Written by the Biznerdly Editorial Team. Technical review by Tanzila Tanjim Tusra, Researcher, a statistics graduate & partially qualified chartered accountant (ICAB) and data analyst. Published September, 2026. Last verified: September 2026, tested on Excel 365 and Excel 2021.
Excel's Forecast Sheet and Power BI's built-in forecast both run the same underlying algorithm family, exponential smoothing (ETS), so choosing between them is mostly about where you need the output, not which is "smarter." Python earns its place when you need a custom model, multiple input variables, or forecasting at a scale neither built-in tool handles well. Below is a transparent, reproducible test: the same 48-month dataset, the same train/test split, and the actual accuracy numbers, not marketing claims.
Most 2026 "AI forecasting" articles describe Excel, Python, and Power BI in isolation and never actually run the same numbers through all three. That's the gap this article closes. Everything below is reproducible: the synthetic dataset, the train/test split, and the accuracy metric are all downloadable, so you're not being asked to take our word for it.
What each tool actually does under the hood
This is the part most comparison articles skip, and it changes how you should think about the "which is smarter" question entirely.
- Excel's Forecast Sheet (Data → Forecast Sheet) uses
FORECAST.ETS, an exponential smoothing algorithm that automatically detects trend and seasonality in a single time series. - Power BI's built-in forecast (Analytics pane → Forecast, on a line chart) uses the same family of algorithm: ETS AAA (additive trend, additive seasonality) for seasonal data, or ETS AAN when no seasonality is detected. It is, functionally, the same statistical approach as Excel's Forecast Sheet, just running natively inside a Power BI report instead of a worksheet.
- Python gives you access to a much broader family of models, including ARIMA/SARIMA (statistical, tuned by hand or with auto-selection tools) and Prophet (Meta's open-source library, built for business time series with multiple seasonal patterns and holiday effects). Python is also where you'd go for models that use more than just historical values of the series itself, such as incorporating marketing spend or macroeconomic indicators as inputs.
The practical implication: if your forecasting need is "one time series, one seasonal pattern, quick answer," Excel and Power BI are running essentially the same math, so the choice between them comes down to where the forecast needs to live, not which one is more advanced. Python only pulls meaningfully ahead once your problem outgrows a single-variable, single-seasonality forecast.
Methodology: the dataset, the split, the metric
Full transparency on how this test was built, since a comparison without a visible methodology isn't worth trusting.
- Dataset: 48 months of synthetic monthly revenue (January 2022 through December 2025), constructed with a linear upward trend, a repeating yearly seasonal pattern (including a December spike, modeling a retail-style holiday effect), and random noise. Built with a fixed random seed so the exact same numbers are reproducible by anyone who downloads the dataset.
- Train/test split: the first 36 months (2022 to 2024) were used to fit each model; the final 12 months (2025) were held out and never seen by either model during fitting, then used purely to score forecast accuracy.
- Models compared: an ETS model (additive damped trend, additive seasonality, 12-month seasonal period), representing what Excel's Forecast Sheet and Power BI's native forecast would produce, run via Python's
statsmodelslibrary for a controlled, identical comparison environment; and a SARIMA model (order 1,1,1, seasonal order 1,1,1,12), representing a generic, off-the-shelf Python statistical model, not hand-tuned for this specific dataset. - Accuracy metric: MAPE (Mean Absolute Percentage Error), the average of the absolute percentage difference between each forecasted month and the actual value for that month. Lower is better.
One honest caveat worth stating plainly: the SARIMA model here was deliberately left untuned, using a common textbook starting specification, to represent what a Python user gets without extra effort. A properly tuned SARIMA, or a Prophet model with holiday effects configured, could reasonably outperform the ETS result on this same dataset. This test measures out-of-the-box performance, not the ceiling of what each tool is capable of.
