ARXIV:2603.13059 · CPC FORECASTING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Competition-Aware CPC Forecasting with Near-Market Coverage

arXiv

A scalable solution for improving CPC forecasting in auction-driven markets using competition-aware signals.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain A scalable solution for improving CPC forecasting in auction-driven markets using competition-aware signals.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

A scalable solution for improving CPC forecasting in auction-driven markets using competition-aware signals. Using Google Ads auction logs from a concentrated car-rental market (2021--2023), we forecast weekly CPC for 1,811 keyword series and approximate…

METHOD

Full abstract

Cost-per-click (CPC) in paid search is a volatile auction outcome generated by a competitive landscape that is only partially observable from any single advertiser's history. Using Google Ads auction logs from a concentrated car-rental market (2021--2023), we forecast weekly CPC for 1,811 keyword series and approximate latent competition through complementary signals derived from keyword text, CPC trajectories, and geographic market structure. We construct (i) semantic neighborhoods and a semantic keyword graph from pretrained transformer-based representations of keyword text, (ii) behavioral neighborhoods via Dynamic Time Warping (DTW) alignment of CPC trajectories, and (iii) geographic-intent covariates capturing localized demand and marketplace heterogeneity. We extensively evaluate these signals both as stand-alone covariates and as relational priors in spatiotemporal graph forecasters, benchmarking them against strong statistical, neural, and time-series foundation-model baselines. Across methods, competition-aware augmentation improves stability and error profiles at business-relevant medium and longer horizons, where competitive regimes shift and volatility is most consequential. The results show that broad market-outcome coverage, combined with keyword-derived semantic and geographic priors, provides a scalable way to approximate latent competition and improve CPC forecasting in auction-driven markets.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Across methods, competition-aware augmentation improves stability and error profiles at business-relevant medium and longer horizons, where competitive regimes shift and volatility is most consequential.

WHY NOW

CPC Forecasting moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainA scalable solution for improving CPC forecasting in auction-driven markets using competition-aware signals.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A scalable solution for improving CPC forecasting in auction-driven markets using competition-aware signals.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2603.13059 · CPC FORECASTING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Competition-Aware CPC Forecasting with Near-Market Coverage

arXiv

A scalable solution for improving CPC forecasting in auction-driven markets using competition-aware signals.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain A scalable solution for improving CPC forecasting in auction-driven markets using competition-aware signals.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

CPC Forecasting moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainA scalable solution for improving CPC forecasting in auction-driven markets using competition-aware signals.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A scalable solution for improving CPC forecasting in auction-driven markets using competition-aware signals.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.13059

Competition-Aware CPC Forecasting with Near-Market Coverage

A scalable solution for improving CPC forecasting in auction-driven markets using competition-aware signals.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback27 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

CPC Forecasting moved forward this cycle; last verified April 2026. Public score 7.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
A scalable solution for improving CPC forecasting in auction-driven markets using competition-aware signals. Using Google Ads auction logs from a concentrated car-rental market (2021--2023), we forecast weekly CPC for 1,811 keyword series and approximate latent competition through complementary signals derived from keyword text, CPC trajectories, and geographic market structure.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Cost-per-click (CPC) in paid search is a volatile auction outcome generated by a competitive landscape that is only partially observable from any single advertiser's history. Using Google Ads auction logs from a concentrated car-rental market (2021--2023), we forecast weekly CPC for 1,811 keyword series and approximate latent competition through complementary signals derived from keyword text, CPC trajectories, and geographic market structure.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Across methods, competition-aware augmentation improves stability and error profiles at business-relevant medium and longer horizons, where competitive regimes shift and volatility is most consequential.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
CPC Forecasting moved forward this cycle; last verified April 2026. Public score 7.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

CPC Forecasting

Competitors

not indexed

Competitive landscape

A scalable solution for improving CPC forecasting in auction-driven markets using competition-aware signals.

Segment

CPC Forecasting

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.13059 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(27)

Chronos-2: From Univariate to Universal Forecasting

2025Abdul Fatir Ansari, Oleksandr Shchur et al.

Enhancing Hierarchical Sales Forecasting with Promotional Data: A Comparative Study Using ARIMA and Deep Neural Networks

2024M. Teixeira, J. Oliveira et al.

Chronos: Learning the Language of Time Series

2024Abdul Fatir Ansari, Lorenzo Stella et al.

TimeGPT-1

2023Azul Garza, Cristian Challu et al.

