What is EPD's forecasted revenue?

Enterprise Products Partners L.p. is forecasted to generate $54.1B in next-four-quarter revenue, representing -7.5% year-over-year growth.

How accurate is the EPD revenue forecast model?

The model has 9.4% training MAPE (Mean Absolute Percentage Error). In a holdout test, it predicted $14B versus the actual $18B.

Enterprise Products Partners L.p. EPD

Revenue Intelligence Report • 62 quarters of SEC filing data • Updated 2026-10-14 • Data through 2026-06-30

Enterprise Products Partners L.p.'s latest quarterly revenue is $18.3B for the period ending 2026-06-30. The model projects $54.1B across the next four quarters (-7.5% versus the preceding four quarters). The forecast uses G&A data; model selection and estimation exclude the latest quarter, whose retrospective holdout error is 22.8%. These are statistical associations, not estimates of the causal effect of spending.

Investment Thesis

The ARDL forecast is based on reported revenue and G&A data through 2026-06-30. Training MAPE measures fit to historical data; the separately reported holdout error measures a withheld quarter. Spending coefficients describe associations and do not establish causation.

Next 4 Quarters

$54.1B

-7.5% YoY

G&A Elasticity

0.05x

Training Fit

9.4% MAPE

Retrospective holdout (Q2 FY2026): The model predicted $14.11B vs the actual $18.27B — an error of 22.8%.
Retrospective one-quarter holdout: model fitting and selection exclude this quarter. Historical inputs use the latest available restatements, so this is not a preserved real-time forecast.

Note: The current filings use G&A (GeneralAndAdministrativeExpense). Spending estimates refer to this expense measure, not separately reported SG&A.

Note: Enterprise Products Partners L.p. does not report R&D expenses separately. The model uses the available expense measure without a separate R&D term.

Investor insight: Actual revenue ($18B) came in 23% above the spending-based forecast ($14B). This suggests that Enterprise Products Partners L.p.'s recent revenue growth is driven significantly by external demand factors — such as market pricing, product cycle tailwinds, or structural demand shifts — beyond what its R&D and SG&A spending alone would predict.

Revenue Forecast

Quarterly Detail

Quarter	Model Forecast	Actual	95% Range	YoY Growth	Status
Q2 FY2026 Period end 2026-06-30	$14.11B	$18.27B	$11.17B–$17.82B	+24.2%	✗ Outside range
Q3 FY2026 Period end 2026-09-30	$13.85B		$9.95B–$19.26B	+15.2%
Q4 FY2026 Period end 2026-12-31	$13.61B		$9.08B–$20.39B	-1.3%
Q1 FY2027 Period end 2027-03-31	$13.40B		$8.40B–$21.37B	-6.9%
Q2 FY2027 Period end 2027-06-30	$13.21B		$7.84B–$22.27B	-27.7%

Seasonal Factors

Multiplicative seasonal adjustment: These factors capture Enterprise Products Partners L.p.'s systematic quarterly revenue patterns relative to the trend model. A factor of 1.05 means that quarter typically runs 5% above the underlying trend; 0.95 means 5% below. Factors are computed as the median of (actual / fitted) across all available quarters.

Fiscal Quarter	Seasonal Factor	vs Trend	Interpretation	Obs.
FQ1 (Jan–Mar)	1.0311	+3.1%	+3.1% above trend	15
FQ2 (Apr–Jun)	0.9346	-6.5%	-6.5% below trend	15
FQ3 (Jul–Sep)	1.038	+3.8%	+3.8% above trend	15
FQ4 (Oct–Dec)	1.0492	+4.9%	+4.9% above trend	15

Spending and Revenue

Reading this chart: Each line shows the cumulative revenue association with a 1% change in spending over subsequent quarters. These estimates describe historical associations and do not establish causation.

Time-Varying Spending Associations

Time-varying analysis: A penalized spline model (GAM) tracks the association between log spending and log revenue over 62 quarters. Its coefficients describe conditional historical associations, not causal returns on spending.
Current G&A elasticity: 0.129x
The model-term chart shows relative absolute term magnitudes. Its percentages are not shares of revenue or causal contributions to growth.

Want this analysis for your portfolio?

I build custom revenue intelligence reports for investors and companies using SEC filing data, econometric modeling, and AI-powered insights.

Get in Touch