The goal of this post is to walk you through the steps to create and train an AI deep learning neural network for anomaly detection using Python, Keras and TensorFlow. I will not delve too much in to the underlying theory and assume the reader has some basic knowledge of the underlying technologies.
However, I will provide links to more detailed information as we go and you can find the source code for this study in my GitHub repo. In the NASA study, sensor readings were taken on four bearings that were run to failure under constant load over multiple days. Our dataset consists of individual files that are 1-second vibration signal snapshots recorded at 10 minute intervals. Each file contains 20, sensor data points per bearing that were obtained by reading the bearing sensors at a sampling rate of 20 kHz.
You can download the sensor data here. You will need to unzip them and combine them into a single data directory. We will use an autoencoder deep learning neural network model to identify vibrational anomalies from the sensor readings. The goal is to predict future bearing failures before they happen.
The concept for this study was taken in part from an excellent article by Dr. In that article, the author used dense neural network cells in the autoencoder model. A key attribute of recurrent neural networks is their ability to persist information, or cell state, for use later in the network.
This makes them particularly well suited for analysis of temporal data that evolves over time. LSTM networks are used in tasks such as speech recognition, text translation and here, in the analysis of sequential sensor readings for anomaly detection.
Anomaly Detection with LSTM in Keras
There are numerous excellent articles by individuals far better qualified than I to discuss the fine details of LSTM networks. I will be using an Anaconda distribution Python 3 Jupyter notebook for creating and training our neural network model. We will use TensorFlow as our backend and Keras as our core model development library. The first task is to load our Python libraries.
We then set our random seed in order to create reproducible results. The assumption is that the mechanical degradation in the bearings occurs gradually over time; therefore, we will use one datapoint every 10 minutes in our analysis. Each 10 minute data file sensor reading is aggregated by using the mean absolute value of the vibration recordings over the 20, datapoints.
We then merge everything together into a single Pandas dataframe. Next, we define the datasets for training and testing our neural network. To do this, we perform a simple split where we train on the first part of the dataset, which represents normal operating conditions. We then test on the remaining part of the dataset that contains the sensor readings leading up to the bearing failure.
First, we plot the training set sensor readings which represent normal operating conditions for the bearings. Next, we take a look at the test dataset sensor readings over time. Midway through the test set timeframe, the sensor patterns begin to change. Near the failure point, the bearing vibration readings become much stronger and oscillate wildly. To gain a slightly different perspective of the data, we will transform the signal from the time domain to the frequency domain using a Fourier transform.
I am trying to label the points as signal or background the signal appears usually periodically, several times, for a given light curve.
However, the data is not labeled. I tried labeling it by hand, and using a bi-directional LSTM succeeds in labeling the data points properly. However, there are thousands of light curves and labeling all of them would take very long.
Is there any good unsupervised approach to do this unsupervised LSTM maybe, but any other method that might work on time series would do just fine? This will solve your problem of with too much latency time that LSTM required. LSTM also doesn't give preference on newer data too. For more information, you can follow more on this great article.
GRU Cells. Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Is there an LSTM-based unsupervised learning algorithm to label a dataset of curves? Ask Question. Asked 11 months ago. Active yesterday. Viewed 49 times. Alex Marshall Alex Marshall 1 1 silver badge 5 5 bronze badges. If the dataset is quite imbalanced, you may be able to separate the data into two clusters and check some of the results for signal by hand?
That way you could separate signal which would appear as anomaly from noise which seemingly dominates the signal. Active Oldest Votes. For more information, you can follow more on this great article GRU Cells. New contributor.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. 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.Anomaly detection is the problem of identifying data points that don't conform to expected normal behaviour.
Unexpected data points are also known as outliers and exceptions etc. Anomaly detection has crucial significance in the wide variety of domains as it provides critical and actionable information.Finding an outlier in a dataset using Python
For example, an anomaly in MRI image scan could be an indication of the malignant tumour or anomalous reading from production plant sensor may indicate faulty component. Simply, anomaly detection is the task of defining a boundary around normal data points so that they can be distinguishable from outliers.
But several different factors make this notion of defining normality very challenging. Moreover, defining the normal region which separates outliers from normal data points is not straightforward in itself.
In this tutorial, we will implement anomaly detection algorithm in Python to detect outliers in computer servers. The Gaussian model will be used to learn an underlying pattern of the dataset with the hope that our features follow the gaussian distribution.
After that, we will find data points with very low probabilities of being normal and hence can be considered outliers. For training set, we will first learn the gaussian distribution of each feature for which mean and variance of features are required.