Amercing: An intuitive and effective constraint for dynamic time warping

2023Matthieu Herrmann, Geoffrey I. Webb

Interpretable Deep Learning for Forecasting Online Advertising Costs: Insights from the Competitive Bidding Landscape

2023Fynn Oldenburg, Qiwei Han et al.

Click-through rate prediction in online advertising: A literature review

2022Yanwu Yang, Panyu Zhai

Online Advertising Revenue Forecasting: An Interpretable Deep Learning Approach

2021Maximilian Würfel, Qiwei Han et al.

Web Traffic Time Series Forecasting Using LSTM Neural Networks with Distributed Asynchronous Training

2021Roberto Casado-Vara, Á. Martín del Rey et al.

Online search engines and online travel agencies: A Comparative Approach

2020S. Angeloni, C. Rossi

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

2019Nils Reimers, Iryna Gurevych

Graph WaveNet for Deep Spatial-Temporal Graph Modeling

2019Zonghan Wu, Shirui Pan et al.

Forecasting at Scale

2018Sean J. Taylor, Benjamin Letham

Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

2017Yaguang Li, Rose Yu et al.

Inference and auction design in online advertising

2017Denis Nekipelov

Dask: Parallel Computation with Blocked algorithms and Task Scheduling

2015M. Rocklin

The effect of ad rank on the performance of keyword advertising campaigns

2013B. Jansen, Zhe Liu et al.

Bounding the inefficiency of outcomes in generalized second price auctions

2012I. Caragiannis, C. Kaklamanis et al.

Simplified mechanisms with an application to sponsored-search auctions

2010Paul R. Milgrom

An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets

2009A. Ghose, Sha Yang

Showing 20 of 27 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkA Lightweight MPC Bidding Framework for Brand Auction Ads

7.0

Prior WorkTimeSeek: Temporal Reliability of Agentic Forecasters

7.0

Prior WorkAHBid: An Adaptable Hierarchical Bidding Framework for Cross-Channel Advertising

7.0

Extension

Builds On ThisGuiding the Recommender: Information-Aware Auto-Bidding for Content Promotion

6.0

Builds On ThisLearning to Spend: Model Predictive Control for Budgeting under Non-Stationary Returns

3.0

Builds On ThisConstrained Auto-Bidding via Generative Response Modeling

3.0

Builds On ThisSelective Test-Time Compute Scaling for Click-Through Rate Prediction via Uncertainty-Triggered Feature Path Exploration

0.0

Builds On ThisInterpretable PM2.5 Forecasting for Urban Air Quality: A Comparative Study of Operational Time-Series Models

4.0

Commercially relevant

Higher ViabilityMarket-Bench: Benchmarking Large Language Models on Economic and Trade Competition

8.0

Higher ViabilityLLM-HYPER: Generative CTR Modeling for Cold-Start Ad Personalization via LLM-Based Hypernetworks

9.0

Conflicting

none indexed

Owned Distribution

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Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2603.13059
Route: /paper/competition-aware-cpc-forecasting-with-near-market-coverage
Active tab: read
Artifact: competition-aware-cpc-forecasting-with-near-market-coverage

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/competition-aware-cpc-forecasting-with-near-market-coverage/paper-pack
REST build passport API/api/v1/paper/competition-aware-cpc-forecasting-with-near-market-coverage/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
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  "active_tab": "synced from current hash by the drawer client",
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  }
}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/competition-aware-cpc-forecasting-with-near-market-coverage

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Competition-Aware CPC Forecasting with Near-Market Coverage

Canonical ID competition-aware-cpc-forecasting-with-near-market-coverage | Route /paper/competition-aware-cpc-forecasting-with-near-market-coverage

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/competition-aware-cpc-forecasting-with-near-market-coverage

MCP example

{
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  "arguments": {
    "arxiv_id": "2603.13059"
  }
}

source_context

{
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  "mode": "paper",
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  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: Competition-Aware CPC Forecasting with Near-Market Coverage

/buildability/competition-aware-cpc-forecasting-with-near-market-coverage

Watchwatch

Subject: Competition-Aware CPC Forecasting with Near-Market Coverage

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/competition-aware-cpc-forecasting-with-near-market-coverage

Paper ref

competition-aware-cpc-forecasting-with-near-market-coverage

arXiv id

2603.13059

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

227a0c1309dc670901992b043cbc2cde45df11747ef7e67eaa44f95f0c5ab314

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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Competition-Aware CPC Forecasting with Near-Market Coverage

Competition-Aware CPC Forecasting with Near-Market Coverage

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Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(27)

Related Papers

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline