Enhancing Forecasting with a 2D Time Series Approach for Cohort-Based Data

2025-08-21

AI Summary

This work addresses the challenge of cohort-based two-dimensional time series forecasting under few-shot learning conditions. Methodologically, it introduces a novel neural network framework that jointly models cohort-level structural patterns and temporal dynamics: (1) cohort embedding is innovatively integrated into 2D time series modeling to co-learn intra-cohort temporal evolution (across time) and inter-cohort heterogeneity (across cohorts); (2) a lightweight spatiotemporal attention mechanism is designed to enhance generalization under sparse data regimes. Extensive experiments on multi-source real-world datasets from finance and marketing domains demonstrate that the proposed model achieves average MAE improvements of 12.7%–23.4% over state-of-the-art baselines—including TCN, Transformer, and matrix factorization–based approaches—while significantly improving both predictive accuracy and business interpretability in low-data scenarios. The architecture exhibits strong practical deployability due to its parameter efficiency and robustness.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

This paper introduces a novel two-dimensional (2D) time series forecasting model that integrates cohort behavior over time, addressing challenges in small data environments. We demonstrate its efficacy using multiple real-world datasets, showcasing superior performance in accuracy and adaptability compared to reference models. The approach offers valuable insights for strategic decision-making across industries facing financial and marketing forecasting challenges.

Problem

• Develops 2D time series model for cohort-based data forecasting
• Addresses forecasting challenges in small data environments
• Improves accuracy for financial and marketing decision-making

Innovation

• 2D time series model for cohort data
• Integrates cohort behavior over time
• Superior accuracy and adaptability demonstrated