You have a ts object named my_ts with a frequency of 12. Which R code correctly performs an additive decomposition?

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BlackTom AI · Accepted Answer

Question restatement: You have a ts object named my_ts with a frequency of 12. Which R code correctly performs an additive decomposition?

Option 1: my_ts %>% tslm(type = "additive"). Here, the pipe operator (%) is used to pass my_ts to tslm, but tslm does not take a type argument in this form, and the correct usage for additive seasonality with a ts object requires a formula interface like y ~ trend + season. This option is incorrect because it misuses both the function interface and the argument shape.

Option 2: tsl(my_ts, model = "additive"). This option uses tsl, which is not a standard function for additive decomposition with a ts object in the common forecasting workflows. The intended function for regression-based decomposition is tslm, and tslm uses a formula interface rather than a model argument like this. Therefore, this is incorrect due to wrong function and incorrect argument structure.

Option 3: tslm(my_ts, type = "additive"). Here, tslm is being called with a ts object as the response, but the function expects a formula (e.g., y ~ predictors) rather than a bare ts object plus a type parameter. Additionally, there is no type = "additive" argument for tslm in typical usage. This makes the option incorrect due to improper specification of the model interface.

Option 4: tslm(my_ts~trend+season). This uses the correct formula interface for tslm, with my_ts as the response variable and trend + season as predictors, which corresponds to an additive decomposition framework (trend and seasonal components) for a time series. Given a ts object with frequency 12, the seasonal component is captured via season. This is the correct form.

Summary of reasoning: Options 1–3 misuse the function interface or arguments for tslm and do not specify the model with a proper formula. Option 4 adheres to the standard tslm usage for additive decomposition by modeling my_ts as a function of trend and season. The frequency-12 context aligns with using seasonality in the formula.

You have a ts object named my_ts with a frequency of 12. Which R code correctly performs an additive decomposition?

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