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The right forecasting method depends on the business decision, the data you have, and the pattern you need to predict. If relevant history is missing, structured expert judgment may be more useful than a statistical model. If you have numerical history, choose a time-series model for patterns in the target itself, a regression model for measurable outside drivers, or a mixed model that uses both.
These ten commonly taught approaches are a practical guide, not a universal taxonomy: some are individual techniques, while others are broad model families. A forecast is an estimate, so judge alternatives on the horizon and decision you actually face—not on how sophisticated the method sounds.
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Start by matching the method to the decision and data
Forecasting: Principles and Practice says quantitative forecasting is appropriate when numerical information about the past is available and it is reasonable to expect some aspects of past patterns to continue. Qualitative methods can help when historical data are absent, irrelevant, or unable to capture a changed situation. The same reference advises considering a method’s properties, accuracy, cost, and intended use (Forecasting: Principles and Practice).
| Approach | Data basis | What it can represent | Main consideration |
|---|---|---|---|
| Qualitative judgment | Expert knowledge, surveys, or other informed assessments | New conditions or offerings not reflected in the target’s history | Make assumptions and adjustments visible; judgment is not statistical evidence. |
| Time series | Past values of the target, recorded over time | Level, trend, seasonality, and autocorrelation | Can omit external drivers such as promotions or competitor activity. |
| Explanatory regression | Target values plus possible drivers | Measured relationships between the target and predictors | Future predictor values may be needed; a fitted relationship alone does not prove causation. |
| Mixed model | Target history and external predictors | Both recurring patterns in the target and relationships to drivers | Requires suitable history and usable predictor values. |
NIST/SEMATECH’s e-Handbook of Statistical Methods describes time-series analysis as accounting for internal structure such as autocorrelation, trend, or seasonal variation. That distinction helps frame the choice: are you forecasting from the target’s own sequence, from outside variables, or from both?
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Four qualitative forecasting methods
Qualitative approaches organize informed judgment rather than estimate a pattern from numerical history. They are especially relevant for a new product, a major market shift, or a situation where past observations no longer provide a sound basis. A textbook treatment of forecasting includes qualitative models and the sales-force composite among its methods (Quantitative Analysis for Management, 13th edition).
1. Executive judgment or jury of opinion
Gather estimates from managers with relevant knowledge—such as finance, operations, marketing, or product leadership—and make the assumptions behind the combined view explicit. This can be useful when a decision depends on new conditions that have not yet appeared in historical data. Treat the result as a reasoned estimate, not as a statistical forecast simply because several executives agree.
2. Delphi method
Use a structured, iterative process to gather expert views when expertise is distributed and a considered consensus is useful. The method is a form of qualitative forecasting; the available references establish it as part of that broad approach but do not establish a single required procedure. Be clear about how views are gathered and reconciled rather than presenting consensus as certainty.
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3. Sales-force composite
Combine estimates from frontline sales staff who understand customer conversations, local conditions, or pipeline changes. This can contribute useful context for a new offering or changing customer demand. Keep the underlying assumptions and any management adjustments visible: the aggregate is still a judgment-based input, not objective ground truth.
4. Consumer or market survey
Use stated purchase intentions or market research when historical sales do not yet reflect a new offering. Survey responses can inform a forecast, but intention is not the same as a completed purchase. Account for that distinction when translating responses into an expected level of demand.
Six quantitative approaches
Quantitative methods use numerical observations, but they differ in the information they rely on. The first five methods below focus on the target’s history; regression adds measured predictors. A practical list necessarily mixes levels of detail: a moving average is a specific technique, while ARIMA is a model family.
5. Moving average
A moving average forecasts by averaging a rolling window of recent observations, smoothing short-term noise. A shorter window reacts more quickly to new changes but may be noisier; a longer window smooths more but can lag when the level shifts. NIST includes moving-average and smoothing techniques in its time-series handbook (NIST/SEMATECH e-Handbook, time-series methods).
