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I am using the forecast package in R to implement ARIMA Models. I am using one variable, the data is from 2016 to 2019 and it is monthly. When I look at the residual check, the 2016 residuals do not look correct. There is a flat like for 2016 and this is having an impact on tuning the model. I am looking for advice on how to fix this issue.

Data

auto.arima output

Residual Plot

fit.arima <- auto.arima(RevenueDataTSTest)

checkresiduals(fit.arima)

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  • $\begingroup$ How are you tuning the model and what exactly is the "impact" on it? $\endgroup$ – whuber♦ Nov 24 '20 at 17:02
  • $\begingroup$ I have not done any tuning at this point. 2016 residuals should not be 0. If I removed 2016, and used 2017 to 2019, 2017 residuals are 0. I'm trying to understand why this is happening. $\endgroup$ – Danielle DeLosa Nov 24 '20 at 18:34
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    $\begingroup$ The first 12 months are used to initialize the seasonal terms. $\endgroup$ – whuber♦ Nov 24 '20 at 18:44

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