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Templates

Time series and forecasting

By e2er authors

A series in, its trend, seasonality, models and forecasts out. The forecast setup and its hold-out are frozen before any model is fitted; every model is judged on the hold-out against naive baselines, with errors recomputed by e2er and forecasts given with intervals. Optional pre-registration of the setup.

In placeIn e2er 0.16.1empiricaltime series
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Input
A research question and a series
Output
Its trend, seasonality, models and forecasts with intervals, every model judged on a hold-out frozen before fitting, as a draft paper with a dossier
At the end
When any run ends, e2er builds the PDF, writes the audit trail and the structured export, and pushes to GitHub if you set that up.

Sources

Methods

George E. P. Box, Gwilym M. Jenkins and Gregory C. Reinsel. Time Series Analysis: Forecasting and Control (4th edition). Wiley.

Rob J. Hyndman and George Athanasopoulos. Forecasting: Principles and Practice (3rd edition). OTexts, Melbourne.

Rob J. Hyndman and Yeasmin Khandakar. Automatic Time Series Forecasting: The forecast Package for R. Journal of Statistical Software 27(3).

Rob J. Hyndman and Anne B. Koehler. Another look at measures of forecast accuracy. International Journal of Forecasting 22(4), 679-688.

Leonard J. Tashman. Out-of-sample tests of forecasting accuracy: an analysis and review. International Journal of Forecasting 16(4), 437-450.

Francis X. Diebold and Roberto S. Mariano. Comparing Predictive Accuracy. Journal of Business & Economic Statistics 13(3), 253-263.

David Harvey, Stephen Leybourne and Paul Newbold. Testing the equality of prediction mean squared errors. International Journal of Forecasting 13(2), 281-291.

David A. Dickey and Wayne A. Fuller. Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association 74(366a), 427-431.

Denis Kwiatkowski, Peter C.B. Phillips, Peter Schmidt and Yongcheol Shin. Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics 54(1-3), 159-178.

Henry B. Mann. Nonparametric Tests Against Trend. Econometrica 13(3), 245-259.

Pranab Kumar Sen. Estimates of the Regression Coefficient Based on Kendall's Tau. Journal of the American Statistical Association 63(324), 1379-1389.

Steps

Greyed steps do not run in the selected mode.

  1. 1
    Design review
    The researcher reads the plan, the data and the forecast setup (forecast_design.json: series, hold-out, horizon, baselines, candidate models, diagnostics, interval level) before any model is fitted.
    your review
  2. 2
    Pre-registration
    OPTIONAL (choose it on New study, or `e2er run --review-at preregister`): the setup assembled into preregistration.md and frozen with its fingerprint on approval.
    your review
  3. 3
    Forecast setup and hold-out frozen
    Checks the setup against the series and freezes it with its hold-out (holdout_freeze.json); refuses a setup written after a model was fitted.
    check
  4. 4
    Forecasts judged before the draft
    check
  5. 5 specialists
  6. 6
    Improve until it stops getting better
    specialists
  7. 7
    Estimation check
    The results file held to the time-series contract.
    check
  8. 8
    Forecasts judged on the hold-out
    Diagnostics on training periods only, every model fitted before the hold-out and evaluated on exactly the hold-out, errors recomputed from the predictions, baselines present, forecasts with intervals (src/core/pipeline/forecast_checks.py).
    check
  9. 9
    Figures re-read from the data
    Every figure re-read from the data or results it names.
    check
  10. 10
    Draft review
    The researcher reads the draft before the reviewers.
    your review
  11. 11
    Self-critique
    Self-critique
    specialists
  12. 12 specialists, parallel
  13. 13 reviewers, parallel
  14. 14 specialists
  15. 15
    Replication package
    Replication package
    specialists
Technical details

Template id time-series-forecasting · file pipelines/time-series-forecasting.toml, checked against pipeline.schema.json.

StepId in the template fileSpecialists
1. Design reviewreview_design
2. Pre-registrationpreregister
3. Forecast setup and hold-out frozenforecast_design_gate
4. Forecasts judged before the draftforecasts_before_draft
5. Design, data, estimation and draftinitialidea_developer, literature_scanner, data_architect, identification_strategist, econometrics_specialist, data_analyst, paper_drafter
6. Improve until it stops getting betteriterative
7. Estimation checkestimation_gate
8. Forecasts judged on the hold-outforecast_gate
9. Figures re-read from the datafigure_gate
10. Draft reviewreview_draft
11. Self-critiqueself_attackself_attacker
12. Polishpolishpolish_formula, polish_numerics, polish_bibliography
13. Review panelreviewmethods_reviewer, technical_reviewer, data_reviewer, plausibility_reviewer, literature_reviewer, writing_reviewer
14. Revisionrevisionrevisor, patch_revisor
15. Replication packagereplicationreplication_packager

At the end (finalize): compile, audit_export, github_push, structured_export. Modes: single_pass, iterative.

More

Description

Data model
Discipline
General
Method family
Empirical (quantitative)
Design
not specified
Research stage
ReviewRevision and editingReplication
Authors
e2er authors (Software, Methodology)
Usage
not used in published research yet
Source
e2er repository · pipelines/time-series-forecasting.toml · @d8fd8ab
Record
template:time-series-forecasting · JSON

Solid tags are declared by the source or mapped from its terms; dashed tags are inferred by a published rule. Hover a tag for its provenance.

Versions and evaluations

One version, unreleased

One version so far: pipelines/time-series-forecasting.toml in e2er 0.16.1. No evaluation records exist yet.

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Cite this template
Persistent identifiers planned 
@software{e2erauthors2026e2ertimeseriesandforecas,
  title   = {e2er time series and forecasting template},
  author  = {e2er authors},
  year    = {2026},
  note    = {e2er template. Persistent identifier planned},
  url     = {https://e2er.org/templates/time-series-forecasting}
}

Connectors this template can use

What a run of this template can reach outside your machine, and what each sends out.

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Checks on this template

  • ✓Output contracts · Every step produced the artifact it declared, in the declared form
    Required of every template.
  • ✓Execution reliability · Analysis code ran and wrote a parseable, non-empty result
    Required of every template.
  • ○Numbers · Every number in tables and text matches the output of executed code
    Proposed. Proposed for the floor; runs log it but may not block on it.
  • ○Citations · Every reference resolves to an existing publication via OpenAlex, Semantic Scholar or Crossref
    Proposed. Proposed for the floor; runs log it but may not block on it.
  • ○Verbatim passages · Every claim about a source points to a passage found word for word in that source
    Proposed. Proposed for the floor; runs log it but may not block on it.

Runs of this template

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Use it

Install e2er as described on the Install page, then start a run:

e2er run "<your research question>" --template time-series-forecasting

From e2er 0.16.1. Get started goes from install to publish.

On e2er.org

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