Idea
A convolutional module for accurate long-term time series forecasting benefiting data scientists and enterprises with trend-driven data.
Research Paper
Core Innovation
This paper introduces the ARMA block, a CNN-based module inspired by ARIMA that directly performs multi-step forecasting without iterative steps. It combines autoregressive and moving average convolutional components to capture trends and local variations efficiently. The block also encodes absolute positional information, offering a simpler alternative to traditional positional embeddings.
Market Size (TAM)
$2–10B TAM, $1–2B SAM; assumption: growing demand for advanced time series forecasting in finance, energy, and supply chain sectors.
Potential Customers & Pain Points
- Financial institutions needing accurate long-term forecasts
- Supply chain managers facing demand variability
- Energy companies predicting consumption trends
- AI developers seeking efficient positional encoding
- Researchers working on multivariate time series models
Business Model
Licensing the ARMA block as an API or integration module for forecasting platforms; consulting and customization services for enterprise clients.
Competitive Landscape
- DeepAR
- N-BEATS
- Informer
Implementation Challenges
- Adoption of new forecasting modules in established pipelines
- Competition from well-known forecasting models
- Demonstrating consistent superiority across diverse datasets
Validation Strategy
- Benchmark ARMA block against leading models on diverse datasets
- Pilot integration with enterprise forecasting systems
- Collect user feedback and iterate on model improvements
Research Paper Overview
ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting
Summary
This paper proposes a simple yet effective convolutional module for long-term time series forecasting. The proposed block, inspired by the Auto-Regressive Integrated Moving Average (ARIMA) model, consists of two convolutional components: one for capturing the trend (autoregression) and the other for refining local variations (moving average). Unlike conventional ARIMA, which requires iterative multi-step forecasting, the block directly performs multi-step forecasting, making it easily extendable to multivariate settings. Experiments on nine widely used benchmark datasets demonstrate that our method ARMA achieves competitive accuracy, particularly on datasets exhibiting strong trend variations, while maintaining architectural simplicity. Furthermore, analysis shows that the block inherently encodes absolute positional information, suggesting its potential as a lightweight replacement for positional embeddings in sequential models.