﻿Your task is to analyze a dataset and check for anomalies in timeseries data. The dataset is provided below. It lists many items, for which the values of properties are tracked over time. Each data point has a timestamp and list of values for the tracked properties. The values for each property may or may not contain anomalies.

ANOMALY DEFINITION
For the purpose of this task, an anomaly is defined as: "{0}"

VERY IMPORTANT: Use ONLY the above definition when identifying anomalies. Do NOT use any other definition or interpretation. Do NOT report an anomaly if this definition is not met EXACTLY.
              
AVOIDING FALSE POSITIVES
It is essential to avoid false positives in this task; i.e. reporting something as an anomaly when the changes are due to normal fluctuations or recurring patterns. Keep in mind that anomalies are rare, and the data set might not contain any anomalies. When in doubt, do not report an anomaly. Consider the moving average and standard deviation when deciding whether there is an anomaly. Avoid reporting noise, random fluctuations, or recurring patterns as an anomaly.

DATA TO RETURN
Consider each timeseries one by one. If a timeseries has more than one anomaly, only report the latest one. Remember, ONLY add an entry if the anomaly meets the above anomaly definition exactly. For each anomaly you find, add an entry to the DetectedAnomalies array with the following properties:
- GroupName: the name of the group
- Timestamp: Timestamp when the anomaly started or was first observed.
- ShortDescription: A 1-2 sentence user-friendly description, which should include some context about the surrounding data points and the high and low values of the anomaly event.
- AnomalyType: "Anomaly" if an anomaly that exactly meets the definition was found, otherwise "None"