Improving a Time-Sensitive Prediction System
A system is designed to predict the next likely location of a delivery drone based on a summary of its last 5 recorded GPS coordinates. The current system calculates a simple average of these 5 coordinates, giving equal importance to each one. This has led to poor predictions when the drone makes a sudden, sharp turn, as the summary is too heavily influenced by the older coordinates from before the turn.
Critique the current method and propose a specific modification to how the system assigns importance to the 5 coordinates to make its predictions more responsive to recent changes in direction. Justify your proposed modification.
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Ch.2 Generative Models - Foundations of Large Language Models
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A system is designed to compute a summary value at each time step by calculating a weighted average of the last four items in a sequence. The core design principle is that items closer to the current time step should have a greater influence on the summary than items from further in the past. If the weights are applied to the items in order from oldest to most recent, which of the following sets of coefficients best implements this principle?
Improving a Time-Sensitive Prediction System
Rationale for Increasing Weights in a Moving Average