协变量在tslm中不起作用

Question

I have seasonal time series data for weekly retail sales and am trying to use the tslm function of Hyndman's forecast package to fit a model with regressors in addition to trend and season.

The issue I'm running into is that when I build the tslm, before adding any regressors (only trend + season), I get a perfect fit (R^2 =1) on the training data!

A prefect fit is problematic because any additional covariate I add to the model (# of items being sold, distribution, etc.) have no impact on predictions (insignificant). Just looking at the data, I know these other regressors matter so I'm not exactly sure where I'm going wrong. Hoping somebody in the community can help me out.

Some information about the data I am using:

1. Dataset contains weekly data from 2014 - 2017
2. Training data contains 156 weekly observations (2014 - 2016)
3. Test data contains 48 observations in 2017

I am using weekly seasonality to build the time series:

ts.train <- ts(df.train$sales, freq=365.25/7)
m.lm <- tslm(ts.train ~ trend + season + items, data=df.train)
p.lm <- forecast(m.lm, 
               h=48,
               newdata=data.frame(items=df.test$items))

If I leave "items" out of the formula, the predictions do not change at all.

I appreciate any input and guidance!

Answer 1

Items probably has too many variables (if they are dummy variables), since you get a perfect fit. See: https://www.otexts.org/fpp2/useful-predictors.html

For example, you need only 6 dummy variables to account for 7 week days.