Is it correct to make a conclusion as to whether a model is best for weekly or daily forecasting by comparing the root mean square errors?

by Stoner   Last Updated September 12, 2019 01:19 AM

I am performing daily and weekly forecasts for 28 days and 4 weeks respectively.

Once I have used the same model to obtain the respective forecasts an root mean square errors (RMSE), I will like to make a conclusion based on just these results, as to whether the model is more suited for daily or weekly forecasting.

Denote the daily forecasts RMSE to be $RMSE_d$ and weekly forecasts RMSE to be $RMSE_w$. I then arrived at the following formulas and proposition:

$$RMSE_d = \sqrt{\frac{1}{n_i} \sum_j (y_{ij} - \hat{y}_{ij})^2}$$

$$RMSE_w = \sqrt{\frac{1}{n} \sum_i (y_i - \hat{y}_i )^2}$$

where $n = \sum_i n_i , y_i = \sum_j y_{ij}$, for weeks $i = 1, \dots , n$ and days $j=1,\dots,7$.

Then for comparing both metrics, I suppose that $y_i \approx 7 y_{ij}$ for each $i$. Am I right to conclude that $RMSE_w \approx 7^2 RMSE_d$?

I can't seem to find any related online literature nor on this site, but I apologize if there has indeed been a post like this before..

Some insights will be deeply appreciated!

Related Questions

Pandas Time Series DataFrame Missing Values

Updated July 01, 2016 08:08 AM

Time Series One Step Ahead vs N-Step Ahead

Updated April 24, 2018 07:19 AM