Exploring Anomaly Detection In Time Series Data Using Lstm

Introduction

In the world of artificial intelligence and machine learning, anomaly detection stands as a method to identify unusual patterns or outliers in the provided data. Aspects where anomaly detection finds a significant application include fraud detection, fault detection, system health monitoring, event detection in sensor networks, and detecting abnormalities in medical imaging. This post would delve into undertaking anomaly detection in time-series data using Long Short-Term Memory (LSTM), an artificial recurrent neural network architecture used in deep learning.

What is LSTM?

Before exploring the application of LSTM in anomaly detection, it is essential to understand its core concept. LSTM, unlike the traditional feed-forward neural networks, has feedback connections that enable it to process sequences of data, making it suitable for time-series forecasting.

# A basic LSTM model using Keras in python

from keras.models import Sequential
from keras.layers import LSTM, Dense

model = Sequential([
    LSTM(100, activation='relu', input_shape=(3,1)),
    Dense(1)
])
model.compile(optimizer='adam', loss='mse')

The above python code snippet provides a base model of LSTM with the Keras package. The model is compiled using the 'adam' optimizer and aims at minimizing the mean squared error.

Anomaly Detection in Time Series Data with LSTM

Time-Series data is a sequence of data points, measured typically over successive time. Forecasting time series data using LSTM is based on the idea of using past data to predict future points with the sequence data. Anomaly points or outliers are detected once these predicted points differ from the actual points beyond a certain level.

Implementation

Our data for this demonstration would showcase a single feature with a time-step of 3.

import numpy as np

# training data
x = np.array([[110, 125, 133], [125, 133, 146], [133, 146, 158], [146, 158, 172]])

# reshaping input to be [samples, time steps, features]
x = np.reshape(x, (x.shape[0], x.shape[1], 1))

# training labels 
y = np.array([146, 158, 172, 187])

model.fit(x, y, epochs=1000, verbose=0)

test_input = np.array([158, 172, 187])
test_input = test_input.reshape((1, 3, 1))
test_output = model.predict(test_input, verbose=0)

print(test_output)

The 'model.fit' function initiates the training process with the provided input and corresponding labels for 1000 epochs. The prediction is consequently tested over a new data set.

Anomalies can be found out by calculating the error rate (difference of predicted and actual results). If the error rate escalates beyond a certain pre-defined threshold, it can be identified as an anomaly.

Note: This is a basic presentation of anomaly detection in time series data using LSTM. The complexity can be extended based on the structure and requirements of the precise data you are working with.