Predictive Insights
AI-powered forecasting and anomaly detection
ARIMA Time Series
activeType:Statistical
Accuracy:87.3%
MAE:3.20
RMSE:4.80
Last Trained:2/11/2026
Random Forest Regressor
activeType:Machine Learning
Accuracy:91.5%
MAE:2.10
RMSE:3.20
Last Trained:2/12/2026
Neural Network (LSTM)
trainingType:Deep Learning
Accuracy:93.8%
MAE:1.80
RMSE:2.70
Last Trained:2/13/2026
30-Day Forecast
1
6
11
16
21
26
Predicted Value
Confidence Interval
Predicted Anomalies
HIGH2/18/2026
Patient Wait Time
Unexpected spike in emergency admissions
Predicted:42.5
Actual:58.3
Deviation:37.2%
MEDIUM2/25/2026
Throughput
Equipment maintenance scheduled
Predicted:125
Actual:98
Deviation:-21.6%
CRITICAL3/3/2026
Defect Rate
Material quality issue detected
Predicted:2.1
Actual:4.8
Deviation:128.6%
Trend Analysis
Overall Trend:↓ declining
Velocity:-0.5/day
Acceleration:-0.02/day²
Seasonality:Yes (7 days)
Confidence:87%
Key Drivers
Staffing Optimization35%
Process Improvements28%
Technology Adoption22%
Training Programs15%