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Google uses continuous streams of data from industry partners in order todeliver accurate results to users. Unexpected drops in traffic can be anindication of an underlying issue and may be an early warning that remedialaction may be necessary. Detecting such drops is non-trivial because streamsare variable and noisy, with roughly regular spikes (in many different shapes)in traffic data. We investigated the question of whether or not we can predictanomalies in these data streams. Our goal is to utilize Machine Learning andstatistical approaches to classify anomalous drops in periodic, but noisy,traffic patterns. Since we do not have a large body of labeled examples todirectly apply supervised learning for anomaly classification, we approachedthe problem in two parts. First we used TensorFlow to train our various modelsincluding DNNs, RNNs, and LSTMs to perform regression and predict the expectedvalue in the time series. Secondly we created anomaly detection rules thatcompared the actual values to predicted values. Since the problem requiresfinding sustained anomalies, rather than just short delays or momentaryinactivity in the data, our two detection methods focused on continuoussections of activity rather than just single points. We tried multiplecombinations of our models and rules and found that using the intersection ofour two anomaly detection methods proved to be an effective method of detectinganomalies on almost all of our models. In the process we also found that notall data fell within our experimental assumptions, as one data stream had noperiodicity, and therefore no time based model could predict it.

Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data