About

Fourier is a standalone desktop application, built with data security and compliance in mind, for identifying outliers and anomalous behavior in numerical, real-valued time-series data using advanced statistical methods and explainable AI. It runs entirely on the user's local machine and requires no internet connection to use; just a valid license file.

Fourier was designed to help automate and solve a persistent problem that many institutions and businesses who manage large volumes of time series data face: Identifying anomalies in newly reported values as data updates over time. It acts as a virtual analyst or analyst assistant  that efficiently scans all of your data to surface outlier behavior for human review. 

Fourier is a truly plug-and-play solution with no manual tuning required, eliminating learning curves and enabling effortless use even for non-technical users . With a clean, intuitive interface and fast local processing, analysts can run anomaly checks across hundreds of thousands of time series in seconds and quickly prioritize their reviews using Fourier’s ranked anomaly scores. This saves time and improves data quality by allowing analysts to focus on what they do best, interpreting results and making informed decisions, instead of tedious manual searches. 

At its core, Fourier uses efficient yet powerful mathematical techniques to model trends in your data and automatically detect if the latest entries deviate from their historical norms. 

Unlike traditional rules-based methods which are often ineffective when the underlying data is highly variable or complex, this dynamic approach allows Fourier to work on a huge variety of datasets without the need for any tuning. The only technical requirements are that the data must be real-valued,  1-dimensional, and have at least 5 data points.

To further improve its outlier detection capabilities, Fourier possesses advanced,  toggleable, feature detection and mitigation systems which are explained in more detail below.