In data mining, anomaly detection also outlier detection is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text.

Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions. In particular in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods in particular unsupervised methods will fail on such data, unless it has been aggregated appropriately.

In this article, we will focus on the first category, i. The type of algorithm we will use is called auto encoders. Auto encoders provide a very powerful alternative to traditional methods for signal reconstruction and anomaly detection in time series.

lstm anomaly detection github

What is an auto encoder? It is an artificial neural network used for unsupervised learning of efficient codings. Its goal is to induce a representation encoding for a set of data by learning an approximation of the identity function of this data. Architecturally, the simplest form of an auto-encoder is a feedforward, non-recurrent neural net which is very similar to the multilayer perceptron MLPwith an input layer, an output layer and one or more hidden layers connecting them.

The differences between autoencoders and MLPs, though, are that in an auto-encoder, the output layer has the same number of nodes as the input layer, and that, instead of being trained to predict the target value given inputsautoencoders are trained to reconstruct their own inputs. Therefore, autoencoders are unsupervised learning models.

An autoencoder always consists of two parts, the encoder and the decoder, which can be defined as transitions andsuch that:. In the simplest case, where there is one hidden layer, an autoencoder takes the input and maps it onto with :.

This is usually referred to as code or latent variables latent representation. Here, is an element-wise activation function such as a sigmoid function or a rectified linear unit.

lstm anomaly detection github

After that, is mapped onto the reconstruction of the same shape as :. As said previously, auto-encoders mostly aim at reducing feature space in order to distill the essential aspects of the data versus more conventional deep learning which blows up the feature space up to capture non-linearities and subtle interactions within the data. Autoencoding can also be seen as a non-linear alternative to PCA.Each sequence corresponds to a single heartbeat from a single patient with congestive heart failure.

An electrocardiogram ECG or EKG is a test that checks how your heart is functioning by measuring the electrical activity of the heart. With each heart beat, an electrical impulse or wave travels through your heart.

lstm anomaly detection github

This wave causes the muscle to squeeze and pump blood from the heart. Assuming a healthy heart and a typical rate of 70 to 75 beats per minute, each cardiac cycle, or heartbeat, takes about 0. Frequency: 60— per minute Humans Duration: 0. The data comes in multiple formats.

Variational Autoencoders

This will give us more data to train our Autoencoder. We have 5, examples. Each row represents a single heartbeat record. The normal class, has by far, the most examples. It is very good that the normal class has a distinctly different pattern than all other classes. Maybe our model will be able to detect anomalies?

The reconstruction should match the input as much as possible.

Time Series Anomaly Detection using LSTM Autoencoders with PyTorch in Python

The trick is to use a small number of parameters, so your model learns a compressed representation of the data. In a sense, Autoencoders try to learn only the most important features compressed version of the data. When training an Autoencoder, the objective is to reconstruct the input as best as possible.

This is done by minimizing a loss function just like in supervised learning. This function is known as reconstruction loss. Cross-entropy loss and Mean squared error are common examples.

But first, we need to prepare the data:. We need to convert our examples into tensors, so we can use them to train our Autoencoder. Each Time Series will be converted to a 2D Tensor in the shape sequence length x number of features x1 in our case. Sample Autoencoder Architecture Image Source.Skip to content. Instantly share code, notes, and snippets.

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Download ZIP. We predict the number of queries in the next hour using a LSTM recurrent neural network. An ad hoc anomaly detection is outlined in the final for loop. This comment has been minimized. Sign in to view. Copy link Quote reply. Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Using OpenDNS domain query activity, we retrieve 5 days.

We predict the number of queries in. An ad hoc anomaly detection is outlined in the final. Refer to:.There is a wide variety of abnormal events that might take place even in a single location, and the definition of abnormal event differs from location to another and from time to time. Using automated systems to detect unusual events in this scenario is highly desirable and leads to better security and broader surveillance.

In general, the process of detecting anomalous events in videos is a challenging problem that currently attracts much attention by researchers, it also has broad applications across industry verticals, and recently it has become one of the essential tasks of video analysis.

lstm anomaly detection github

There is a huge demand for developing an anomaly detection approach that is fast and accurate in real-world applications. Understanding the basics of the following topics:. If we want to treat the problem as a binary classification problem, we need labeled data and in this case, collecting labeled data is hard because of the following reasons:.