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6. Exponential smoothing
Exponential smoothing gives greater weight to recent observations than to older ones. The suitable form depends on the pattern:
- Simple exponential smoothing: a reasonably stable level without meaningful trend or seasonality.
- Holt’s method: a trend without seasonality.
- Damped Holt: a trend expected to weaken rather than continue indefinitely at the same pace.
- Additive Holt-Winters: seasonal effects whose size stays roughly constant as the series level changes.
- Multiplicative Holt-Winters: seasonal effects whose size changes in proportion to the series level.
- MSTL: a series with multiple seasonal patterns.
These distinctions are described in Microsoft’s Fabric planning documentation; they are guidance for the documented product, not a guarantee that a particular method will perform best for every business (Microsoft Learn: forecasting in Fabric).
7. Trend projection
Estimate a trend from historical observations and extend it forward when continuation is a defensible assumption. A projection can be useful for planning, but it does not explain why the trend occurred. A structural change—such as a new competitor, policy, or distribution channel—can make the past trend a poor guide to the future. Trend projection is included among the forecasting methods in the cited management textbook (Quantitative Analysis for Management, 13th edition).
8. Seasonal decomposition or seasonal-index models
Separate recurring seasonal movement from the underlying level or trend when calendar-related variation matters to the decision. Consider whether seasonal swings stay about the same size as the series grows or grow in proportion to it: that pattern informs whether additive or multiplicative seasonal treatment is appropriate. The seasonal forms are described in Microsoft’s Fabric planning documentation (Microsoft Learn: forecasting in Fabric).
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9. Regression or explanatory forecasting
Relate the target—such as sales—to predictors that may help explain its movement, such as price or promotions. This approach can be useful when the relationships can be measured and future predictor values can be estimated. If a model requires next quarter’s promotion plan or price, those inputs must be available or forecast too.
Including predictors does not, by itself, establish that they cause the outcome. Use causal language only when the research design supports a causal conclusion. Forecasting: Principles and Practice distinguishes explanatory models using other variables from time-series models based on the target’s past, and notes the challenge of knowing future predictor values (Forecasting: Principles and Practice).
10. ARIMA and seasonal ARIMA
ARIMA models use prior observations, differencing, and past forecast errors to represent dependence across time. Seasonal ARIMA adds seasonal structure. They are candidates when regularly observed history contains autocorrelation or recurring seasonal patterns that merit this additional modeling—not automatic upgrades over simpler methods.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose a starting model
- Define the decision and horizon. Specify what the forecast will inform—such as staffing, inventory, or budgeting—and how far ahead it must reach. A model should be assessed on that use, not just on how well it describes history.
- Check whether relevant numerical history exists. If it is missing or no longer relevant, begin with structured judgment, a survey, or another suitable qualitative input. If the target has useful observations over time, test a quantitative approach.
- Inspect the pattern you need to represent. A stable level may suit simple smoothing; a trend suggests a trend-capable method; recurring seasonal variation points to seasonal treatment; multiple seasonal cycles may call for a method such as MSTL.
- Decide whether outside drivers matter. If prices, promotions, or other predictors are important and their future values can be estimated, test an explanatory or mixed model. If prediction is the priority and future predictors are unavailable, a target-history method may be more practical.
- Compare plausible candidates on relevant data. Evaluate their forecast errors on data relevant to the intended horizon and decision. Historical fit alone does not settle usefulness, and no single accuracy score applies to every business forecast.
- Communicate uncertainty and assumptions. A point forecast is an estimate, not a promise. Where available, prediction intervals show a range of plausible future values; state the assumptions that could make the forecast less reliable.
There is no universally established minimum number of observations that makes each method appropriate. The usable history depends on the method, the regularity and quality of the data, and the pattern being estimated. Prefer the simplest approach that represents the relevant pattern well enough for the decision, then validate it against that decision’s needs.
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