If you want to turbocharge your business in 2025, most experts, including us at Inetum, will encourage you to embrace the world of artificial intelligence.
After all, combining Deep Learning models, advanced data science, and big data gives you powerful insights that help you make decisions at scale, but how do you know when something’s not quite right with your data?
Behind these seemingly magical AI technologies, the best organizations invest in a combination of advanced anomaly detection techniques, including Long Short-Term Memory (LSTM) autoencoders, to ensure their time-related data remains super accurate. Without them, businesses risk their data becoming toxic, leading to inaccurate processes, defunct automations, and baseless decision-making.
In this article, we dive deep into anomaly detection, looking at how you can use networks for anomaly detection and Machine Learning tools such as LSTM to help you improve the accuracy of your dataset!
What is Time Series Data?
Time series is a sequence of related data points ordered by time. It captures how a particular data point or metric changes over time.
In business, you can think of time series data as your organization’s fitness tracker on steroids. These are the time-based data points that tell the story of your business growth and evolution over time, rather than just provide regular point-in-time data snapshots.
Real-world examples of time series data are everywhere, and include:
- A hospital’s continuous monitoring of a patient’s heart rate, blood pressure, and oxygen levels captures vital signs every few seconds. This constant stream of measurements helps medical staff spot dangerous changes in a patient’s condition.
- The number of visitors hitting your company’s website each hour creates a pattern that reflects user behavior, seasonal trends, and the impact of marketing campaigns. When you look at this data over months or years, you can see how your online presence grows and changes.
- Stock prices ticking up and down throughout the trading day represent perhaps the most well-known example of time series data. Each price movement carries information about market sentiment, company performance, and broader economic conditions.
What makes time series data special is the relationship between each measurement. For example, yesterday’s sales could influence today’s, and today’s could influence tomorrow’s. So, this interconnected nature makes time series data critical to businesses looking to predict future performance, meaning it cannot be left unsupervised and risk becoming inaccurate over time.
What is an Anomaly?
In all forms of data management, an anomaly is an outlier that doesn’t seem to fit with the rest of your data set. It’s the data point that makes you sit up and say “hmm, that doesn’t look right”.
In the world of time series data, anomalies are patterns that don’t fit your expectations based on historical observations. But here’s the catch – not all unusual events are anomalies, and not all anomalies are obvious.
Let’s look at some examples:
Imagine you’re monitoring a manufacturing plant’s power consumption. The typical pattern shows higher usage during working hours and lower usage at night. An anomaly might be:
- A sudden spike in power usage at 3 AM when all machines should be idle
- A gradual but unusual increase in baseline power consumption over several days
- Regular brief power dips that don’t align with scheduled maintenance
Or consider a cloud service provider monitoring network traffic:
- A sudden surge in data transfer rates could indicate an attack vector, such as a DDoS attack
- Unusual patterns in API calls might reveal a security breach
- Periodic latency spikes could point to failing hardware
What makes anomaly detection in time series particularly tricky is that “normal” behavior often changes over time. A retail website’s traffic pattern during Black Friday would look completely abnormal during a regular week. But if you classed them as anomalies they’d be false positives, as this sort of behavior is perfectly acceptable for the time of year.
For this reason, if you tried to do anomaly detection using standard methods, such as a simple Python code, there’s a strong chance you wouldn’t pick them up, or you’d end up deleting good data. Instead, you need something more powerful to help make the distinctions, such as a neural network.
Using Recurrent Neural Networks for Machine Learning & Time Series Anomaly Detection
A recurrent neural network (RNN) is a type of artificial neural network where connections and memory between nodes means the output from one node influences the input of another.
Like traditional deep neural networks, recurrent neural networks use test data to achieve unsupervised learning as they evolve and grow over time. But, they are distinguished by their “memory” as they take information from prior inputs to influence the current input and output.
This makes them especially useful for time series data, as they look back on the past to help predict the future. This context makes them especially useful at spotting future data items that don’t look right, with logic applied to help them sniff out anomalies quickly and efficiently.
