Statistical vs. Deep Learning forecasting methods
Seems like these guys just wasted $11k to erroneously claim, “deep learning bad! Simple is better!”<p>There’s definitely use for these classical, model-based methods, for sure. But a contrived comparison claiming they’re king is just misinformation.<p>Eg, here are a number of issues with classical techniques where dl succeeds (‘they’ here refers to classical techniques):<p>- they often don’t support missing&#x2F;corrupt data<p>- they focus on linear relationships and not complex joint distributions<p>- they focus on fixed temporal dependence that must be diagnosed and specified a priori<p>- they take as input univariate, not multiple interval, data<p>- they focus on one-step forecasts, not long time horizons<p>- they’re highly parameterized and rigid to assumptions<p>- they fail for cold start problems<p>A more nuanced comparison would do well to mention these.