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Project 4 — Anomaly Detection

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CSE4/510: Applied Deep Learning

Project 4 — Anomaly Detection

Description
In this assignment we will practice to detect anomalies in the benchmark dataset. We
will explore deep learning approaches that include building a sequence to sequence
MLP and autoencoder.
Dataset
NAB (Numenta Anomaly Benchmark) is a novel benchmark for evaluating algorithms
for anomaly detection in streaming, real-time applications. It is composed of over 50
data files designed to provide data for research in streaming anomaly detection. It is
comprised of both real-world and artificial timeseries data containing labeled anomalous
periods of behavior.

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Description

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CSE4/510: Applied Deep Learning

Project 4 — Anomaly Detection

Description
In this assignment we will practice to detect anomalies in the benchmark dataset. We
will explore deep learning approaches that include building a sequence to sequence
MLP and autoencoder.
Dataset
NAB (Numenta Anomaly Benchmark) is a novel benchmark for evaluating algorithms
for anomaly detection in streaming, real-time applications. It is composed of over 50
data files designed to provide data for research in streaming anomaly detection. It is
comprised of both real-world and artificial timeseries data containing labeled anomalous
periods of behavior.
https://github.com/numenta/NAB/tree/master/data
Tasks
Part I: MLP for Anomaly Detection [50 points]
1. Choose any dataset from NAB (except those used in class) and prepare it for
training (normalize, split between train/test/validation). Explore the dataset by
visualizing it and showing statistical parameters about it.
2. Build an MLP/LSTM model for predicting a sequence of values (min 5 values).
Work with 3 different setups of the window size and the size of the output
sequence.
3. Using 3 different loss/distance measures identify the anomalies in the dataset.
Compare the measurements.
4. Discuss the results and provide the graphs, e.g. train vs validation accuracy and
loss over time. Show a confusion matrix (normal vs anomaly).
Part II: Autoencoder for Anomaly Detection [50 points]
1. Build a Autoencoder model for predicting a sequence of values. Show 3 different
Autoencoder setups (e.g. using Dense/LSTM/Conv1D layers).
2. For one of the model builded in 1 show the process of hyperparameters tuning
(e.g. thresholds, # of layers, activation functions).
3. Discuss the results and provide the graphs, e.g. train vs validation accuracy and
loss over time. Show the confusion matrix.
Submit the Project
● Submit at UBLearns > Assignments
● The code of your implementations should be written in Python. You can submit
multiple files, but they all need to have a clear name
● All project files should be packed in a ZIP file named
TEAMMATE#1_UBIT_TEAMMATE#2_UBIT_project4.zip (e.g.
avereshc_neelamra_project4.zip).
● Your Jupyter notebook should be saved with the results. If you are submitting
python scripts, after extracting the ZIP file and executing command python
main.py in the first level directory, all the generated results and plots you used in
your report should appear printed out in a clear manner.
● In your report include the answers to questions for each part. You can complete
the report in a separate pdf file or in Jupyter notebook along with your code.
● Include all the references that have been used to complete the project.
Important Information
This project can be done in a team of up to two people. The standing policy of the
Department is that all students involved in an academic integrity violation (e.g.
plagiarism in any way, shape, or form) will receive an F grade for the course. Refer to
the Academic Integrity website for more information.
Late Days Policy
You can use up to 5 late days throughout the course that can be applied to any project.
You don’t have to inform the instructor, the late submission will be tracked in UBlearns.
If you work in teams the late days used will be subtracted from both partners. E.g. you
have 4 late days and your partner has 3 days left. If you submit one day after the due
date, you will have 3 days and your partner will have 2 days left.
Important Dates
August 4, 11:59pm – Project 4 is Due