Market research and historical forecast datasets Evaluate research archives and forecast vintages with publication timing, methodology, revision history, licensing scope and source-grounded comparisons. Historical forecast datasets preserve what a source projected at a particular date, not just the latest revised history. They connect publication vintage, target period, forecast horizon, scenario, units and methodology with later outcomes. That structure matters for forecast evaluation, economic assistants and temporal retrieval. IMF and OECD releases provide public reference points, while commercial research services may offer different coverage and delivery. Buyers should distinguish a subscription, analysis permission and a model-training license. Compare actual vintage depth and source authority rather than treating every historical table as information that was available to a decision-maker at the time. Dataset purpose Market research and historical forecast data Availability Source and commercial scope reviewed for each project Reviewed 12 November 2026 A forecast record has more than one time axis The date a forecast is published and the period it predicts are different fields. A projection for a future year can be revised several times before that year begins and again during it. Preserve the publication vintage, target period, horizon and any source cutoff date. A current database entry cannot reliably reconstruct those earlier beliefs unless the source retained its historical releases. The observed outcome also has a version. A statistic initially reported as actual may be revised later. Decide whether the evaluation compares a forecast with the first release, a later revision or another defined outcome measure. Keep that choice explicit. Without it, two apparently identical forecast tests may answer different questions, and a model may benefit from later information that was unavailable when the original projection was made. Use public release systems as structural references [The IMF WEO database](https://data.imf.org/Datasets/WEO) and its [FAQ](https://data.imf.org/Datasets/WEO/Frequently-Asked-Questions) distinguish actual-data metadata, projections and historical vintages. [OECD’s sources-and-methods page](https://www.oecd.org/en/topics/sub-issues/economic-outlook/key-facts-about-the-oecd-economic-outlook.html) explains forecast editions, frequencies and revision issues. These are useful public examples of why provenance and release structure matter. They are not universal substitutes for proprietary sector research, company-level estimates or every historical forecast. Coverage, definitions, missing values and series continuity need inspection. Compare the actual indicators and vintages required for the project. An archive with a long time span may have only a few consistent series, while a recent source may offer detailed industry segmentation. A headline date range alone cannot establish comparable information content. Reference What it supplies What it does not establish [IMF WEO dataset](https://data.imf.org/Datasets/WEO) Macroeconomic history, forecasts and preserved release references Current revised history is not the same as past forecasts. [OECD Economic Outlook sources and methods](https://www.oecd.org/en/topics/sub-issues/economic-outlook/key-facts-about-the-oecd-economic-outlook.html) Economic Outlook editions and method documentation Country/series coverage and provisional data require review. [Oxford Economics AI and data solutions](https://www.oxfordeconomics.com/ai-data-solutions/) Commercial economic data and AI integration services Subscription scope does not automatically establish model-training or resale rights. Separate baseline, scenario and opinion A baseline projection, downside scenario and analyst opinion may use similar language while representing different claims. Record the scenario definition, assumptions and source methodology. A scenario is not necessarily a probability-weighted forecast. If probabilities are supplied, preserve whether they were explicitly stated or inferred later by an annotator. Narrative research can also contain conditional statements that should not become unconditional numeric labels. A report saying growth could fall under a particular shock is not equivalent to predicting that fall as the central case. Retain the supporting passage and the conditions attached to it. If the project includes extracted forecasts, review the extraction against the original source so contextual qualifiers are not lost during normalization or summarization. Methodology changes are part of the archive Definitions, geographic groupings, sector classifications and base years can change. A continuous-looking series may therefore combine different measurement regimes. Preserve source notes and versioned dictionaries. If a standardized output is supplied, document how older categories map to the new schema and what information was lost or estimated. An archive may also contain merged or discontinued series. Treat absence as a source fact rather than filling it with an invented estimate. A buyer can choose interpolation or another modeling assumption, but that is a separate derivative. Record the method and keep the original observations available where permitted. The goal is a corpus whose historical claims can be traced, not a cosmetically complete table that silently conceals breaks in meaning. Temporal retrieval needs the right evidence A financial or economic assistant may need to answer what a source expected at a particular time, why it changed its view or which evidence supported a scenario. Those questions require release-specific source documents, not only the final values. Associate narrative passages, charts, tables and revised forecasts with stable publication identifiers. Keep the expected answer and evidence references separate from the model’s searchable input when designing an evaluation. Include questions that should be answered with uncertainty because the source did not publish the requested combination. A retrieval corpus that always contains a clean answer to every question can overstate performance in real research workflows. Distinguish unsupported answers from answers based on the wrong vintage; both matter, but they reveal different failures. Commercial services and buyer use cases [Oxford Economics markets AI and data integration services](https://www.oxfordeconomics.com/ai-data-solutions/) that connect economic information with customer workflows. That is a concrete supplier example, not proof of a particular training-data license or a relationship with Fiund. Subscription terms, delivery methods and permitted uses should be reviewed for the actual project. Potential use cases include economic research assistants, forecast calibration, scenario retrieval and industry intelligence tools. The useful data requirement differs between them. A model comparing past projections needs vintages; a current decision-support tool may emphasize timely updates and source explanations. A market-research archive can be valuable when it captures a specific sector or methodology, but the page does not establish that a named buyer is currently procuring it. Treat public supplier descriptions as