But, RNNs have one major flaw, as data sets get bigger and bigger, they’re unable to memorize data over a long time period. This means that after a while they begin to forget their previous inputs, reducing their effectiveness for time-series data.
So, they need some help boosting their memory, and that’s where the concept of Long Short-Term Memory (LSTM) autoencoders come in. \
Know more with our webinar: Anomaly Detection Engine (ADE) using LSTM Neural Networks
What are Long Short-Term Memory (LSTM) Autoencoders?
Long Short-Term Memory (LSTM) autoencoders are like highly sophisticated file compressors. Rather than trying to remember vast amounts of data, they take the complex inputs, shrink them down into memorable patterns, and then decode them when they require them later.
So, unlike simpler neural networks that forget what happened a few steps ago, LSTMs maintain their own internal memory to boost their effectiveness. This makes them perfect for time series data, where what happened hours or days ago might influence current behavior.
When you combine all of these concepts, you get a powerful Long Short Term Memory model that can:
- Learn complex temporal patterns in your data
- Understand both short-term and long-term dependencies
- Create efficient representations of normal behavior
- Identify when new data doesn’t fit learned patterns from the training data
How do LSTM Autoencoders Detect Anomalies?
But wait, what is the data actually changes for real over time? How do you stop it being unfairly classed as an anomaly? Here’s where things get really clever and how the LSTM model sets itself apart from other anomaly detection approaches.
LSTM autoencoders detect anomalies through a clever trick: they’re trained to be really really good at reconstructing normal patterns, but they struggle with patterns they’ve never seen before. It’s like teaching someone to recognize authentic signatures – after seeing enough real ones, they’ll notice when something looks off.
The process works in several steps:
- First, the LSTM autoencoder is trained on normal, anomaly-free data. During this phase, it learns to compress and reconstruct typical patterns in your time series data. The network adjusts its internal parameters to minimize the difference between its input and output – in other words, it gets really good at reproducing normal behavior.
- Once trained, the autoencoder is used to process multiple data sets. For each new sequence:
- The encoder compresses the input sequence into a compact representation
- The decoder attempts to reconstruct the original sequence
- The reconstruction error is calculated by comparing the input and output
- When the autoencoder encounters normal data similar to what it was trained on, the reconstruction error will be low. But when it sees anomalous data, the reconstruction error spikes because the network hasn’t learned how to reproduce these unusual patterns effectively. This indicates the presence of an anomaly.
Imagine monitoring a server’s CPU usage. The LSTM autoencoder learns the typical daily pattern: higher usage during business hours, periodic spikes during scheduled jobs, and lower usage at night. When an unusual event occurs – say, a runaway process consuming excessive CPU – the autoencoder struggles to reconstruct this pattern accurately, resulting in a higher reconstruction error that flags the anomaly.
The beauty of this approach is its adaptability. Detection with LSTM autoencoders using a neural network can:
- Learn complex seasonal patterns and trends
- Adapt to gradual changes in what’s considered “normal”
- Detect subtle anomalies that might be missed by simpler methods
- Handle multiple input variables simultaneously
Using an External Partner to Master Anomaly Detection with LSTM, Deep Learning, and Neural Networks
Organizations all over the world are maximizing their data using advanced AI tools to predict customer behavior, manage KPI data sets, and complete competitor analysis.
But traditional detection methods won’t keep you safe when it comes to identifying anomalies in your data curation process. This is especially true when trying to complete detection in time series data, where you need backwards looking context to identify errors in future time steps.
This is where advanced online anomaly detection using LSTM networks comes in, using advanced neural network-based long-term memory to accurately identify and flag anomalies.
As you’ve seen from this article, anomaly detection algorithms are complex work that requires detailed knowledge and expertise to get right. So, if your current anomaly detection isn’t working, why not reach out to our expert team at Inetum to help?
We’ve helped hundreds of clients implement advanced AI-backed technologies, including series anomaly detection with LSTM. If you’d like to hear more, contact us and see how we revolutionize industries and transform the way businesses operate.