What Data Is Needed to Assess Event Filtering?

Direct answer

To assess event filtering, you need data that lets you (1) define the filtering rule, (2) identify where the event information came from, (3) verify timing accuracy, and (4) evaluate data quality. Because the term “event filtering” is used differently across tools and providers, the minimum data set should make your filtering logic unambiguous before you consider outcomes.

Mechanism and definition: what “event filtering” is

Event filtering is a process that selects or excludes events (often scheduled releases) based on rules applied to event attributes and timing. Assessing it requires separating stable mechanics from variable conditions.

Start with definition inputs:

• Filtering criteria: which event types/categories are included or excluded, and any thresholds used.
• Event attributes used by the filter: for example, event name/category, currency/region tags, and whether an event has an expected/previous value.
• The decision time reference: when the system applies the filter (e.g., before the release, within a window, after a publication).

Then capture provenance:

• Source of the event calendar/feed (the provider or dataset name).
• How the data is delivered (file, API feed, scraped page, etc., at a high level).
• Any documented mapping rules (for example, how the provider assigns currencies or countries to events).

Finally, timing data:

• Original scheduled timestamp and the event’s time zone.
• Publication timestamp (if available) and any “last updated” timestamp.
• Update frequency for the feed that supplies events.

Evidence or example: a checklist for the data you should be able to state

A clear assessment usually requires you to answer four questions with concrete data:

1. What exactly is being filtered?

• List the event fields used by the rule and confirm they exist consistently across records.

2. Where did the event data come from?

• Provide the dataset/provider identity and, if possible, the update mechanism.

3. Does the timing align with the decision window?

• Convert timestamps to a single time basis you choose, and record the time-zone handling method.
• Note whether you are using scheduled times only, actual publication times, or both.

4. Is the data reliable enough for the intended use?

• Verify completeness: missing fields, malformed timestamps, or absent currency/region tags.
• Verify consistency: the same event should not appear under conflicting identifiers without a documented reason.
• Verify change tracking: use “last updated” information to detect retroactive edits.

Assumption example (state it explicitly): if you apply a window “from 30 minutes before to 30 minutes after,” you must specify whether timestamps are scheduled or actual publication times, and which time zone conversion you used.

AFVinkpunten (control checklist)

• Klarere filtering rule described as inputs + decision time reference.
• Evidence of provenance: identify the event-feed/provider and update behavior.
• Rode vlaggen checked: missing timestamps, timezone ambiguity, inconsistent identifiers, retroactive edits without versioning.
• Klaarcriterium: you can reproduce which events pass/fail the filter using the stated data.

Limitations and risks (including failure modes)

Several limitations can break event filtering assessments even when the filtering logic looks correct:

• Timing mismatch failure mode: using scheduled times when actual publication differs can shift which events are included during your decision window.
• Time-zone and formatting ambiguity: inconsistent time-zone handling can cause apparent “correctness” to fail under different interpretations.
• Feed versioning and retroactive edits: if events are corrected after publication without accessible history, past assessments may not be reproducible.
• Quality drift failure mode: missing fields or provider mapping changes can silently alter which events match criteria.

More general uncertainty applies: outcomes vary with market conditions, costs, execution, and jurisdiction. Historical relationships do not establish future results, and no real-time market data is assumed here.

Verification or next question

To verify your assessment independently, ensure you can reproduce the filter’s pass/fail results from the same event records:

• Keep a small sample dataset with the fields used by the rule (attributes, timestamps, time-zone basis, and provider identity).
• Record assumptions (scheduled vs actual time, window boundaries, and conversions).
• Re-run the filtering logic after any feed update to see whether results change.

If you want the next step, specify your intended filtering rule (criteria + decision window) and the event-feed fields you plan to use, then compare whether you can state provenance and timing details without ambiguity.