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SEO Forecast

Free, no signup. Most traffic forecasts treat an algorithm update like noise that will smooth itself out — it won't. Upload your Search Console, GA4, Ahrefs, Semrush, or Adobe export and get a forecast that treats updates as the permanent level shifts they are, backtested on your own history before you're asked to trust it.

Built by Patrick Stox

GSC / GA4 / Ahrefs (traffic + traffic value) / Semrush / Adobe / Google Trends / generic — format auto-detected

Runs in your browser — uploads are not sent to our server, but the current project is autosaved in this browser’s local storage until you replace it or clear this site’s browser data. Creating a share link is opt-in; it uploads the scenario you choose and retains it for 7 days. Anonymous run-level outcome counters may be used for aggregate research; URLs, domains, IPs, and identifiers are never included, and no statistic is released below 100 runs.

Google algorithm-update data last synced Jul 7, 2026.

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Found something broken in Seo Forecast? Let us know what happened — this goes straight to a private triage queue, not a public list.

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 No tool inputs, uploads, pasted source, complete results, query parameters, or URL fragments are attached automatically. You can edit or remove the selected passage above. Browser and anti-abuse metadata is processed for spam prevention. 
  
 
  
 
 
  
 
 
   
          
    
          

Forecasts assume no future algorithm updates, content changes, or SERP-feature shifts — the
        intervals come from each model's real backtest errors on your data, not from optimistic
        formulas. Third-party traffic and traffic-value figures are estimates. Nothing you upload is
        sent anywhere: parsing, modeling, and charts all run locally in your browser.

  
 
Sample report
 

Suppose you upload a weekly Search Console clicks export that dips after the March 2025 core
          update and recovers late in the year:

 
 
Date,Clicks,Impressions,CTR,Position
2025-01-06,9800,410000,2.4%,7.1
2025-01-13,9950,415000,2.4%,7.0
2025-03-17,8200,398000,2.1%,8.3
2025-06-02,8600,405000,2.1%,8.0
2025-11-10,10400,430000,2.4%,6.6
 
 
…and the Forecast tab returns a summary like this
(illustrative projection — invented numbers, not real data):
 
Illustrative example — forecasting your own export runs entirely in your browser; the numbers below are fabricated to show the summary's shape, not a real backtest
 
 ▲ 12%
  Clicks forecast for demo-site.example: 11.6k per week
          in 26 weeks (now 10.4k), 80% range 9.8k–13.5k.
          Model: Theta (won the backtest, MASE 0.71).
          Algorithm updates that measurably moved this series: March 2025 core update (-16%).

⚠ Illustrative figures only — run the tool on your own export for real numbers.
 
 
     
  • The 80% range is the actual forecast, not the 11.6k headline number — a spread
            of 9.8k–13.5k means real outcomes anywhere in that band are consistent with the model. See
how to read the range ↓.
    •  
  • The model name and MASE score explain why you should trust it — Theta won because
            it had the lowest backtest error; MASE below 1 means it beat a naive seasonal-repeat baseline, not
            an arbitrary bar.
    •  
  • Only algorithm updates that measurably moved this series appear — the March 2025
            core update is listed because the model detected an actual ~16% shift, not because of when it
            shipped. The methodology behind this detection is covered in
SEO forecasting.
    •  
 
 
 
How to use it
 
     
  1. Upload a CSV (or click Load demo data to explore first).
            Search Console, GA4, Ahrefs, Semrush, Adobe, Google Trends, and generic date-plus-value files
            are auto-detected — you don't pick a format.
    2.  
  2. Pick the series to forecast (clicks, sessions, position, traffic value…),
            set group by to weekly or monthly, and drag the horizon slider
            to how many periods ahead you want.
    3.  
  3. In Detected events, label any level shift the tool found — a migration,
            redesign, or tracking change — so it's modeled correctly. Known Google updates are handled
            automatically.
    4.  
  4. Leave model on auto (best backtest) or force a specific one, then
            press Forecast.
    5.  
  5. Read the summary, then switch tabs: Explain for the seasonal-vs-real verdict
            and per-update impact, Backtest for model accuracy, Keywords
for a ranking-change scenario. Export the forecast as CSV or PNG.
    6.  
 