The above reasons promoted the need to use unsupervised or semi-supervised methods like dictionary learning, Spatio-temporal features, and autoencoders. Unlike supervised methods, these methods only require unlabeled video footages that contain little or no abnormal events that are easy to obtain in real-world applications. Autoencoders are neural networks that are trained to reconstruct the input.

The autoencoder consists of two parts:. It is all about the reconstruction error. We use an autoencoder to learn regularity in video sequences. The intuition is that the trained autoencoder will reconstruct regular video sequences with low error but will not accurately reconstruct motions in irregular video sequences.

We will use the UCSD anomaly detection dataset, which contains videos acquired with a camera mounted at an elevation, overlooking a pedestrian walkway. In normal settings, these videos contain only pedestrians. Abnormal events are due to either:. The UCSD dataset consists of two parts, ped1 and ped2.

We will use the ped1 part for training and testing. Download the UCSD dataset and extract it into your current working directory or create a new notebook in Kaggle using this dataset. The training set consists of sequences of regular video frames; the model will be trained to reconstruct these sequences. One last point is that since the number of parameters in this model is huge, we need a large amount of training data, so we perform data augmentation in the temporal dimension.

To generate more training sequences, we concatenate frames with various skipping strides. For example, the first stride-1 sequence is made up of frames 1, 2, 3, 4, 5, 6, 7, 8, 9, 10whereas the first stride-2 sequence consists of frames 1, 3, 5, 7, 9, 11, 13, 15, 17, Here is the code. Note: if you face memory error, decrease the number of training sequences or use Data Generator. Finally, the fun part begins! The below image shows the training process; we will train the model to reconstruct the regular events.

So let us start discovering the model settings and architecture.This post will walk through a synthetic example illustrating one way to use a multi-variate, multi-step LSTM for anomaly detection.

One approach to doing anomaly detection in such a setting is to build a model to predict each metric over each time step in your forecast horizon and when you notice your prediction errors start to change significantly this can be a sign of some anomalies in your incoming data.

This is essentially an unsupervised problem that can be converted into a supervised one. You train the model to predict its own training data. Then once it gets good at this assuming your training data is relatively typical of normal behavior of your dataif you see some new data for which your prediction error is much higher then expected, that can be a sign that you new data is anomalous in some way. The rest of this post will essentially walk though the code.

Below shows the imports and all the parameters for this example, you should be able to play with them and see what different results you get.

We will generate some random data, and then smooth it out to look realistic. Then we will make a copy of this normal data and inject in some random noise at a certain point and for a period of time. The hope is that in reality the model once trained would be good at picking up much more nuanced changes in the data that are less obvious to the human eye.

For example here are the errors averaged across all five features are each timestep prediction horizon. In the above plot we can see the averaged error of the model on its training data. Each line represents a different forecast horizon. If we look at the standard deviation of our errors in a similar way, we can see how the standard deviation of our errors generally tends to increase at times when our 5 original features are diverging from each other as you can imagine these are the hardest parts of our time series for this model to predict.

From the above we can see that as soon as the random broken data comes into the time series the model prediction errors explode. As mentioned, this is a very obvious and synthetic use case just for learning on but the main idea is that if your data changed in a more complicated and harder to spot way then your error rates would everywhere reflect this change.

These error rates could then be used as input into a more global anomaly score for your system. I may add some more complicated or real world examples building on this approach at a later stage. View all posts by andrewm Skip to content. Recent Posts All Posts. A matrix of 5 metrics from period t to t-n. Share this: Twitter Facebook. Like this: Like Loading Tagged anomaly-detection keras lstm machine-learning python. Published by andrewm Published September 9, September 9, Leave a Reply Cancel reply.This is not a new topic by any means, though.

Niche fields have been using it for a long time. Nowadays, though, due to advances in banking, auditing, the Internet of Things IoTetc. As with other tasks that have widespread applications, anomaly detection can be tackled using multiple techniques and tools.

This article takes a look at how different types of neural networks can be applied to detect anomalies in time series data using Apache MXNeta fast and scalable training and inference framework with an easy-to-use, concise API for machine learning, in Python using Jupyter Notebooks. By the end of this tutorial, you should:. Join the O'Reilly online learning platform. Get a free trial today and find answers on the fly, or master something new and useful.

