time-series-anomaly-analysis

Time-series anomaly detection in phone network data works by comparing recent call activity against a historical baseline to identify unusual call patterns and behaviors. It analyzes call trends, including night-time activity and anomaly dates, to flag spikes or irregular behavior.

To detect abnormal call patterns, you provide a specific phone number and a time range, such as the last 7 days. The analysis compares the recent call activity against baseline data to identify unusual spikes or changes in behavior for that number.

Yes, you can analyze a specific phone number or a group of numbers to identify unusual call patterns. This allows you to detect fraud, unusual activity, or behavioral changes across multiple lines within the phone network.

You need call data that includes time fields to detect anomalies accurately. The analysis requires this time-series data to establish a baseline of normal activity and compare recent call trends against it to identify behavioral anomalies.

You should use time-series analysis for phone network behavior when you need to identify fraud, unusual activity, or changes in calling behavior. It is specifically designed to detect anomalies by comparing recent call patterns against historical baseline data.

Yes, anomaly detection in phone networks works for night-time call behavior. The analysis specifically evaluates call trends including night-time behavior and anomaly dates to detect unusual patterns in phone network activity.