 

SEO Forecast projects organic traffic from your own analytics export and treats Google
          algorithm updates as permanent level shifts rather than temporary blips. For the theory behind
          the approach see SEO forecasting; to turn a projected
          traffic gain into a business case, pair it with the
SEO ROI Calculator.

 
How to read the estimate
 
     
  • The big % + arrow — projected change from now to the end of the horizon.
            Green is favourable, red unfavourable (and for inverted metrics like average position, a lower
            number is the good direction).
    •  
  • 80% / 95% bands — prediction intervals from each model's real backtest
            errors. The 80% band is your realistic planning range; it widens further out because
            uncertainty compounds.
    •  
  • Central dashed line — the model's best single estimate; the solid line is
            your actual history.
    •  
  • Update markers — a bold vertical line with a measured % means a Google
            update moved your series; a faint line means a known update had no measurable effect
            here and was left out of the math.
    •  
  • Anomaly dots — one-off outliers (an outage, a tracking glitch) that were
            down-weighted so they don't distort the baseline.
    •  
  • Data quality score — a 0–100 read on whether you have enough clean history
            (ideally two-plus yearly cycles) for seasonality to be identifiable.
    •  
  • MASE in the backtest table — mean absolute scaled error; below 1 beats the
            seasonal-naive baseline, and lower is better.
    •  
 
How it works
 

Everything runs client-side. Your CSV is parsed and auto-format-detected, then the heavy work —
          decomposition, level-shift and outlier detection, model fitting, and a rolling-origin backtest —
          happens in a Web Worker so the page stays responsive and your data never leaves the
          browser. The tool fits several models (seasonal-naive, drift, ETS, Theta, and an
          updates-aware curve fit), scores each by repeatedly forecasting held-out slices of your own
          history, and picks the winner. Google updates from a bundled dates file are tested as candidate
          level shifts and only kept when they measurably moved your series. Prediction intervals come from
          those backtest errors, not from a formula — which is why they're honest about uncertainty.

 
Features
 
     
  • Auto-detects GSC, GA4, Ahrefs (traffic + traffic value), Semrush, Adobe, Google Trends, and
            generic exports; weekly/monthly/as-uploaded aggregation.
    •  
  • Five backtested models with an auto-pick by lowest MASE, plus a manual override.
    •  
  • Algorithm updates modeled as level shifts, kept only when they moved your data; unlabeled
            shifts surfaced for you to label.
    •  
  • Explain tab: seasonal-vs-real drop verdict, trend/seasonal decomposition, and
            counterfactual per-update impact.
    •  
  • Backtest tab: how wrong each model was on your history, with an overlay of
            what the top models would have predicted.
    •  
  • Compare & SoV tab: forecast multiple sites on one chart or as share of
            voice, with competitor-estimate calibration to your ground truth.
    •  
  • Keywords tab: scenario math (volume × CTR-by-position) for ranking changes —
            no time series needed, with optional CTR-curve fitting from your GSC Queries export.
    •  
  • Stress-test overlay, 80/95% bands, log scale, saved-scenario ghost line, and CSV/PNG/scenario
            exports.
    •  
 
Limitations
 

A forecast assumes no future algorithm update, content change, or SERP-feature shift
          during the horizon — it can't predict events that haven't happened, which is exactly why the
          intervals widen. It needs enough clean history (roughly two yearly cycles) for seasonality to be
          reliable; short or gappy series get wide bands and a low quality score. Third-party traffic and
          traffic-value figures (Ahrefs/Semrush) are estimates, not measured clicks — calibrate them to your
          own ground truth before trusting absolute numbers. The Keywords tab is scenario math, not
          curve-fitting, so it's only as good as the CTR curve and hit-probability you give it. And the
          example figures on this page are illustrative, not a benchmark for your site.

