AXON Analyst - Anomaly detection & prediction

Introduction

The Anomaly Detection and Prediction features are Piano Analytics' two timeseries functionalities powered by data science seamlessly integrated within Explorer. The Anomaly detection & Prediction functionalities are located under the "AXON analyst" button in the graph type selection. These features are available in all timeseries graphs within Explorer.

Anomaly detection

Anomalies are detected when our statistical model identifies suspicious variations for a given metric within the context of the analysis.

Example: A large decrease in visits on a Saturday may seem suspicious within the context of a week however prove to be a perfectly ordinary variation as the scale of a year (decreases in traffic can be observed each weekend).

Anomalies are illustrated on the graph with round symbols overlaid on the timeseries.

Positive anomalies will be shown in green:

Negative anomalies will show up in red:

Prediction

The prediction functionality will provide you with the expected future values of a given metric based on your historical data:

• In hourly granularity we forecast the following 24 hours after the last hour of the analysis
• In daily granularity we forecast the following 30 days after the last day of the analysis
• In weekly granularity we forecast the following 12 weeks after the last week of the analysis
• In monthly granularity we forecast the following 12 months after the last month of the analysis

Availability

Analyses

Graph granularities

Anomaly Detection & Prediction functionalities are available on all graph granularities: hour, day, week, month. When analysing the current day, the actual curve will be overlayed with the day's forecast giving you hourly targets for the day.

Data requirements

A minimum of 2 weeks of historical data (with less than 30% of the historical data being zeros) is necessary to identify anomalies and to provide forecasts on hourly and daily granularities. A minimum of 2 years worth of historical data is necessary (with less than 30% of the historical data being zeros) for weekly and monthly anomaly detections & predictions. If your site is new, you will have to wait until enough historical data is collected for our model to satisfy goodness-of-fit assumptions. Consequently, an explicit error message is shown until you have enough data. For optimal accuracy in our model, we recommend at least 8 weeks and 3 years of data respectively.

Metrics

The Anomaly detection & Prediction features are available for all metrics (absolute and ratios).

Basic concepts

Anomaly

When the anomaly option is selected an “tolerance” area is shown on the graph. We expect the curve to remain within the boundaries of this area. Any data point of the curve outside this zone will be flagged as an anomaly.

Trend

The trend illustrates macro fluctuations in your data over time. The trend is illustrated using a dashed curve.

Baseline

The Baseline illustrates how the metric would have evolved without the intervention of external factors. The baseline is illustrated using a dotted curve.

Forecast

The forecast is a prediction based on your historical data. The forecasted values are shown on the graph using a green dotted line. The forecast has its own level of accuracy which we illustrate using the light green area around the predicted curve.

Algorithm sensitivity

The detection sensitivity will effect the algorithm's tolerence area. Three levels of sensitivity are available: low, medium, high. With a low level of sensitivity, the algorithm will be more tolerant, resulting in a larger tolerance area, and less alerts. On the contrary, a high level of sensitivity will result in a small tolerance area around the curve, and thus more alerts will be identified. The detection sensitivity can be changed on the graph options.

Low

Medium

High

Technical Information and FAQ