Time-Series Anomaly Detection System - 30/01/2025 02:45 EST

1. Understand the Requirements: The project involves: Analyzing daily variances of a user activity metric from a time-series dataset. Using machine learning models like RandomForestClassifier with adversarial variations. Generating alerts when today's variance surpasses a specified threshold. Including a forecasting component for predictions. Determining the threshold during the model's training phase. 2. Plan the Development Process Data Collection and Preprocessing Load the time-series user activity data. Clean and preprocess it (handle missing values, normalization, etc.). Feature Engineering Compute daily variances of the user activity metric. Extract meaningful features like moving averages or trend components. Model Development Train a model (e.g., RandomForestClassifier or similar). Include techniques for anomaly detection or adversarial validation. Forecasting Implement a forecasting model (e.g., ARIMA, LSTM, or Prophet) for time-series predictions. Threshold Determination Use metrics like AUC (Area Under Curve) to determine thresholds for triggering alerts. Alert System Develop an alerting mechanism (email, SMS, or push notifications). Evaluation and Deployment Evaluate model performance and refine it. Deploy the solution in a production environment. 3. Implementation Steps A)Set Up Your Environment pip install numpy pandas scikit-learn matplotlib seaborn statsmodels prophet For advanced forecasting models, install TensorFlow or PyTorch. B) Data Preprocessing import pandas as pd import numpy as np # Load the dataset data = pd.read_csv("[login to view URL]") # Replace with your dataset file # Handle missing values [login to view URL](method='ffill', inplace=True) # Calculate daily variance data['daily_variance'] = data['activity_metric'].rolling(window=7).var() C)Train the ML Model from [login to view URL] import RandomForestClassifier from sklearn.model_selection import train_test_split from [login to view URL] import roc_auc_score # Feature selection features = ['daily_variance', 'other_feature_1', 'other_feature_2'] # Add relevant features X = data[features] y = data['alert_label'] # Target label for anomaly detection # Split data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Train model model = RandomForestClassifier(random_state=42) [login to view URL](X_train, y_train) # Evaluate model y_pred = model.predict_proba(X_test)[:, 1] auc = roc_auc_score(y_test, y_pred) print("AUC Score:", auc) D)Add Forecasting from prophet import Prophet # Prepare data for forecasting forecast_data = data[['date', 'activity_metric']].rename(columns={'date': 'ds', 'activity_metric': 'y'}) # Train forecasting model model = Prophet() [login to view URL](forecast_data) # Forecast future values future = model.make_future_dataframe(periods=30) forecast = [login to view URL](future) E)Alert System def send_alert(message): print(f"ALERT: {message}") # Replace with email or SMS API # Check for anomalies threshold = 1.5 # Example threshold if data['daily_variance'].iloc[-1] > threshold: send_alert("Daily variance exceeded the threshold!") 4. Deploy the System Use a cloud platform (e.g., AWS, Azure, GCP) for deployment. Set up automated scripts or APIs for real-time data input and alerts.