Hp inc.: 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. 1 sampled paths extend beyond the displayed chart range; the plotted intervals use the full simulations. Open full-size chart.
| Quarter ending | Predictive median | 80% predictive interval | 95% predictive interval |
|---|---|---|---|
| 2026-10-31 | $16.624B | $15.787B–$17.519B | $15.128B–$18.240B |
| 2027-01-31 | $16.422B | $15.100B–$17.968B | $14.207B–$19.102B |
| 2027-04-30 | $15.865B | $14.198B–$17.804B | $13.212B–$19.230B |
| 2027-07-31 | $16.439B | $14.378B–$18.880B | $13.110B–$20.799B |
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 | 1.30% | 3.50% | 6.32% | 4/4 | 4/4 |
| 2 quarters | 4 | 5.08% | 2.46% | 9.20% | 4/4 | 4/4 |
| 3 quarters | 4 | 5.63% | 5.54% | 9.98% | 4/4 | 4/4 |
| 4 quarters | 4 | 5.58% | 6.87% | 6.87% | 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 1.003 · minimum bulk ESS 3942 · minimum tail ESS 2925 · 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.
Hp inc. HPQ
Revenue Intelligence Report • 47 quarters of SEC filing data • Updated 2026-10-14 • Data through 2026-07-31
Earlier summary (2026-10-14): Hp inc.'s latest quarterly revenue is $15.7B for the period ending 2026-07-31. The model projects $58.1B across the next four quarters (-1.7% versus the preceding four quarters). The forecast uses SG&A data; model selection and estimation exclude the latest quarter, whose retrospective holdout error is 8.6%. 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 2026-07-31. 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 |
|---|---|---|---|---|---|
| Q3 FY2026 Period end 2026-07-31 |
$14.33B | $15.68B | $13.18B–$15.48B | +2.9% | ✗ Outside range |
| Q4 FY2026 Period end 2026-10-31 |
$14.44B | $12.81B–$16.06B | -1.4% | ||
| Q1 FY2027 Period end 2027-01-31 |
$14.63B | $12.64B–$16.62B | +1.3% | ||
| Q2 FY2027 Period end 2027-04-30 |
$14.67B | $12.37B–$16.97B | +1.8% | ||
| Q3 FY2027 Period end 2027-07-31 |
$14.41B | $11.84B–$16.98B | -8.1% |
Seasonal Factors
| Fiscal Quarter | Seasonal Factor | vs Trend | Interpretation | Obs. |
|---|---|---|---|---|
| FQ1 (Nov–Jan) | 0.9867 | -1.3% | In line with trend | 11 |
| FQ2 (Feb–Apr) | 0.9781 | -2.2% | In line with trend | 11 |
| FQ3 (May–Jul) | 0.9966 | -0.3% | In line with trend | 10 |
| FQ4 (Aug–Oct) | 1.0283 | +2.8% | In line with trend | 10 |
Spending and Revenue
Time-Varying Spending Associations
Current SG&A elasticity: 0.1538x • R&D elasticity: -0.131x
The model-term chart shows relative absolute term magnitudes. Its percentages are not shares of revenue or causal contributions to growth.
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