FORECAST.ETS.ADD in Collabora Office: Additive Forecasts

=FORECAST.ETS.ADD(target; values; timeline; [period_length]; [data_completion]; [aggregation])

Formulas here use English worksheet names and semicolon separators. Square brackets describe optional arguments and are not typed. The first three arguments are target, historical values and historical timeline. Put the future time in target, not the number of periods ahead: with times 1 through 16, the next target is 17.

Additive seasonality adds a seasonal adjustment to level plus trend. It is a useful candidate when the seasonal swing stays roughly constant in the same measurement units as the observations. A visually additive pattern does not establish the best fitted model or its future accuracy.

Use two numeric columns with one value per timestamp, equal-sized ranges, and headers outside those ranges. Sort the two columns together if you sort them yourself. Collabora documents internal timeline sorting, but sorting only one column changes which observation belongs to each time. Use a consistent positive timeline step; any intended gaps must be whole multiples of that step. A measured zero belongs in the values column. An absent timestamp is a different data problem.

The fixtures below use simple integer time indexes to avoid locale-dependent dates. For monthly records, enter real dates such as =DATE(2025;1;1), not ambiguous text such as 01/2025. The pinned implementation recognizes same-day-of-month dates and converts them to month units. This special handling does not license arbitrary irregular timestamps. Month-end transitions, time fractions and monthly gaps need separate target-build checks.

For functions with period_length, use an integer: 0 explicitly selects nonseasonal double exponential smoothing (EDS); 1 requests automatic detection; 2 or more requests that many samples per seasonal cycle. The omitted default is 1. A value of 1 does not mean a literal one-observation season. Some Help example descriptions say “one sample per period,” but that conflicts with the formal parameter description. A four-quarter cycle means 4 samples; a twelve-month cycle means 12, provided the input cadence actually matches.

The ETS family exposes data_completion and aggregation. Completion defaults to 1 (TRUE); 0 (FALSE) requests zero filling. The documented duplicate-timestamp choices are 1 AVERAGE (default), 2 COUNT, 3 COUNTA, 4 MAX, 5 MEDIAN, 6 MIN and 7 SUM. Use these integer codes, not function names in that argument. Numeric COUNT/COUNTA semantics do not establish acceptance of text observations. These examples deliberately contain no gaps or duplicates, so changing their cleanup options should not change the input dataset.

Do not quietly rely on default AVERAGE to repair duplicate timestamps. In the pinned Collabora implementation, case 1 removes later equal-timestamp rows without computing their mean. The Help describes AVERAGE instead. For duplicate observations 90 and 150, the arithmetic mean is 120 and sum is 240; neither calculation establishes which observation the affected default branch retains after sorting. Resolve duplicates deliberately upstream, or verify the intended aggregation against the exact build before trusting a forecast.

Help calls completion by 1 interpolation. The saved source calculates a single mean of the endpoints for each missing block. With values 60 at time 3 and 90 at time 6, linear interpolation would insert 70 and 80 at times 4 and 5; that source instead constructs 75 and 75. These are independent preprocessing arithmetic controls, not native observations. Help’s “up to 30%” description also leaves the denominator unclear: the source divides inserted missing points by the original supplied count after duplicate handling. Two missing points and fourteen supplied points give 2/14, not 2/16. Avoid threshold claims without a target test.

Range-shape, numeric-content and option errors are also documentation-versus-runtime questions. Help describes particular spreadsheet error labels, but the source sometimes raises a different internal error category, including the unequal-range path. No exact native error display is verified here. Fix the paired data and integer options first; do not use this guide as an error-code compatibility table. Fractional options, blank-cell coercion, array-entry gestures and automatic spill behavior remain unverified.

Enter the following synthetic teaching data in A1:B17. Keep the headers in row 1; the formulas use only the sixteen numeric observations. The same data are used in the ADD and MULT guides so model comparisons do not change the history.

The four block means are 96.25, 119.5, 144.5 and 168.25. Their ranges are 50, 59, 71 and 83: seasonal variation grows broadly with the level, with deliberate noise. This makes a useful comparison between fixed-size additive adjustments and proportional multiplicative adjustments; it does not predetermine the best future model.

How to read the numbers: all fitted values below are independently checked calculations from the pinned Collabora source model, rounded for display. They are not observed results from an installed Collabora application. The history is complete, positive and uniquely timed, the timeline step is 1, and the seasonal period is explicitly 4. The checked mathematical harness does not execute worksheet parsing, error displays or the application interface.

Put targets 17, 18, 19 and 20 in D2:D5. Enter this formula in E2 and copy down to E5:

=FORECAST.ETS.ADD(D2;$B$2:$B$17;$A$2:$A$17;4;1;1)

The anchored ranges hold the training sample fixed; D2 changes to the next target. The explicit period 4 requests four observations per cycle. The final 1 and 1 select the documented completion and aggregation defaults, which do not alter this complete, duplicate-free history.

