Taimoor Ahmad
—
First author of 2026 arXiv preprints on revision-aware time-series forecasting and certified freshness-aware retrieval
2
papers here
2
reviewed by Evolve
2
first-author
0
corresponding
—
works · OpenAlex
—
h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-09-30) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.
Research profile
Output—
Output measures total publication volume and venue placement. The underlying record is not available yet — only two 2026 arXiv preprints are cataloged, so there is no full publication history to score.
Impact—
Impact tracks citations and downstream influence of the work. No citation data exists for this researcher yet, so the axis cannot be scored; both cataloged papers are recent 2026 preprints with no measurable citation record.
Focus0/100
The focus score is 0, and the raw field distribution explains it: the two cataloged papers split evenly at 0.5 time-series machine learning (forecasting/econometrics) and 0.5 database systems / information retrieval. With only two outputs and no overlap between the fields, dispersion is maximal; the split spans forecasting methodology and spatial-keyword data access rather than one niche.
Collaboration14/100
The collaboration score is 14, matching the raw count of 1 distinct coauthor — the frequent-coauthor list contains only the author's own name. Both 2026 preprints are solo, first-authored works without a corresponding-author designation, so there is no co-author network to analyze; the low score reflects isolation rather than weak ties.
Activity—
Activity measures publication cadence and recency over time. The data is insufficient: with only two preprints, both from 2026, there is no multi-year series of publication dates from which to compute a pace or trend.
Reproducibility78/100
Reproducibility averages 78 across the 2 scored papers. The certified semantic-spatial retrieval pilot is rated high — its certified-recall protocol and 5.85× overhead are concretely reported — while the revision-aware forecasting paper is rated medium because its real ALFRED/Chronos-2 experiments are unrun. Honest self-limitation in both write-ups supports the score of 78.
Research summary
Taimoor Ahmad is an early-stage researcher whose public footprint, as captured in the Evolve catalog, consists of two solo first-authored arXiv preprints posted in 2026. The record splits evenly — 0.5 and 0.5 in the field distribution — between time-series machine learning (forecasting/econometrics) and database systems / information retrieval, which is why the focus score sits at 0. The first paper, 'Time-Series Foundation Models That Understand Data Revisions,' proposes VINTAGE-TS, an Evolve-reviewed protocol for revision-aware forecasting; the review calls the protocol rigorous and honest while noting the real ALFRED/Chronos-2 experiments remain unrun and the reference list is padded (reproducibility readiness: medium). The second, 'A Functional Pilot for Certified Freshness-Aware Semantic–Spatial Range Retrieval,' presents a certified-recall design for geo-plus-semantic threshold range queries with a freshness watermark and delta overlay; Evolve judges it sound but tiny, with certified mode 5.85× slower than an exact scan (readiness: high). The two papers share one concern — data that changes after it is recorded. Across the two papers the mean reproducibility score is 78. No citation, output-volume, or activity data is available yet, and no affiliation or ORCID is attached.
Collaboration pattern
Collaboration is minimal: the raw record lists 1 distinct coauthor, and the frequent-coauthor list contains only 'Taimoor Ahmad' himself. Both 2026 preprints are solo, first-authored works, each without a corresponding-author designation, yielding a low collaboration score of 14. No co-author network, lab group, or mentor appears in the cataloged data, so joint work would be the main lever for growth on this axis.
Research keywords
Highlights
VINTAGE-TS: a revision-aware forecasting protocol
Certified freshness-aware semantic-spatial retrieval
One thread: data that changes after capture
Both 2026 first-author preprints confront mutation of stored data — revisions to recorded time series in one, freshness watermarking and a delta overlay for spatial-keyword indexes in the other — an unusual bridge between forecasting and database systems.
Timeline
- 2026Posts an arXiv preprint, 'A Functional Pilot for Certified Freshness-Aware Semantic–Spatial Range Retrieval', as sole first author; Evolve rates its reproducibility readiness high.
- 2026Posts 'Time-Series Foundation Models That Understand Data Revisions' (VINTAGE-TS), again as sole first author; Evolve rates reproducibility readiness medium.
- 2026Catalog picture in late 2026: two solo first-author preprints split evenly between time-series machine learning and database systems/IR, with a mean reproducibility score of 78.
Papers in the Evolve catalog
Frequent co-authors
About this profile
This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.
- Radar scores rest on a very small sample — just two 2026 arXiv preprints — and the next paper could move any dimension sharply.
- No institution, country, or ORCID is attached; the profile is name-resolved from public bibliographic data, and 'Taimoor Ahmad' is a common name, so identity conflation is possible.
- The output, impact, and activity axes lack underlying data entirely; no citation counts or full publication history were available.
- Paper evidence comes from Evolve-reviewed arXiv preprints only; no peer-reviewed venue record is included.