 
Frequently asked questions
 
Can you really forecast SEO traffic accurately?
No forecastSEO forecasting uses historical data — traffic, click-through rate, rankings, and search volume — to project future organic search performance and its business impact. It's a probabilistic model under defined assumptions, not a guarantee of results. is exact, and this tool is deliberate about saying so. Instead of a single confident line, it fits several models, backtests each one on slices of your own history it never saw, and reports a prediction interval built from those real errors — not from an optimistic formula. The forecast is only as good as the assumption that no future algorithm update, content change, or SERP-feature shift lands during the horizon, which is why the intervals widen the further out you look. Treat the central line as a planning midpoint and the 80% band as the realistic range.
How does the tool handle Google algorithm updates?
Known Google updates from a bundled dates list are tested against your series as candidate level shifts — permanent step changes in the baseline rather than temporary dips. An update is only kept in the model when it measurably moved your data; if a core update did not touch your site, it is shown faintly and left out of the math. The tool also detects unlabeled level shifts (a migration, a redesign, a tracking change) and asks you to label them so they are modeled correctly instead of being smoothed away.
What CSV formats can I upload?
Search Console, GA4, Ahrefs (traffic and traffic value), Semrush, Adobe Analytics, Google Trends, and a generic date-plus-value layout are all auto-detected — you do not pick a format. It reads any file with a recognizable date column and one or more numeric columns, so you can forecast clicks, sessions, impressions, average position, or estimated traffic value. Everything is parsed in your browser; no file is uploaded to a server.
Is my data uploaded to a server?
No. Parsing, model fitting, backtesting, and chart rendering all run locally in your browser, with the heavy computation in a Web Worker so the page stays responsive. Your export never leaves your machine. The only thing that can be sent anywhere is a share link, and only if you explicitly click Create share link.
What is a prediction interval and why does it widen?
A prediction interval is the range the tool expects the true value to fall in, at a stated confidence — the 80% band should contain the actual outcome roughly 80% of the time. It comes from how wrong each model was during backtesting on your history. The band widens further into the future because uncertainty compounds: a small weekly error accumulates over many weeks, and the chance of an unmodeled event landing grows the longer the horizon.
 
     
 Local data 
Saved targets, named lists, and recent check summaries remain only in this browser.
 
   
 
    
 
 
   
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Feature requests for Seo Forecast
 

Upvote what you want most. New ideas can be submitted from the floating Feedback menu;
      requests appear here once approved, and the most-wanted rise to the top.

   
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  ➕ Request a feature   
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Where this tool helps
Common use cases
Forecast an SEO time series
Upload GSC, GA4, Ahrefs, Semrush, or Adobe data and generate prediction intervals from the site's own history.
Compare backtested models
See which supported forecasting approach performed best on held-out historical periods before using its projection.
Account for major disruptions
Model algorithm updates as level shifts and flag migration or anomaly periods that could distort a naive trend.
Explain uncertainty to stakeholders
Use the seasonality view and honest intervals to frame a range of plausible outcomes rather than a guaranteed target.
Watch the full workflow
SEO Forecast walkthrough
Read the transcript

      
      

        
SEO Forecast

        

        
SEO forecasts are planning ranges, not promises. I’ll show you when this tool helps, how to load and configure a series, review detected events, interpret the demo forecast and backtest, use scenarios and business cases, respect the limitations, and choose practical next steps.

      

      

        
Step 1

        

        
This browser-based tool forecasts a time series from your own export, tests multiple models on held-out history, and treats measured algorithm-update effects as lasting level shifts. Use it for budgets, targets, capacity planning, and scenario discussion, while preserving uncertainty instead of promising one number.

      

      

        
Step 2

        

        
Useful cases include forecasting Search Console clicks, analytics sessions, impressions, average position, third-party traffic, or traffic value; testing an algorithm hit; comparing competitors; and estimating ranking scenarios. Use a consistent metric with enough clean history, ideally more than two seasonal cycles.

      

      

        
Step 3

        

        
Upload one or more C-S-V files, paste rows, or load the generated demo. Search Console, G-A-four, Ahrefs, Semrush, Adobe, Google Trends, and generic date-value layouts are auto-detected. Parsing, fitting, and charts stay in your browser unless you explicitly create a temporary share link.

