For Twitter, finding anomalies — sudden spikes or dips — in a time series is important to keep the microblogging service running smoothly. A sudden spike in shared photos may signify an "trending" event, whereas a sudden dip in posts might represent a failure in one of the back-end services that needs to be addressed. To detect such anomalies, the engineering team at Twitter created the AnomalyDetection R package, which they recently released as open source. (Late last year Twitter released a separate but related R package to detect "breakouts" in time series.) Finding spikes and dips is relatively easy...