Numpy provides the method to calculate both mean and variance covariance matrix efficiently. Similarly, Scipy library provide method to estimate gaussian distribution. Let's get started! By first importing requried libraries and defining functions for reading data, mean normalizing features and estimating gaussian distribution. Next, define a function to find the optimal value for threshold epsilon that can be used to differentiate between normal and anomalous data points.
For learning the optimal value of epsilon we will try different values in a range of learned probabilities on a cross-validation set. The f-score will be calculated for predicted anomalies based on the ground truth data available. The epsilon value with highest f-score will be selected as threshold i. We have all the required pieces, next let's call above defined functions to find anomalies in the dataset.
Also, as we are dealing with only two features here, plotting helps us visualize the anomalous data points. We implemented a very simple anomaly detection algorithm. To gain more in-depth knowledge, please consult following resource: Chandola, Varun, Arindam Banerjee, and Vipin Kumar.
The complete code Python notebook and the dataset is available at the following link. Toggle navigation Aaqib Saeed. Github Twitter LinkedIn.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again.
If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. This is an implementation of RNN based time-series anomaly detector, which consists of two-stage strategy of time-series prediction and anomaly score calculation. Keogh et al. We first train this model with a trainset which contains no anomalies, then we use the trained model to detect anomalies in a testset, where anomalies are included.
Recursive multi-step prediction using RNNs is a rather difficult problem. As the prediction progresses, the prediction errors are accumulated and the predictions rapidly become inaccurate. To solve this problem, we need a model that is robust to input noise. Time-series prediction: Train and save RNN based time-series prediction model on a single time-series trainset. Anomaly detection: Fit multivariate gaussian distribution and calculate anomaly scores on a single time-series testset.
Model performance was evaluated by comparing the model output with the pre-labeled ground-truth. Note that the labels are only used for model evaluation. The anomaly score threshold was increased from 0 to some maximum value to plot the change of precision, recall, and f1 score. Here we show only the results for the ECG dataset. Execute the code yourself and see more results. Please consider citing this project in your publications if it helps your research.
The following is a BibTeX reference. Malhotra, Pankaj, et al. Presses universitaires de Louvain, Skip to content.
LSTM RNN anomaly detection and Machine Translation and CNN 1D convolution
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Sign up. Python Shell. Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Latest commit da4b Apr 6, Requirements Ubuntu Suggesstions are welcomed.This repository contains the code used in my master thesis on LSTM based anomaly detection for time series data.
The thesis report can be downloaded from here. Due to the challenges in obtaining labeled anomaly datasets, an unsupervised approach is employed. The resulting prediction errors are modeled to give anomaly scores. We investigate different ways of maintaining LSTM state, and the effect of using a fixed number of time steps on LSTM prediction and detection performance. LSTMs are also compared to feed-forward neural networks with fixed size time windows over inputs.
Our experiments, with three real-world datasets, show that while LSTM RNNs are suitable for general purpose time series modeling and anomaly detection, maintaining LSTM state is crucial for getting desired results. Moreover, LSTMs may not be required at all for simple time series.
This file has different configuration settings. For training the model and generating predictions two main files are provided:. For anomaly detection we need to calculate prediction errors or residuals, model them using Gaussian distribution and then set thresholds.
This is done in "Part 3" of the corresponding notebook files.Jump to navigation. This article shares the experience and lessons learned from Baosight and Intel team in building an unsupervised time series anomaly detection project, using long short-term memory LSTM models on Analytics Zoo. In manufacturing industry, particularly in the steel industry, there are two ways to avoid producing unqualified products caused by device failure.
Both approaches could be unnecessarily expensive. However, it is possible to collect a massive amount of vibration data of different devices, and automatically detect anomalies of the device statuses using these data.
Efficient time-series data retrieval and automatic failure detection of the devices at scale is the key to saving a lot of unnecessary cost.
As connectionist models, RNNs capture the dynamics of sequences via cycles in the network of nodes. We have built the end-to-end LSTM-based anomaly detection pipeline on Apache Spark and Analytics-Zoo, which applies unsupervised learning on a large set of time series data. Figure 1. Figure 2. Figure 3 shows comparisons between LSTM model predictions and ground truth of vibration time series. Only two statistics are shown here, namely, peak and RMS of the same channel.
Other statistics show similar fluctuations.
Recurrent neural network
The red points are anomalies detected. The orange line is prediction of the LSTM model. The blue line represents the ground truth. The model successfully detects the failure of the device at the end, as well as spikes after timesteps.
Anomaly Detection, a short tutorial using Python
Some of the early fluctuations give warnings. Figure 3. Anomaly detection of time series would likely to play a key role in the use cases such as monitoring and predictive maintenance.
Share Tweet Share Send. The entire end-to-end pipeline is illustrated in Figure 1. For more complete information about compiler optimizations, see our Optimization Notice. Rate Us. Get the Newsletter.