Structural Break Real-Time: may infer use prior completed-series state?

Competition ADIA Lab
1

For the current Structural Break Real-Time competition, may `infer()` maintain a **forward-only summary of series that have already been completely processed** and use that summary when scoring later series?

The proposed state would:

- run with parallelism 1;

- contain only clean-history summaries and prediction trajectories from previously completed test series;

- never use test labels, data files, a future point of the current series, or the current series’ final horizon;

- yield each prediction before consuming the next online point; and

- never revise any earlier prediction.

Or must every series’ predictions be independent of all other test series?

If forward-only cross-series state is allowed, is the series order guaranteed to be stable/deterministic in public and out-of-sample scoring?

2

Hello Allomancer,

Yes, that’s totally doable. You can detect when a time series is over after the for loop:

def infer(...):
    yield

    for x_historical, x_online in datasets:
        for point in x_online:
            yield result

        ...  # previous timeseries just ended, record a summary

Even though the order is deterministic, making it easier for participants to debug their code, we never mentioned one. Therefore, your code should function the same way whether the order is the same or random.

For the Out-of-Sample, your model will only be run once on a brand new dataset. I’m not sure if how a stable ordering would be useful in that case.