Predictive Analytics Intermediate

Predictive analytics uses historical data and ML models to forecast future network behavior. This enables proactive capacity planning, failure prevention, and intelligent alerting.

Time-Series Forecasting

Network metrics are inherently time-series data. Forecasting models capture trends, seasonality, and patterns to predict future values.

Capacity Prediction

Predict when network links or devices will reach capacity using trend analysis:

from prophet import Prophet
import pandas as pd

# Load 12 months of interface utilization data
df = pd.read_csv('interface_util.csv')
df.columns = ['ds', 'y']  # Prophet format

# Add capacity ceiling
df['cap'] = 100  # 100% utilization is the maximum

model = Prophet(growth='logistic', yearly_seasonality=True)
model.fit(df)

# Predict 90 days ahead
future = model.make_future_dataframe(periods=90)
future['cap'] = 100
forecast = model.predict(future)

# Find when 80% threshold is breached
breach = forecast[forecast['yhat'] >= 80].iloc[0]
print(f"Capacity threshold reached: {breach['ds']}")

Failure Probability Modeling

Predict the likelihood of device failure or link degradation using survival analysis and classification models. Key predictive features include device age, error rate trends, environmental factors, and firmware version.

Proactive Alerting