Falkonry Time Series Intelligence¶

Time Series Intelligence (TSI) is an AI-native software tool designed to analyze real-time metrics from telemetry and instrumentation sources with minimal programming effort. It enables engineers to extract actionable insights from observability data across industrial, IT, and networking systems. By efficiently processing large volumes of data, TSI identifies and highlights periods and subsets of time series containing the most informative data. This facilitates alerting, root cause analysis, action planning, and comparative assessments to drive continuous operational improvement.

Key Features¶

Observability systems generate vast amounts of complex data that traditional techniques like business intelligence, complex event processing, search, or mathematical modeling struggle to handle effectively. Deriving actionable insights from such data requires advanced methodologies, including stream processing, signal processing, convolutional analysis, and logical reasoning.

Features Overview¶

TSI addresses the challenges of processing observability data and offers the following AI-driven capabilities:

No-code/low-code exploratory data analysis: Simplifies data exploration without extensive programming expertise.
Time series embedding generator: Captures essential characteristics of time series data for downstream tasks.
Time series anomaly detection and classification: Identifies and categorizes deviations from expected behavior.
Machine learning model management: Organizes, deploys, and monitors machine learning models.
Model inference scheduling: Automates continuous and efficient model predictions.
Dependency management for time series calculations: Handles interdependent time series transformations efficiently.
Data aggregation for scaled processing: Combines large datasets for streamlined analysis.
Metadata-based dynamic dashboards: Creates dashboards that adapt based on metadata attributes.
Visualization tools for time series: Offers dynamic and customizable visual representations of time series data.

Capabilities¶

TSI empowers engineers to interrogate and derive inferences from time series data.

Data Ingestion¶

TSI ingests data into a streaming interface, enabling seamless processing and analysis. Data can be ingested from a variety of sources while dealing with data flow challenges resulting from upstream errors, networking glitches and changing operations.

Reports¶

Reports visualize time series data through flexible formats such as time plots, value distribution plots, and temporal trends. They provide dynamic access to time series data based on time ranges, enhancing visibility and root cause analysis.

Calculations¶

Calculations transform raw data into derivative time series, highlighting domain-specific characteristics based on expert knowledge. These transformations, akin to Matlab expressions and Python functions, offer a flexible and intuitive mechanism for applying standard signal derivations.

Rules¶

Rules convert analog quantities like raw signals, anomaly scores, and condition labels into discrete events based on thresholds automatically assessed by Falkonry TSI. They enable condition-based actions through a no-code interface, leveraging specific behaviors derived from signal combinations.

Insights¶

Insights automatically analyze time series data for anomalies by identifying deviations from normal operating behavior without requiring setup or supervision. They help users understand the origins and contributors of anomalies, enabling appropriate actions to mitigate their effects and likelihood.

Patterns¶

Patterns uncover meaningful temporal correlations, such as early warnings or stages of deterioration, in complex systems using multivariate data streams. Designed for reliability engineers and data analysts, Patterns facilitate continuous monitoring of specific events and known conditions, as well as root cause analysis to identify permanent fixes.

Stakeholder Benefits¶

Here’s a high-level overview of the problems Falkonry TSI solves for different stakeholders:

For Business & Leadership:¶

Drives Operational Excellence: TSI's vision is to drive actions from time series data to optimize and protect assets, simplifying the analysis of production data and enabling advanced continuous improvement.
Lowers Total Cost of Ownership (TCO): By automating anomaly detection and pattern recognition without requiring extensive coding or feature engineering, TSI significantly reduces support and maintenance overhead compared to traditional data science programs and custom ML development.
Faster Decision Making: It provides timely intelligence from raw sensor data, leading to quicker project decision-making.
Improved Productivity and Coverage: Implementing best practices with TSI can lead to increased productivity and significantly increased coverage by focusing on methods that that enable faster data analysis.

For Operations Teams & Engineers:¶

Uncovers Hidden Problems: TSI automatically detects never-before-seen anomalies and emerging patterns in complex industrial systems, ensuring early detection of unforeseen operational risks that traditional methods might miss. This helps to prevent unscheduled downtimes and improve overall asset performance.
Reduces Alert Overload: Unlike SCADA systems that can inundate users with alerts, TSI has capabilities to denoise and auto-snooze alerts, providing high-value notifications without overwhelming users.
Simplifies Data Understanding: It offers intuitive, maps-like visualization of high-resolution time series data, allowing users to easily zoom, pan, and explore signals for quick diagnosis and root cause analysis. This enhances situational awareness and speeds up troubleshooting.
Empowers Non-Programmers: With its no-code interface, TSI democratizes analytics, allowing subject matter experts (SMEs), electrical engineers, equipment engineers, and process engineers to build rules, detect anomalies, and recognize patterns without needing to write code or manage complex IT infrastructure.
Streamlines Workflows: It supports condition-based actions by triggering alerts based on specific behaviors and integrating with existing maintenance and asset management systems (CMMS/EAM) to create work orders and reports.
Automates Data Preparation: TSI is designed to handle various data pathologies like gaps, different sampling frequencies, and irregular timestamps, performing the majority of data wrangling automatically so users can focus on analysis.
Enhances Collaboration & Knowledge Transfer: Users can create comprehensive reports with rich-text summaries, charts, and images, fostering collaboration and serving as a knowledge base for incident documentation, model validation, and analysis.

For IT/Digital Teams:¶

Robust Data Ingestion: TSI provides flexible and secure inbound connectivity options to bring in live and historical time-series data from multiple industrial sources like PLCs, SCADA, historians, and IoT devices using standard protocols such as MQTT, Parquet, CSV, AWS IoT Core, Azure IoT, iba Systems, and Litmus.
Scalable Architecture: It has a cloud-native architecture that can be deployed in a public cloud, private cloud, or on local infrastructure, offering flexibility to match data security and latency needs.
Efficient Resource Utilization: TSI is resource-efficient and can significantly reduce I/O costs and accelerate model learning through features like data downsampling.
Seamless Integration: All capabilities are accessible via comprehensive REST APIs, allowing IT teams to easily integrate Falkonry TSI with existing enterprise systems, dashboards, and custom applications, accelerating automation and data extraction.
Reduced Operational Delays: Actively managing data connections helps to remove operationalization delays due to improperly formatted data or signal changes, potentially reducing time from data availability to ready-to-use findings by up to 10 days.

To quickly start processing your time series data through TSI, please see our Quick Start Tutorial.