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Anomaly Detection in Time Series Data
Model Description
This model is designed for Anomaly Detection in Time Series Data using a simple feedforward neural network. It takes a time series dataset as input and identifies anomalies based on learned patterns. The model is implemented in PyTorch and trained using synthetic data.
Model Architecture
- Input Layer: 10-dimensional feature vector
- Hidden Layer: 128 neurons, ReLU activation
- Output Layer: 1 neuron, Sigmoid activation (for binary classification)
Training Details
- Dataset: Synthetic time series data (100 samples, 10 features each)
- Loss Function: Binary Cross Entropy Loss (BCELoss)
- Optimizer: Adam (learning rate = 0.001)
- Epochs: 10
How to Use
Load the Model
import torch
import torch.nn as nn
# Define model architecture
class AnomalyDetectionModel(nn.Module):
def __init__(self, input_dim=10):
super(AnomalyDetectionModel, self).__init__()
self.fc = nn.Sequential(
nn.Linear(input_dim, 128),
nn.ReLU(),
nn.Linear(128, 1),
nn.Sigmoid()
)
def forward(self, x):
return self.fc(x)
# Load the model
model = AnomalyDetectionModel()
model.load_state_dict(torch.load("anomaly_detection_model.pth"))
model.eval()
Make Predictions
# Dummy input sample (10 features)
input_data = torch.rand(1, 10)
prediction = model(input_data).item()
if prediction > 0.5:
print("Anomaly detected!")
else:
print("No anomaly detected.")
Applications
- Fraud detection in financial transactions
- Network intrusion detection
- Predictive maintenance in IoT systems
- Fault detection in industrial equipment
Limitations
- The model was trained on a synthetic dataset; real-world datasets may require retraining.
- Hyperparameter tuning and feature engineering can improve performance.
License
MIT License
Author
ShreyasP123
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