Comparing forecasts across markets sounds simple, but a meaningful comparison requires more than placing percentages in a table. Different markets may have different levels of volatility, different amounts of available information, and different settlement rules. A careful review of HQ Oracle forecasts should therefore separate the quality of the prediction from the difficulty and structure of the market.
HQ, or Hylaq Quantum, combines frontier AI with quantum provenance from IBM hardware and publishes a signed forecast track record at /proof. That public record gives you a starting point for checking forecasts rather than relying only on a current dashboard or a retrospective summary.
Start by defining what “market” means
Before comparing results, write down the unit being compared. A market might refer to a geographic region, an asset class, an event category, or another clearly defined group of forecasts. Do not assume that two labels describe equivalent prediction tasks.
For each group, capture:
- The outcome: What exactly counts as correct, incorrect, unresolved, or cancelled?
- The timestamp: When was the forecast issued and when did the relevant information become available?
- The horizon: How far into the future was the forecast looking?
- The information set: Were the forecasts made under similar data and evidence conditions?
- The sample: How many forecasts are included, and are they all from the same period?
This prevents a short-term, information-rich forecast from being treated as directly interchangeable with a long-range forecast made under uncertainty.
Align the forecast horizon and outcome rules
Time horizon is one of the most important controls. Compare near-term forecasts with other near-term forecasts, and longer-range forecasts with similar horizons. If alignment is impossible, report the difference instead of presenting one overall number as though the tasks were identical.
Outcome rules also need to be consistent. Decide in advance how to handle events that are postponed, revised, ambiguous, or never resolve. If one market allows “unknown” outcomes while another forces a binary result, their accuracy rates may not be comparable.
When reviewing HQ Oracle, preserve the original forecast and its issue time. Do not replace an earlier forecast with a later update and then describe the result as an evaluation of the original prediction. The public signed record is especially useful here because provenance and timing are part of the evidence being reviewed.
Use more than a win-loss percentage
A simple accuracy percentage is easy to understand, but it can conceal important differences. A system that makes cautious forecasts may have a different accuracy profile from one that makes highly confident forecasts. Consider reporting several measures where the data supports them:
- Accuracy: The proportion of resolved forecasts that were correct.
- Confidence calibration: Whether stated probabilities corresponded to observed frequencies.
- Log loss or another probabilistic score: A way to reward useful probability estimates and penalize misplaced confidence.
- Coverage: How many eligible questions received a forecast rather than being omitted.
- Performance by horizon: Results separated by short-, medium-, and long-range forecasts where appropriate.
Use the same scoring method for every market. If a market has too few resolved forecasts for a stable conclusion, say so plainly. A small sample can be informative, but it should not be presented as a definitive ranking.
Check provenance before interpreting performance
Performance figures are more credible when readers can inspect how the forecast was generated and recorded. With HQ, review the signed track record and the available quantum provenance rather than treating the phrase “quantum AI” as proof of superior predictive accuracy. Quantum hardware provenance can document an aspect of the system’s process; it does not, by itself, establish that every forecast will be correct.
Keep three questions separate:
- Was the forecast recorded before the outcome?
- Can the forecast and its relevant metadata be verified?
- Did the forecast perform well under a defined scoring rule?
These questions address timing, authenticity, and predictive performance respectively. They should not be collapsed into one claim.
Build a transparent comparison table
A useful comparison table might include the market label, number of forecasts, date range, forecast horizon, scoring method, accuracy, calibration measure, and unresolved cases. Add a link or reference to the underlying record whenever possible. Showing the denominator is essential: “eight correct forecasts” means something different with ten observations than with one hundred.
For a repeatable workflow, export or record the same fields for every market, then lock the evaluation rules before reviewing the outcomes. If you are preparing a separate data-loading workflow, you can try Loadit, but keep that operational step separate from the forecast evaluation itself.
Interpret differences cautiously
If one market performs better than another, several explanations are possible. The market may be easier, the sample may be larger, the outcomes may be less volatile, or the available information may be more complete. A difference in scores is a result to investigate, not automatic proof that HQ Oracle is inherently stronger in one domain.
The most useful conclusion is usually specific: which markets were compared, over what period, at what horizon, under which scoring rules, and with what evidence. That approach makes HQ Oracle forecasts easier to audit and gives readers a fairer basis for deciding how much weight to place on them.