Read the ADD column for this function. At target 17 its expected source-model value is 148.638780; it is a future observation estimate, not the next block’s average. The paired table demonstrates that changing only the model can change the seasonal shape. The fitted training RMSE is 4.526982, in observation units. It describes this fit, not a confidence radius.

On a separate sheet, use timeline 1 through 16 and values 88, 104, 120, 96, 100, 116, 132, 108, 112, 128, 144, 120, 124, 140, 156, 132. Keep the same formula options and target cells. Each four-observation block has range 32 and means 102, 114, 126 and 138. The source-model ADD predictions at targets 17..20 are 136, 152, 168 and 144. MULT on the identical history gives 135.272256, 152.443935, 169.564675 and 142.324626 (display rounded).

The ADD fit is an exact synthetic seasonal control, with zero training residuals. That does not mean real future observations are certain. The MULT differences illustrate why a proportional seasonal model need not reproduce a constant absolute swing exactly.

On the noisy primary data, =FORECAST.ETS.ADD(17;$B$2:$B$17;$A$2:$A$17;0;1;1) explicitly disables seasonality. Both ADD and MULT then use the source’s nonseasonal EDS path and give a source-model forecast of 189.702090. Compare that with the seasonal result above to see why omitting a known cycle can change the first forecast substantially. Zero is an intentional model choice; it is not the automatic setting.

Omitting period requests automatic detection (1), not the explicit-four fit shown here. The automatic detector and subsequent fit are separate operations. No numeric result for an automatic fit is asserted in this example.

For a future-accuracy exercise, train only on times 1..12 and forecast times 13..16 before comparing with those held-out observations. Repeat with the other model on exactly the same rows. This is a suggested workflow, not a computed holdout result in this guide. Do not test forecast accuracy by passing historical targets into a fit that already includes those observations: the source’s historical/interpolation path is different. Use more than one forecast origin when the history permits it, and check whether errors or seasonal structure change over time.

The source uses its own nested search for alpha, beta and gamma, including endpoint ties and a resolution of 0.001. This is not a promise of a globally optimal statistical fit. It retains seasonal-buffer changes between trial fits; restarting those buffers for every trial changes the algorithm. Its additive initialization divides the first observation by the initial seasonal index, and its ADD versus MULT seasonal-index tests differ at the period boundary. The source-model calculation preserves these details rather than replacing them with a textbook Holt-Winters implementation.

The future seasonal lookup also has a specific modulo offset, including at a whole-period horizon. Do not substitute another product’s ETS coefficients, generic library predictions or hand-extended block means and call them Collabora results. Refit after correcting observations, but compare the same history, period and model before drawing conclusions from a change.

• Different-sized or nonnumeric history ranges: check both ranges exclude headers and still pair each value with its timestamp. The wrapper rejects incompatible shapes/numeric matrices; its internal error category is not a verified on-screen error label.
• Irregular timeline: establish a consistent positive step and aggregate duplicate observations deliberately. Missing dates and measured zero values are different inputs.
• Too little seasonal history: supply at least two complete cycles for the requested period. More data may improve estimation, but do not invent missing observations merely to satisfy a count.
• Invalid options: use the stated integer period and option codes. Fractional coercions of completion/aggregation/selectors are not a supported shortcut. A negative or fractional period is rejected by the pinned wrapper.
• Unexpected forecast: verify the argument order, fixed references, target units and explicit period before interpreting the fitted model. Training fit alone cannot establish future reliability.

The Help/source discrepancies above remain unresolved at the installed-product level. No invalid or potentially unsafe case was executed to manufacture an error table.

The contract uses Collabora’s English Help archived on October 17, 2026, scoped to Office 26.04, and the co-26.04 implementation at commit c1013fad0d38683630b48c01b6d5208b8d81181e, retrieved October 19. The latest Help URLs are mutable; the archived bytes establish what was reviewed. A later unsuccessful Help fetch is not fresh verification. First introduction and last functioning installed build are not established. Desktop, Online and mobile editions may differ.

The example calculations were checked using a bounded extraction of the pinned source, an independently written mathematical implementation, full optimizer-state comparisons and a separate arithmetic review. That establishes source-model expectations on the stated fixtures, not vendor certification or identical behavior in every build. These example calculations did not execute formulas in a Collabora application; no native result or screenshot was obtained. No installed RNG or recalculation behavior was tested. Parser/coercion behavior, arrays/spills, calendars, unsupported boundaries and exact displayed error labels remain unverified. The guide is a documentation-qualified tutorial; it is not a claim of native product testing.

• FORECAST.ETS.MULT.
• FORECAST.ETS.PI.ADD.
• FORECAST.ETS.PI.MULT.
• FORECAST.ETS.STAT.ADD.
• FORECAST.ETS.STAT.MULT.
• FORECAST.ETS.SEASONALITY.
• Linear FORECAST.
• Collabora official function Help, reviewed from the archived snapshot.
• Pinned Collabora implementation, including preprocessing, fitting and worksheet dispatch.