capability evidence rather than a measured demand survey. Pricing: compare scope before an estimate The checked commercial service descriptions do not establish a comparable public price for licensing a historical research archive for model training. A user subscription, API access and rights to redistribute or train on the material are different products. Do not turn a retail report price into a category-wide dataset valuation. Ask which source rights, vintages, indicators, narrative content and update obligations a written offer includes. Preparation can involve extracting tables, reconciling revisions, normalizing units, linking reports and validating point-in-time answers. These costs are separate from the underlying content license. No defensible market average is asserted here. A budget should therefore identify fixed source review, variable preparation, expert checks and ongoing delivery before estimating totals. If a provider supplies a quote, keep its date and assumptions so a later comparison does not confuse changed scope with a changed market rate. Disclosed content partnerships are not unit-price benchmarks The [Financial Times–OpenAI announcement](https://openai.com/index/content-partnership-with-financial-times/) describes a journalism licensing partnership without disclosing consideration. It shows that named parties can structure content access around AI products. It does not price a historical forecast corpus, reveal a rate per report or establish access to unrelated research archives. Investment rounds, acquisitions and publisher revenues are also different from a standalone data-license value. Where no directly comparable amount is public, state that limitation. Expertise comes from explaining the source and the commercial object precisely, not attaching a large unrelated deal value to a category. Fiund’s discussion should identify the actual proposed collection, permitted uses, preparation and acceptance evidence rather than treating every financial-content announcement as a comparable sale. Example schema: retain belief, release and outcome separately A vintage-aware schema should let a reviewer reconstruct the information available at the decision point. The fields below describe one possible design. They are not a claim that every publisher exposes all of them or that a retrospective reconstruction is equivalent to an original preserved release. In particular, a target year can appear in many publications with different statuses and values. A later revised actual should not overwrite the earlier forecast simply to produce one tidy series. Retaining both makes the relationship usable for forecasting research, temporal question answering and source comparison. The original source and the transformation history remain part of the evidence. Illustrative field Why it matters Acceptance question Publication vintage Identifies the release and when the information became available. A file creation timestamp is not necessarily the original publication date. Target period States the future or historical interval being described. Keep forecast horizon separate from the indicator’s own frequency. Scenario and status Distinguishes baseline, alternative scenario, estimate and observed value. Do not infer a probability or certainty the publisher did not state. Definition version Preserves units, base years, sector mappings and method changes. A continuous-looking series can contain a break in meaning. Outcome vintage Defines the realized-value release used for an evaluation comparison. First-release and later revised outcomes answer different questions. Evidence reference Connects the value and qualifiers to a source table or passage. A conditional narrative should not become an unconditional numeric label. Case study design: compare two forecasts without hindsight Choose two dated forecasts for the same target period. Record what changed, which assumptions were stated and which later outcomes are excluded from the model’s input. Then ask the system to explain the revision using only contemporaneous evidence. If the report does not state a cause, the expected answer should acknowledge that limit instead of constructing a persuasive retrospective story. Repeat the comparison with different outcome vintages. A forecast may appear more or less accurate depending on the later statistical revision used as the target. This is not a reason to select the most flattering result. It is a reason to define the evaluation convention and retain enough metadata for another reviewer to reproduce it. The exercise makes clear why historical release depth and provenance can matter more than raw row count. A vintage-aware sample acceptance exercise Choose one indicator and compare several dated releases, including at least one revision and a change in the target period’s status. Trace a value from the delivered table to the source publication. Verify the units, scenario and dates. Then ask a historical question using only material available at the chosen cutoff, keeping later outcomes out of the input. For a broader research archive, test a sector classification change and a narrative statement with conditions. Record whether the proposed schema can preserve those meanings. Send Fiund the required markets, dates, task and intended uses, together with examples of questions the source should support. Source authority and coverage are reviewed for the selected material. A successful small sample can define a preparation approach; it does not establish that every indicator or vintage exists in the desired volume. Is the latest historical series enough for a forecast backtest? Not when the test asks what was knowable at an earlier decision date. Later revisions can introduce information unavailable then. Preserve the original forecast vintage and define which version of the realized outcome is used for comparison. A latest-only series can support other tasks, but should not be mislabeled as a point-in-time information set. Does buying research access permit AI training? Not automatically. A subscription, API agreement, internal analysis permission and model-training license can have different scope. Review the actual publisher terms and proposed use. The examples here identify public and commercial source types; they do not grant rights to copy, redistribute or train on unrelated reports. How should research archive size be measured? Use independent publications, vintages, consistent indicators, jurisdictions and useful labeled tasks where available. Repeated tables, revised exports and many questions derived from one report can inflate counts. The most relevant unit depends on the model objective. Preserve lineage so the buyer can compare real information coverage rather than raw file volume. Sources IMF WEO dataset: https://data.imf.org/Datasets/WEO IMF WEO FAQ: https://data.imf.org/Datasets/WEO/Frequently-Asked-Questions OECD Economic Outlook sources and methods: https://www.oecd.org/en/topics/sub-issues/economic-outlook/key-facts-about-the-oecd-economic-outlook.html Oxford Economics AI and data solutions: https://www.oxfordeconomics.com/ai-data-solutions/ OpenAI / Financial Times licensing partnership: https://openai.com/index/content-partnership-with-financial-times/