Trane Technologies Plc: possible revenue paths
Forecasts are conditional on the listed data cutoff. This rollout reuses the existing company datasets; it is not a fresh SEC-data refresh. Some forecast quarters may already have ended by the computation date. Read each target date before treating an estimate as a future-quarter outlook.
PyMC seasonal AR(1) quarterly log-revenue growth with Student-t shocks; posterior predictive paths include parameter uncertainty and new shocks at every horizon.
Each line is one possible future revenue path. The 80% and 95% intervals are pointwise model probabilities, not simultaneous coverage for the whole path. The median is not an expected dollar mean. Open full-size chart.
| Quarter ending | Predictive median | 80% predictive interval | 95% predictive interval |
|---|---|---|---|
| 2026-09-30 | $6.244B | $5.646B–$6.890B | $5.270B–$7.405B |
| 2026-12-31 | $5.814B | $5.060B–$6.693B | $4.629B–$7.352B |
| 2027-03-31 | $5.288B | $4.441B–$6.310B | $4.007B–$7.054B |
| 2027-06-30 | $6.670B | $5.464B–$8.150B | $4.771B–$9.402B |
Future period-end dates are approximate: month-end issuers retain month-end dates; weekly issuers advance by 13 weeks. Future 53-week adjustments are not known.
Historical forecast checks
Each historical cutoff refits the model using only earlier observations. Across the available outcomes, the 80% intervals covered 16/16 and the 95% intervals covered 16/16. Small samples and overlapping outcomes do not establish nominal coverage; covering every outcome can indicate overly wide intervals.
| Horizon | Cases | Bayesian median MAPE | Last reported revenue MAPE | Same quarter last year MAPE | 80% covered | 95% covered |
|---|---|---|---|---|---|---|
| 1 quarter | 4 | 4.72% | 2.58% | 10.22% | 4/4 | 4/4 |
| 2 quarters | 4 | 2.81% | 7.45% | 8.70% | 4/4 | 4/4 |
| 3 quarters | 4 | 6.23% | 13.69% | 9.31% | 4/4 | 4/4 |
| 4 quarters | 4 | 6.29% | 9.87% | 9.87% | 4/4 | 4/4 |
All point-forecast errors use the same historical cases. A simple baseline may have lower error than the Bayesian median; this rollout does not establish that the Bayesian model is superior for every company.
Up to four historical cutoffs spaced four quarters apart, with at least 16 training quarters; full four-quarter outcomes required. Fixed specification, no winner selection.
These are retrospective checks using latest-restated data, not forecasts archived before their outcomes. Results from DDOG or another company are not transferred to this company.
Sampling diagnostics and limitations
Maximum R-hat 1.001 · minimum bulk ESS 2219 · minimum tail ESS 974 · divergent transitions 0. The required sampling checks passed. These diagnose computation, not predictive calibration.
Fixed priors
mu is long-run quarterly log growth; phi is growth persistence; sigma is the shock scale; season gives quarterly effects; shock_df controls tail thickness.
| Parameter | Prior / fixed assumption |
|---|---|
| mu | Normal(0,0.10) |
| phi | Normal(0,0.5) truncated to [-0.98,0.98] |
| sigma | HalfNormal(0.10) |
| season | 4 Normal(0,0.15), centered to sum zero |
| shock_df | 5 |
Future regime changes and uncertainty over alternative model structures remain outside these intervals. The model should be scored on subsequent releases, and its forecast vintage preserved.
- Revenue-history baseline; does not identify customer-level discounts, acquisitions or future structural breaks.
- Intervals are pointwise, conditional on this model and priors, not guaranteed coverage or simultaneous path bounds.
- Latest-restated historical data; retrospective tests are not archived real-time forecasts. Small test samples cannot validate nominal coverage.
- Student-t log shocks can make dollar means undefined; point forecasts are medians.
- Input source dates are shown separately from generation date; this rollout is not a fresh SEC download.
The report below retains its original forecasts, validation and company-specific discussion. Those revenue estimates use a different model or information cutoff; they are separate from the Bayesian outlook above. Any EPS model below has not been recalculated using the new Bayesian revenue paths.
Trane Technologies Plc TT
Revenue Intelligence Report • 73 quarters of SEC filing data • Updated 2026-10-14 • Data through 2026-06-30
Earlier summary (2026-10-14): Trane Technologies Plc's latest quarterly revenue is $6.4B for the period ending 2026-06-30. The model projects $22.9B across the next four quarters (+3.1% versus the preceding four quarters). The forecast uses SG&A data; model selection and estimation exclude the latest quarter, whose retrospective holdout error is 6.2%. These are statistical associations, not estimates of the causal effect of spending.
Investment Thesis
The ARDL forecast is based on reported revenue and SG&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.
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.
Revenue Forecast
Quarterly Detail
| Quarter | Model Forecast | Actual | 95% Range | YoY Growth | Status |
|---|---|---|---|---|---|
| Q2 FY2026 Period end 2026-06-30 |
$5.96B | $6.35B | $5.60B–$6.31B | +3.7% | ✗ Outside range |
| Q3 FY2026 Period end 2026-09-30 |
$5.95B | $5.45B–$6.45B | +3.6% | ||
| Q4 FY2026 Period end 2026-12-31 |
$5.46B | $4.84B–$6.07B | +6.0% | ||
| Q1 FY2027 Period end 2027-03-31 |
$5.29B | $4.59B–$6.00B | +6.5% | ||
| Q2 FY2027 Period end 2027-06-30 |
$6.20B | $5.42B–$6.99B | -2.3% |
Seasonal Factors
| Fiscal Quarter | Seasonal Factor | vs Trend | Interpretation | Obs. |
|---|---|---|---|---|
| FQ1 (Jan–Mar) | 0.9852 | -1.5% | In line with trend | 17 |
| FQ2 (Apr–Jun) | 1.0209 | +2.1% | In line with trend | 17 |
| FQ3 (Jul–Sep) | 1.0018 | +0.2% | In line with trend | 17 |
| FQ4 (Oct–Dec) | 1.0055 | +0.5% | In line with trend | 17 |
Spending and Revenue
Time-Varying Spending Associations
Current SG&A elasticity: 0.46x
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