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The classical Box-Jenkins reading is that the PACF cut-off suggests the AR order p and the ACF cut-off suggests the MA order q, with d set by how many differences the series needs to become stationary. Parameter count then dictates how much history you need: roughly ten observations per parameter, and at least two or three full seasonal cycles when a seasonal term is included.
Order identification
p = last significant PACF lag; q = last significant ACF lag; d = differences for stationarity; parameters ~ p + q + seasonal terms + constant
Automatic order selection by AICc is a strong default and usually beats manual reading. Use ACF and PACF plots to sanity-check its answer rather than to replace it.
Apply a unit-root test such as KPSS and difference until it passes. Over-differencing shows up as a large negative spike at lag 1 in the ACF and inflates forecast variance.