Service Outage Prediction
“Which areas will experience service degradation?”
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
By submitting, you accept the Terms and Privacy Policy.
Loved by data scientists, ML engineers & CXOs at
A real-world example
Which areas will experience service degradation?
Network outages cost carriers $5,600 per minute in lost revenue, plus SLA penalties and churn acceleration. A carrier experiencing 200 outage events per year (average 45 minutes each) loses $30M directly and $90M in downstream churn. NOC teams react to alarms after degradation has begun. The predictive signal is in the convergence of equipment age, weather patterns, traffic load, and cascading failure histories across the network topology.
How KumoRFM solves this
Graph-learned network intelligence across your entire subscriber base
Kumo builds a network topology graph connecting towers, equipment, weather zones, and ticket history. It learns that when a specific equipment model at towers in a weather-exposed region shows 15% traffic increase above baseline during approaching storms, degradation follows within 4 hours. The graph propagates risk: when one tower in a cluster fails, adjacent towers absorb traffic and their own failure probability spikes. Traditional threshold-based monitoring cannot model these cascading dependencies.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
TOWERS
| tower_id | region | equipment_model | install_year | last_maintenance |
|---|---|---|---|---|
| TWR401 | Northeast | Ericsson 6701 | 2019 | 2024-11-15 |
| TWR402 | Northeast | Nokia AirScale | 2022 | 2025-01-20 |
| TWR403 | Midwest | Ericsson 6701 | 2018 | 2024-08-10 |
EQUIPMENT
| equip_id | tower_id | component | age_months | failure_history |
|---|---|---|---|---|
| EQ01 | TWR401 | Power amplifier | 62 | 2 failures |
| EQ02 | TWR402 | Antenna array | 28 | 0 failures |
| EQ03 | TWR403 | Power amplifier | 74 | 4 failures |
WEATHER
| weather_id | region | date | condition | wind_mph | temp_f |
|---|---|---|---|---|---|
| W01 | Northeast | 2025-03-05 | Ice storm | 35 | 28 |
| W02 | Midwest | 2025-03-05 | Clear | 8 | 42 |
TICKETS
| ticket_id | tower_id | type | created_date | severity |
|---|---|---|---|---|
| TK01 | TWR401 | Performance alarm | 2025-03-01 | P2 |
| TK02 | TWR403 | Hardware alarm | 2025-02-28 | P3 |
TRAFFIC
| traffic_id | tower_id | timestamp | load_pct | dropped_sessions |
|---|---|---|---|---|
| TF01 | TWR401 | 2025-03-04 18:00 | 82% | 12 |
| TF02 | TWR402 | 2025-03-04 18:00 | 55% | 0 |
| TF03 | TWR403 | 2025-03-04 18:00 | 68% | 3 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(TOWERS.OUTAGE_EVENT, 0, 24, hours) FOR EACH TOWERS.TOWER_ID WHERE TRAFFIC.LOAD_PCT > 40
Prediction output
Every entity gets a score, updated continuously
| TOWER_ID | REGION | CURRENT_LOAD | OUTAGE_PROB_24H |
|---|---|---|---|
| TWR401 | Northeast | 82% | 0.78 |
| TWR402 | Northeast | 55% | 0.22 |
| TWR403 | Midwest | 68% | 0.31 |
Understand why
Every prediction includes feature attributions — no black boxes
Tower TWR401 -- Northeast, Ericsson 6701
Predicted: 78% outage probability in next 24 hours
Top contributing features
Approaching ice storm severity
35 mph wind, 28F
29% attribution
Equipment age and failure history
62mo, 2 prior failures
24% attribution
Current load vs baseline
+22% above normal
19% attribution
Adjacent tower status
1 of 3 neighbors degraded
16% attribution
Recent performance alarm
P2 ticket 4 days ago
12% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: A carrier with 50,000 towers that predicts outages 24 hours before they occur prevents 60% of unplanned downtime, saving $30M in direct costs and $90M in churn-driven revenue loss. Kumo models cascading failure risk across the network topology, combining weather, equipment age, and traffic patterns that threshold monitoring cannot anticipate.
Related use cases
Explore more telecom use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
