The well known definition of a long term forecasting model is based on the length of the forecast horizon. In case of long term, it is usually agreed to be in the order of years (ahead of the most recent time of data).
I have observed some academic forecasting papers (will share if required), particularly those relying on ML techniques such as LSTM for high temporal resolution building energy prediction quoting their model as long term forecasting while in fact their
- forecast horizon is just months ahead;
- training period is in the order of years.
Is this just a confusion among researchers or does it show a tendency of ML researchers dealing with high granular data to just pick up few months and demonstrate that their model works fine?
I do not intend any offence to any academic community. Please share your insights.