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4.8.3 Recurrent Networks

Why Feedforward Networks Fall Short for Time Series

All networks discussed so far are feedforward — they map a fixed-size input to an output with no memory of previous inputs.

For time series problems, this is a fundamental limitation.

Example: Predict tomorrow’s stock market index y(t+1) from today’s economic indicators x(t).

Feedforward network:   x(t)  →  [Network]  →  y(t+1)

This uses only x(t). But if y(t+1) also depends on x(t−1), x(t−2), etc., the feedforward network has no access to that history.