  Real-world time series data often present recurrent or repetitive patterns
and it is often generated in real time, such as transportation passenger
volume, network traffic, system resource consumption, energy usage, and human
gait. Detecting anomalous events based on machine learning approaches in such
time series data has been an active research topic in many different areas.
However, most machine learning approaches require labeled datasets, offline
training, and may suffer from high computation complexity, consequently
hindering their applicability. Providing a lightweight self-adaptive approach
that does not need offline training in advance and meanwhile is able to detect
anomalies in real time could be highly beneficial. Such an approach could be
immediately applied and deployed on any commodity machine to provide timely
anomaly alerts. To facilitate such an approach, this paper introduces SALAD,
which is a Self-Adaptive Lightweight Anomaly Detection approach based on a
special type of recurrent neural networks called Long Short-Term Memory (LSTM).
Instead of using offline training, SALAD converts a target time series into a
series of average absolute relative error (AARE) values on the fly and predicts
an AARE value for every upcoming data point based on short-term historical AARE
values. If the difference between a calculated AARE value and its corresponding
forecast AARE value is higher than a self-adaptive detection threshold, the
corresponding data point is considered anomalous. Otherwise, the data point is
considered normal. Experiments based on two real-world open-source time series
datasets demonstrate that SALAD outperforms five other state-of-the-art anomaly
detection approaches in terms of detection accuracy. In addition, the results
also show that SALAD is lightweight and can be deployed on a commodity machine.
