Predictions - AURA Documentation

01

Energy Forecasting

85% accuracy · ±1 point

Your energy tomorrow is not random. It is the mathematical consequence of how you slept, how stressed you were, how much you moved, and what your body has learned to expect. AURA reads these signals and projects 24 hours ahead.

Ensemble Prediction

E(t+24) = 0.4·RF(sleep, stress, exercise, mood)
         + 0.4·GB(Δenergy, Δstress, history)
         + 0.2·LR(baseline_features)

Inputssleep_hours, sleep_quality, stress, exercise_minutes, mood, water_intake

Outputenergy_score (1–10), confidence_interval

ModelEnsemble (RF + GB + LR)

02

Mood Prediction

80% accuracy · ±1 point

Mood is not a mystery—it is a function. Sleep quality, social context, physical activity, and accumulated stress all feed into an emotional state that AURA can anticipate before you feel it. Knowing your mood tomorrow lets you prepare for it today.

Bayesian Conditional

P(mood_high | sleep_good, stress_low, exercise_yes) = 0.78
P(mood_low  | sleep_poor, stress_high)            = 0.82

Inputssleep_quality, stress_level, exercise_done, social_activity, prior_mood

Outputmood_score (1–10), mood_category

ModelBayesian Network + Ensemble

03

Stress Trend Analysis

75–85% accuracy

Stress does not arrive suddenly. It builds—slowly, invisibly—until it overwhelms. The Statistical Analyzer watches the trajectory of your stress over days and weeks, detecting the upward slope before it becomes a cliff.

Trend Detection

MA₇(stress) = (s₋₆ + s₋₅ + ... + s₀) / 7
slope       = LinearRegression(MA₇).coef_
alert       = slope > 0.15 AND MA₇ > 6.5

Inputsstress_history (30 days), sleep_trend, workload_proxy

Outputtrend_direction, peak_estimate, days_to_peak

ModelStatistical Analyzer (moving average + regression)

04

Sleep Quality Optimization

80% accuracy

Not all sleep is equal. Seven hours of fragmented sleep is not the same as seven hours of deep rest. AURA learns your personal sleep architecture—what conditions produce restorative sleep for you specifically—and recommends accordingly.

Quality Score

Q = 0.35·duration_score
  + 0.30·consistency_score   (same bedtime ±30min)
  + 0.20·pre_sleep_stress_inv
  + 0.15·exercise_lag_score  (exercise 4–6h before)

Inputsbedtime, wake_time, stress_evening, exercise_timing, caffeine_proxy

Outputpredicted_quality (1–10), optimal_bedtime, recommendations

ModelEnsemble + Reinforcement Learning

05

Burnout Risk Assessment

90% early warning

Burnout is the most dangerous pattern AURA watches for. It is the convergence of sustained high stress, declining energy, deteriorating sleep, and eroding mood—a slow collapse that most people don't see coming until it has already arrived.

Risk Score

R = w₁·(stress_14d_avg / 10)
  + w₂·(1 - energy_14d_avg / 10)
  + w₃·(1 - sleep_14d_avg / 9)
  + w₄·(1 - mood_14d_avg / 10)
  
alert_threshold = R > 0.72  →  HIGH RISK

Inputs14-day rolling averages: stress, energy, sleep, mood

Outputrisk_score (0–1), risk_level, contributing_factors

ModelStatistical Analyzer + weighted composite

06

Optimal Activity Timing

75% accuracy

The same activity at different times produces different results. A morning run when your energy is naturally high yields different outcomes than the same run at 9 PM when your body is winding down. AURA maps your chronotype and finds your windows.

Markov State Transition

P(high_performance | state=morning_high_energy) = 0.74
P(high_performance | state=evening_low_energy) = 0.31

optimal_window = argmax P(performance | time, current_state)

Inputsactivity_type, time_of_day, current_energy, historical_performance

Outputoptimal_time_window, expected_performance_score

ModelMarkov Chain + Reinforcement Learning

07

Recovery Needs

85% accuracy

Recovery is not laziness—it is a biological requirement. AURA tracks your accumulated stress debt and energy deficit, predicting when your body needs rest before you feel the crash. Prevention is always more effective than recovery.

Deficit Accumulation

stress_debt  = Σ max(0, stress_i - 5) for i in last_7_days
energy_debt  = Σ max(0, 6 - energy_i) for i in last_7_days
recovery_rec = stress_debt > 8 OR energy_debt > 6

Inputs7-day stress history, energy history, exercise_intensity_sum

Outputrecovery_needed (bool), recovery_type, estimated_days

ModelStatistical Analyzer + threshold rules

08

Performance Windows

80% accuracy

Peak performance is not a constant state—it is a window. AURA identifies the convergence of high energy, positive mood, low stress, and good sleep that creates your optimal cognitive and physical performance conditions.

Performance Index

PI = (energy/10)^0.4 · (mood/10)^0.3
   · (1 - stress/10)^0.2 · (sleep_quality/10)^0.1

peak_window = PI > 0.72  →  schedule demanding tasks

Inputsenergy, mood, stress, sleep_quality, time_of_day

Outputperformance_index (0–1), window_duration, task_recommendations

ModelEnsemble + composite index