This repository contains an approach to Tsinghua University's AIOps challenge in fall semester 2018. The goal was to identify anomalies among 26 different key performance indicators (KPIs).

Figure: Trends of two distinct KPIs with highlighted anomalies

• Python >= 3.6
• Pytorch >= 0.4

Temporal KPI data is cleaned, resampled and augmented using the preprocess.py script.

python preprocess.py <source> <destination>

Train the model by calling the train.py script. You should take a look at its content first and select the appropriate model from the models submodule. Available models are ConvModel, FullyConnected and Inception. Hyperparameters can also be changed in this file.

Take a look at the submit.py script if you need to run a prediction pass against a test dataset.