0.0030360769697858,
0.00316665914235777, 0.00163300219677676, 0.00249817841157489,
0.00207838479809976, 0.00192104504850639, 0.00209700948212983,
0.00216356555603635, 0.00250983016815862, 0.0017474879860201
)
tsData <- ts(data = x, start = 2000, frequency = 1)
df <- data.frame(
x = x,
fittedets = fitted(forecast(ets(tsData), h = 7)),
fittedarima = fitted(forecast(auto.arima(tsData), h = 7))
)
df
x fittedets fittedarima
1 0.000000000 -6.997521e-07 0.000000000
2 0.000000000 -7.065016e-11 0.000000000
3 0.000000000 -7.133162e-15 0.000000000
4 0.000000000 -7.201966e-19 0.000000000
5 0.002177650 0.000000e+00 0.000000000
6 0.003390327 2.177430e-03 0.002007587
7 0.003573749 3.390205e-03 0.003125561
8 0.002823288 3.573731e-03 0.003294659
9 0.002726793 2.823364e-03 0.002602805
10 0.003036077 2.726803e-03 0.002513846
11 0.003166659 3.036046e-03 0.002798976
12 0.001633002 3.166646e-03 0.002919360
13 0.002498178 1.633157e-03 0.001505474
14 0.002078385 2.498091e-03 0.002303084
15 0.001921045 2.078427e-03 0.001916074
16 0.002097009 1.921061e-03 0.001771022
17 0.002163566 2.096992e-03 0.001933245
18 0.002509830 2.163559e-03 0.001994603
19 0.001747488 2.509795e-03 0.002313826
The actual values are 0 until the fifth value, while in case of both models, the fitted values are about 0 until the sixth value.
I would assume them to be approximately 0 for the first five values, like the x
column. Am I missing something basic?
It also has to do with the ARIMA model that auto.arima
is fitting to your data. If you look at the model that it's being fitted:
Series: tsData
ARIMA(1,0,0) with zero mean
Coefficients:
ar1
0.9219
s.e. 0.0638
sigma^2 estimated as 6.076e-07: log likelihood=108.59
AIC=-213.17 AICc=-212.42 BIC=-211.28
Remember that ARIMA stands for Autoregressive Integrated Moving Average, and the output tells us that only the AR part of the model was fitted, which makes it an AR(1) model:
y[t] = c + p1 * y[t-1]
With this equation you can get a sense of what happened here:
x fittedets fittedarima
1 0.000000000 -6.997521e-07 0.000000000
2 0.000000000 -7.065016e-11 0.000000000 # .9219 * 0 = 0
3 0.000000000 -7.133162e-15 0.000000000 # .9219 * 0 = 0
4 0.000000000 -7.201966e-19 0.000000000 # .9219 * 0 = 0
5 0.002177650 0.000000e+00 0.000000000 # .9219 * 0 = 0
6 0.003390327 2.177430e-03 0.002007587 # .9219 * .00217 = .002007
7 0.003573749 3.390205e-03 0.003125561 # .9219 * .00339 = .003125
You can also observe this behavior with a plot:
library(ggplot2)
fcast <- forecast(auto.arima(tsData), h = 7)
autoplot(fcast) +
autolayer(fitted(fcast))
For the ets model a similar thing happens, but I hope this made it clear why auto.arima
had such result. Next time you could explore more forecasting models that are included in the forecast
package.
Hope this helped!
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