  Anomaly detection on time series data is increasingly common across various
industrial domains that monitor metrics in order to prevent potential accidents
and economic losses. However, a scarcity of labeled data and ambiguous
definitions of anomalies can complicate these efforts. Recent unsupervised
machine learning methods have made remarkable progress in tackling this problem
using either single-timestamp predictions or time series reconstructions. While
traditionally considered separately, these methods are not mutually exclusive
and can offer complementary perspectives on anomaly detection. This paper first
highlights the successes and limitations of prediction-based and
reconstruction-based methods with visualized time series signals and anomaly
scores. We then propose AER (Auto-encoder with Regression), a joint model that
combines a vanilla auto-encoder and an LSTM regressor to incorporate the
successes and address the limitations of each method. Our model can produce
bi-directional predictions while simultaneously reconstructing the original
time series by optimizing a joint objective function. Furthermore, we propose
several ways of combining the prediction and reconstruction errors through a
series of ablation studies. Finally, we compare the performance of the AER
architecture against two prediction-based methods and three
reconstruction-based methods on 12 well-known univariate time series datasets
from NASA, Yahoo, Numenta, and UCR. The results show that AER has the highest
averaged F1 score across all datasets (a 23.5% improvement compared to ARIMA)
while retaining a runtime similar to its vanilla auto-encoder and regressor
components. Our model is available in Orion, an open-source benchmarking tool
for time series anomaly detection.