All the code and the data used in this tutorial can be found on GitHub. When talking about any machine learning task, I like to start by pointing out that, in many cases, the task is really all about finding patterns. This problem is no different. Anomaly detection is a process of training a model to find a pattern in our training data, which we subsequently can use to identify any observations that do not conform to that pattern.

Such observations will be called anomalies or outliers. In other words, we will be looking for a deviation from the standard pattern, something rare and unexpected.

Similarly, as mentioned before, a wide range of methods can be used to solve this problem. A few of the most popular include:. Several problems arise when you try to use most of these algorithms. For instance, they tend to make specific assumptions about the data, and some do not work with multivariate data sets.

This is why today we will look into the last method—autoencoders—using two types of neural networks: multilayer perceptron and long-short-term-memory LSTM networks. The kind of networks we will discuss here go by many names: autoencoder; autoassociator; or, my personal favorite, Diabolo. The technique is a type of artificial neural network used for unsupervised learning of efficient codings.

LSTM RNN anomaly detection and Machine Translation and CNN 1D convolution

In plain English, this means it is used to find a different way of representing encoding our input data. Autoencoders are sometimes also used to reduce the dimensions of the data.In this chaos, the only truth is the variability of this definition, i. Detection of this kind of behavior is useful in every business and the difficultness to detect these observations depends on the field of applications.

If you are engaged in a problem of anomaly detection, which involves human activities like a prediction of sales or demandyou can take advantage of fundamental assumptions of human behaviors and plan a more efficient solution. This is exactly what we are doing in this post. We try to predict the Taxi demand in NYC in a critical time period.

We formulate easy and important assumptions about human behaviors, which will permit us to detect an easy solution to forecast anomalies.

Anomaly detection with Apache MXNet

All the dirty job is made by a loyalty LSTM, developed in Keras, which makes predictions and detection of anomalies at the same time! I took the dataset for our analysis from the Numenta community. This dataset shows the NYC taxi demand from —07—01 to —01—31 with an observation every half hour. In this period 5 anomalies are present, in terms of deviation from normal behavior.

Our purpose is to detect these abnormal observations in advance! The first consideration we noticed, looking at the data, is the presence of an obvious daily pattern during the day the demand is higher than night hours. The taxi demand seems to be driven also by a weekly trend: on certain days of the week, the taxi demand is higher than the others. We simply prove this computing autocorrelation.

What we can do now is to take note of these important behaviors for our further analysis. I compute and store the means for every day of the weeks at every hour. We need a strategy to detect outliers in advance.

To do this, we decided to care about taxi demand predictions. We want to develop a model which is able to forecast demand taking into account uncertainty.

One way to do this is to develop quantile regression. We focus on predictions of extreme values: lower 10th quantileupper 90th quantile and the classical 50th quantile. Computing also the 90th and 10th quantile we cover the most likely values the reality can assume. We took advantage of this behavior and let our model says something about outliers detection in the field of taxi demand prediction.

We are expecting to get a tiny interval 90—10 quantile range when our model is sure about the future because it has all under control; on the other hand, we are expecting to get an anomaly when the interval becomes bigger. Our model will receive as input the past observations.

We resize our data for feeding our LSTM with daily window size 48 observations: one observation for every half hour. When we were generating data, as I cited above, we operated logarithmic transformation and standardization subtracting the mean daily hour values, in order to see observation as the logarithmic variation from its daily mean hour value.

We build our target variables in the same way with half-hour shifting we want to predict the demand values for the next thirty minutes. Operate quantile regression in Keras is very simple I took inspiration from this post.

Our network has 3 outputs and 3 losses, one for every quantile we try to predict. When dealing with Neural Network in Keras, one of the tedious problems is the uncertainty of results due to the internal weights initialization.

With its formulation, our problem seems to particularly suffer from this kind of problem; i. To avoid this pitfall I make use of bootstrapping in prediction phase: I reactivate dropout of my network trainable: true in the modeliterate prediction for times, store them and finally calculate the desired quantiles I make use of this clever technique also in this post. This process is graphically explained below with a little focus on a subset of predictions.

Given quantile bootstraps, we calculated summary measures red lines of them, avoiding crossover. As I previously cited, I used the first observations for training and the remaining around for testing.