Traditional time-series forecasting often focuses only on minimizing
prediction errors, ignoring the specific requirements of real-world
applications that employ them. This paper presents a new training methodology,
which allows a forecasting model to dynamically adjust its focus based on the
importance of forecast ranges specified by the end application. Unlike previous
methods that fix these ranges beforehand, our training approach breaks down
predictions over the entire signal range into smaller segments, which are then
dynamically weighted and combined to produce accurate forecasts. We tested our
method on standard datasets, including a new dataset from wireless
communication, and found that not only it improves prediction accuracy but also
improves the performance of end application employing the forecasting model.
This research provides a basis for creating forecasting systems that better
connect prediction and decision-making in various practical applications.

Dieser Artikel untersucht Zeitreisen und deren Auswirkungen.

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