Idea
Scalable in-database platform delivering accurate, interpretable time series forecasts and anomaly detection at massive scale.
Research Paper
Core Innovation
This paper introduces ARIMA_PLUS, a modular time series framework combining interpretable statistical models with scalable cloud infrastructure. It uniquely integrates forecasting and anomaly detection in a single system, automates data cleaning and model selection, and achieves superior accuracy over both classical and neural network models. Its direct integration into BigQuery enables massive parallel processing and ease of use via SQL.
Why It Matters
Time series forecasting and anomaly detection are critical for industries like retail, manufacturing, and energy to optimize operations and detect issues early. ARIMA_PLUS automates these tasks at scale with high accuracy and interpretability, reducing manual effort and enabling faster, data-driven decisions. Its cloud-native design supports massive volumes, transforming workflows for enterprises managing millions of time series.
Market Size (TAM)
$20–50B TAM for enterprise time series analytics and forecasting platforms; $2–10B SAM from large enterprises in retail, manufacturing, energy driven by digital transformation and cloud adoption.
Potential Customers & Pain Points
- Retailers – Need scalable accurate demand forecasting
- Manufacturers – Require early anomaly detection to prevent downtime
- Advertisers – Need automated campaign performance insights
- Energy providers – Demand reliable load forecasting and anomaly alerts
- Cloud data teams – Seek integrated easy-to-use forecasting tools.
Business Model
Subscription-based SaaS integrated with Google Cloud services, charging based on volume of time series processed and compute resources used.
Competitive Landscape
- Prophet
- DeepAR
- N-BEATS
- AWS Forecast
- Google Vertex AI Forecasting
Implementation Challenges
- Customer adoption of new forecasting tools over established solutions
- Integration complexity with existing enterprise data workflows
- Maintaining model accuracy across diverse time series types and domains
Validation Strategy
- Pilot deployments with key retail and manufacturing customers
- Benchmarking against existing forecasting and anomaly detection tools
- Collecting user feedback on interpretability and ease of integration
- Scaling tests to demonstrate performance on millions of time series
Research Paper Overview
ARIMA_PLUS: Large-scale, Accurate, Automatic and Interpretable In-Database Time Series Forecasting and Anomaly Detection in Google BigQuery
Summary
ARIMA_PLUS is a scalable, accurate, and interpretable time series forecasting and anomaly detection framework integrated into Google BigQuery. It automates large-scale forecasting and anomaly detection with a simple SQL interface, handling millions of time series efficiently while providing insights through modular model components. It outperforms traditional statistical and modern neural network models on public benchmarks.