The results
| Month | Actual | ETS forecast | SARIMA forecast |
|---|---|---|---|
| Jan 2025 | $64,713 | $64,907 | $66,512 |
| Feb 2025 | $58,620 | $61,420 | $64,896 |
| Mar 2025 | $66,468 | $66,931 | $69,502 |
| Apr 2025 | $72,455 | $72,300 | $75,266 |
| May 2025 | $77,008 | $74,509 | $77,297 |
| Jun 2025 | $79,937 | $78,996 | $81,272 |
| Jul 2025 | $73,307 | $73,617 | $75,146 |
| Aug 2025 | $66,748 | $67,947 | $73,003 |
| Sep 2025 | $62,002 | $66,504 | $67,298 |
| Oct 2025 | $60,120 | $63,536 | $62,988 |
| Nov 2025 | $71,129 | $72,222 | $75,784 |
| Dec 2025 | $94,466 | $88,567 | $92,495 |
| Model | MAPE | Represents |
|---|---|---|
| ETS (damped trend, additive seasonality) | 2.78% | Excel Forecast Sheet / Power BI native forecast |
| SARIMA (1,1,1)(1,1,1,12), untuned | 4.82% | Generic, off-the-shelf Python statistical model |
On this particular dataset, the no-code ETS approach that both Excel and Power BI use natively actually outperformed a generic, untuned Python model. That's a genuinely useful finding precisely because it cuts against the assumption that "Python" automatically means "more accurate." It doesn't, by default. What Python offers is the ability to go further when a plain ETS model isn't enough, not a guaranteed accuracy advantage on every problem.
Effort, skill, and cost comparison
| Factor | Excel Forecast Sheet | Python (statsmodels/Prophet) | Power BI native forecast |
|---|---|---|---|
| Setup time | Under a minute | Requires environment setup (Python, libraries) unless already in place | Under a minute, on an existing line chart |
| Skill required | None; select data and click Forecast Sheet | Python and basic time-series knowledge | None; toggle on in the Analytics pane |
| Handles multiple input variables | No, single time series only | Yes, e.g. marketing spend, pricing, macro indicators as regressors | No, single time series only |
| Handles many products/series at once | Manual, one Forecast Sheet per series | Yes, loop through hundreds of series programmatically | One series per visual, though a report can hold many visuals |
| Lives natively in a refreshable dashboard | No, static once generated | Requires integration work | Yes, recalculates on every data refresh |
| Cost | Included in Excel | Free (open-source libraries), your time to set up | Included in Power BI |
When to use each tool
- Use Excel's Forecast Sheet for a fast, one-off forecast of a single metric, when you need an answer in the next five minutes and don't need it to live in a shared dashboard.
- Use Power BI's native forecast when the same forecast needs to sit inside a report other people refresh and view regularly, since it recalculates automatically and requires no code.
- Use Python when you need more than one input variable, need to forecast many product lines or regions programmatically rather than one at a time, or need a specific business context built in, like holiday effects, promotional periods, or external economic indicators.
For Microsoft's own documentation on the built-in tools, see Excel's Forecast Sheet documentation
Downloadable dataset and scorecard
- Shared Monthly Revenue Test Dataset (CSV), the exact 48-month synthetic revenue series used in this test, with the fixed random seed noted so you can regenerate it exactly. [DOWNLOAD LINK]
- Forecasting ETS vs SARIMA python notebook
Frequently asked questions
Is Excel's Forecast Sheet accurate enough for business planning?
For a single, reasonably well-behaved time series with a clear trend and seasonal pattern, yes; in our test it scored a 2.78% MAPE, which is a strong result. It struggles more with multiple input variables, irregular patterns, or forecasting many series at once, which is where Python or Power BI's automation capabilities take over.
When should I use Python instead of Excel for forecasting?
Switch to Python once you need more than a single input variable, need to forecast many product lines or regions without building a separate Forecast Sheet for each, or need a specific modeling technique like Prophet's holiday-effects handling that Excel's Forecast Sheet doesn't offer.
Does Power BI use real machine learning or just Excel's ETS model under the hood?
Power BI's built-in forecast (in the Analytics pane) uses exponential smoothing (ETS), the same algorithm family as Excel's Forecast Sheet, not a separate machine learning model. For genuine machine learning-based forecasting inside Power BI, you'd need to integrate a Python or R script, or connect to Azure Machine Learning.
Is a Python model always more accurate than Excel or Power BI's built-in forecast?
No, and our own test demonstrates this: a generic, untuned Python SARIMA model performed worse (4.82% MAPE) than the ETS approach both Excel and Power BI use natively (2.78% MAPE) on the same dataset. Python's advantage is flexibility and scale, not automatic accuracy.
Where to go from here
- Variance Analysis in Excel and Power Pivot, for building the materiality-flagged report this article's commentary is written from.
- Price-Volume-Mix Variance Analysis, for giving your AI-drafted commentary a real driver to explain instead of a single blended number.
- Power Pivot DAX for Pivot Table Users, the DAX foundation this article builds on.
- Price-Volume-Mix Variance Analysis, since understanding what drove past variance is often the first input into building a credible forecast assumption.
0 Comments