[wenc]

This is to be expected, because pure time-series models (Holt-Winters, ARIMA, etc.) only capture behavior of historical data (autoregressive, i.e. yₖ = f(yₖ₋₁, yₖ₋₂, ...)). If the patterns of interest aren't primarily time-based patterns, then time series models wouldn't be predictive.

In my experience, the time-series models that are reliably predictive typically aren't purely autoregressive but contain exogeneous variables as well (i.e. yₖ = f(yₖ₋₁, yₖ₋₂, ..., xₖ, xₖ₋₁, xₖ₋₂...), like ARX models). These models don't only capture relationships to historical patterns but to other driving/causal variables.

Price forecasts are often modeled as time-series models, but this assumes that prices only have time-based patterns which is often not true. In my domains of interest for instance, time has tangible yet limited effect on prices -- prices are driven more by variables like weather and certain types of market activity.

[0x008]

totally agree. there is more than enough research published that confirms that simply predicting the last value is on average only marginally worse than most time series models.