﻿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.

VALUES TO RETURN
- ItemId: the 'id' property of the item.
- Property: the name of the property for which an anomaly was detected.
- ShortDescription: Only include if anomalyType is not None. A 1-2 sentence user-friendly description, which should include some context about the surrounding data and the high and low values of the anomaly event. Refer to timestamps with their actual values (e.g. 174157340). Don't refer to the "6th timestamp", "last timestamp", etc.
- Timestamp: Timestamp when the anomaly started or was first observed.
- AnomalyType: "Anomaly" if an anomaly that exactly meets the definition was found, otherwise "None"
