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HOSS_JFL
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A model needs to be fit to a short time series of 3 annual observations of sales (2018 to 2020), e.g. 12, 14 and 13. The next 2 years (annual values) need to be forecasted bsaed on this data.

Is there any theoretical benefit from using the monthly observations instead of the annual data, eg sales in 2018-Jan, 2018-Feb, ..., 2020-Dec?

The number of data point increases, which means that models with more parameters can be fit. However, does this help for the given task? There is for instance no need for seasonal effects because only annual forecasts are relevant anyway.

Would there be a theoretical benefit for increasing the data frequency in this given 3-year period to weeks, days or even minutes? Or is there an argument that for annual forecasts basically filling the gaps by measuring more often within a year cannot increase forecasting accuracy?

A model needs to be fit to a short time series of 3 annual observations (2018 to 2020), e.g. 12, 14 and 13. The next 2 years (annual values) need to be forecasted bsaed on this data.

Is there any theoretical benefit from using the monthly observations instead of the annual data, eg 2018-Jan, 2018-Feb, ..., 2020-Dec?

The number of data point increases, which means that models with more parameters can be fit. However, does this help for the given task? There is for instance no need for seasonal effects because only annual forecasts are relevant anyway.

Would there be a theoretical benefit for increasing the data frequency in this given 3-year period to weeks, days or even minutes? Or is there an argument that for annual forecasts basically filling the gaps by measuring more often within a year cannot increase forecasting accuracy?

A model needs to be fit to a short time series of 3 annual observations of sales (2018 to 2020), e.g. 12, 14 and 13. The next 2 years (annual values) need to be forecasted bsaed on this data.

Is there any theoretical benefit from using the monthly observations instead of the annual data, eg sales in 2018-Jan, 2018-Feb, ..., 2020-Dec?

The number of data point increases, which means that models with more parameters can be fit. However, does this help for the given task? There is for instance no need for seasonal effects because only annual forecasts are relevant anyway.

Would there be a theoretical benefit for increasing the data frequency in this given 3-year period to weeks, days or even minutes? Or is there an argument that for annual forecasts basically filling the gaps by measuring more often within a year cannot increase forecasting accuracy?

Source Link
HOSS_JFL
HOSS_JFL
  • 417
  • 3
  • 13

Is there a theortical benefit from increasing the frequency of a time series, eg monthly instead annual data

A model needs to be fit to a short time series of 3 annual observations (2018 to 2020), e.g. 12, 14 and 13. The next 2 years (annual values) need to be forecasted bsaed on this data.

Is there any theoretical benefit from using the monthly observations instead of the annual data, eg 2018-Jan, 2018-Feb, ..., 2020-Dec?

The number of data point increases, which means that models with more parameters can be fit. However, does this help for the given task? There is for instance no need for seasonal effects because only annual forecasts are relevant anyway.

Would there be a theoretical benefit for increasing the data frequency in this given 3-year period to weeks, days or even minutes? Or is there an argument that for annual forecasts basically filling the gaps by measuring more often within a year cannot increase forecasting accuracy?