      

      

        
Step 4

        

        
Choose the metric series, weekly, monthly, or uploaded cadence, and a four-to-fifty-two-period horizon. Auto selects the lowest-error backtested model; manual selection is available for a justified comparison. Keep the eighty and ninety-five percent bands visible and use stress tests as explicit hypotheticals.

      

      

        
Step 5

        

        
Review detected level shifts and outliers before trusting the result. Known Google updates are retained only when they measurably moved this series. Label unknown shifts such as migrations, redesigns, or tracking changes, and down-weight true one-off outages. A wrong label can change the baseline and forecast.

      

      

        
Step 6

        

        
The live generated demo summary reports change by the selected horizon, a central estimate, uncertainty bands, the winning model, backtest error, and measured update effects. Treat the eighty percent band as the practical planning range. The midpoint is one estimate, and widening bands reflect compounding uncertainty.

      

      

        
Step 7

        

        
The solid line is actual history, the dashed continuation is the estimate, and shaded areas are prediction intervals derived from backtest errors. Update lines and anomaly markers explain modeled events. Export the C-S-V or P-N-G, save the scenario, or create an opt-in seven-day share link.

      

      

        
Step 8

        

        
The Explain tab evaluates whether a drop looks seasonal or structural, decomposes the series, and estimates counterfactual update impact. This supports a diagnosis, not causation. Confirm the timing against releases, analytics changes, demand patterns, Search Console dimensions, and page-level evidence.

      

      

        
Step 9

        

        
The Backtest tab repeatedly asks each model to predict history it had not seen. Lower M-A-S-E is better, and below one beats a seasonal-naive baseline. Compare errors and overlays, not model names. Weak or unstable backtests mean the forecast deserves wider planning margins and less confidence.

      

      

        
Step 10

        

        
Features include multi-series compare and share of voice, third-party estimate calibration, keyword ranking scenarios, custom C-T-R curves, stress overlays, saved scenarios, and a business-case tab for conversion, value, cost, P-P-C equivalent, and content velocity. Those outputs remain assumption-driven planning aids.

      

      

        
Step 11

        

        
The model cannot foresee future updates, content changes, or search-result shifts. Short, gappy, or structurally changing history produces weak seasonality and wide bands. Third-party traffic is estimated, keyword projections depend on C-T-R and hit probability, and conversion or revenue outputs depend entirely on your assumptions.

      

      

        
Step 12

        

        
Save the source range, metric definition, aggregation, horizon, model, backtest scores, quality notes, event decisions, prediction bands, and scenario assumptions. Have stakeholders plan from low, central, and high cases. Refresh with actuals on a regular cadence, track misses, and revise decisions when the data-generating process changes.

      

      

        
Plan with a range, then update the evidence.

        

        
Export the forecast range, selected model, backtest error, event labels, horizon, and business assumptions. Compare actuals with the forecast regularly, investigate misses, update the source data and assumptions, and never present the central line as a guaranteed traffic or revenue outcome.

      

    
 
         
 
Common issues & how to fix them
 
     
  •  Warning Traffic has a lasting level shift Fix: Identify the deployment, migration, algorithm event, or tracking change at the shift date and model pre- and post-shift periods separately after correcting the cause. 
  •  Error Labeled event has a negative estimated impact Fix: Segment the affected pages and queries around the labeled event, reverse or repair the causal change where evidence supports it, and measure against an unaffected comparison segment. 
  •  Warning History is too short for a stable forecast Fix: Provide at least one full seasonal cycle of consistent-period data when possible, or shorten the forecast horizon and label the estimate as unstable. 
  •  Information Forecast is strongly seasonal Fix: Compare the same seasonal period year over year and plan against the seasonal baseline instead of the latest point alone. 
  •  Warning Forecast uncertainty interval is wide Fix: Shorten the forecast horizon or add cleaner history before using the midpoint for planning. 
  •  Warning Backtest accuracy is weak Fix: Use the prediction interval, test a baseline model, and collect more consistent history before relying on the forecast. 
    •