1. AI-Powered Anomaly
Detection in Time Series
Data
Welcome! In this presentation, we'll embark on a technical exploration
of AI's remarkable ability to detect anomalies within time series data.
We will focus on empowering proactive decision-making through
advanced algorithms and techniques. We will also explore how AI
adapts to complex, noisy time series data and scales to real-time
applications.
Group no.17:
Kapil Kanjani,
Arayan Nagdev,
Yash Lalwani,
Mohit Budhani.
2. Introduction to Anomaly
Detection
Anomaly Detection
Identifying rare, unexpected
patterns in data can be critical
for proactive decision-making.
Time Series Focus
We will focus on time series
data (sequential data over
time).
Importance of AI
AI and machine learning enhance our ability to detect deviations from
normal patterns.
Anomaly detection is the process of identifying patterns in data that do not
conform to expected behavior. It's a critical capability across diverse domains,
from finance and healthcare to cybersecurity and industrial monitoring. AI-
powered anomaly detection is critical for spotting irregularities.
3. Understanding Time Series Data
Definition
Time series data comprises
data points collected or
recorded over time.
Examples
Examples include stock
prices, sensor readings,
and network traffic.
Characteristics
Temporal dependency,
trends, and seasonality
define time series data.
Challenges
Distinguishing noise from
true anomalies remains a
primary challenge.
Time series data is a sequence of data points indexed in time order. It's characterized by temporal dependency, meaning that
each data point depends on the previous ones. A major challenge lies in accurately separating genuine anomalies from
background noise, which requires sophisticated analytical approaches.
4. Defining Anomalies in Detail
Point Anomalies
Single data points that deviate
significantly from the norm.
Contextual Anomalies
Data points that are unusual
within a specific context.
Collective Anomalies
Groups of data points that, as a whole, deviate from the norm.
Anomalies are deviations from expected patterns. The goal is to detect
these anomalies without being overwhelmed by false positives. The
complexity of anomaly detection varies based on the scale and domain
of the data.
5. The Power of AI in Anomaly
Detection
1 Traditional Methods
Struggle with the inherent complexity of time series data.
2 AI Adaptation
AI algorithms adapt to dynamic patterns through learning.
3 Scalability
Scales effectively to real-time applications.
Traditional methods, such as statistical thresholds, often fall short when dealing
with complex data. AI, powered by machine learning, adapts to dynamic patterns,
processes high-dimensional data, and offers superior scalability. AI also handles
high-dimensional, noisy time series data better.
6. Core Algorithms for Time
Series Analysis
Statistical Methods
Z-scores, moving averages (simple but limited).
Machine Learning
Isolation Forests, Autoencoders.
Deep Learning
LSTMs, CNNs for temporal modeling.
Statistical approaches offer simplicity but have limitations.
Machine learning techniques adapt better to complex data.
Deep learning algorithms, like LSTMs and CNNs, excel at
capturing temporal dependencies in time series data. We
will consider a hybrid approach when appropriate.
7. Data Preparation: The Foundation
for Success
Data Collection
Collect time series data from sources like IoT sensors or system logs.
Preprocessing
Handle missing values and normalize data to ensure consistency.
Feature Engineering
Create relevant features like lags and rolling statistics.
Data Splitting
Divide data into training (normal) and testing sets for model evaluation.
The first step is to collect time series data from sources. Then, missing values are handled,
and data is normalized. Feature engineering extracts relevant information. Splitting data
into training and testing sets ensures reliable model evaluation.
8. Model Training: Learning Normal Behavior
Data Input
Feed historical time series data to
the model.
1
Learning
The model learns normal behavior
(unsupervised or supervised).
2
Techniques
Employ reconstruction error
(Autoencoders) or sequence
prediction (LSTMs).
3
Output
Generate a baseline model for
anomaly scoring.
4
Historical time series data is the input. The model learns normal behavior through unsupervised or supervised techniques.
Autoencoders and LSTMs are used in this model training. A baseline model is generated for anomaly scoring.
9. Anomaly Detection: Identifying Deviations
Data Comparison
Compare new data to the learned baseline model.
Thresholding
Flag data points exceeding a predefined limit as anomalies.
Real-Time
Apply a sliding window over streaming data for
continuous monitoring.
New data is compared to the learned baseline. Data points exceeding the score threshold are flagged as anomalies. A sliding
window is applied over streaming data for real-time analysis. Feedback loops refine detection, reducing false positives.
10. LSTMs Deep Dive
RNN Variant
LSTMs excel at capturing long-
term dependencies.
Predict Next Value
The model predicts the next
value in the sequence.
Flag Large Errors
Significant prediction errors indicate potential anomalies.
11. Real-World Applications
Cybersecurity
Detect unusual
network traffic
patterns.
Industrial IoT
Predictive
maintenance for
industrial machinery.
Finance
Identify fraudulent
transactions.
Healthcare
Provide early
warnings from
wearable devices.
12. Overcoming Key Challenges
1
Scalability
2 Interpretability
3 Noise vs. Anomaly
4 Imbalanced Data
Address imbalanced data, differentiate noise from anomalies, ensure scalability for high-velocity streams, and interpret AI
decisions.
14. Future Innovations
1
Self-Adaptive Models
Minimize human tuning with adaptive algorithms.
2
Multi-Modal Detection
Integrate time series data with images and text.
3
Edge AI
Enable on-device anomaly detection.
15. Conclusion
Anomaly detection is a powerful AI tool for
extracting proactive insights. By blending
statistics, machine learning, and deep
learning, you can transform data chaos into
clear, actionable intelligence. Experiment
with open datasets like Numenta to begin
your anomaly detection journey.
