{"slug": "accounting-graph-transformer-for-short-history-multi-kpi-forecasting-in-small", "title": "Accounting Graph Transformer for Short-History Multi-KPI Forecasting in Small Businesses", "summary": "A new model called the Accounting Graph Transformer (AGT) outperforms existing baselines in forecasting 13 key performance indicators (KPIs) for small businesses with limited accounting history, achieving a mean absolute error (MAE) of 0.6990 ± 0.0013 compared to 0.7378 ± 0.0014 for the strongest baseline, LightGBM, across 11,993 forecast origins from 1,060 unseen companies. The model, introduced in an arXiv paper (2608.07037v1), uses typed attention on an accounting-relation graph and a gated recency path, and it also outperforms baselines on 7,094 additional companies with origins from January-May 2025, achieving 0.7548 MAE versus 0.7694 for SOFTS.", "body_md": "arXiv:2608.07037v1 Announce Type: new\nAbstract: Small businesses often have only 12-24 months of accounting history, yet planning and risk workflows require coordinated forecasts across financial statements. We study joint 12-month forecasting of 13 income-statement, balance-sheet, cash-flow, and working-capital key performance indicators (KPIs) from 71 monthly ledger series. We introduce the Accounting Graph Transformer (AGT), which represents each ledger series as a masked token, exchanges information through typed attention on a fixed accounting-relation graph, pools target-specific context, and fuses it with a gated three-month recency path. Across 11,993 forecast origins from 1,060 unseen companies, AGT achieves sample-weighted KPI-macro mean absolute error (MAE) $0.6990 \\pm 0.0013$ over three independent seeds, compared with $0.7378 \\pm 0.0014$ for the strongest baseline, LightGBM. At the pre-specified seed 42, a paired company-clustered bootstrap gives a LightGBM-minus-AGT difference of 0.0395 with 95% confidence interval (CI) $[0.0350,0.0439]$. AGT is best on all 13 KPIs against LightGBM, TimeMixer, and SOFTS in the matched seed-42 comparison, while final-architecture ablations show that relational attention, accounting topology, and the recency path each improve validation and test accuracy. On 7,094 additional unseen companies with origins sampled from January-May 2025, AGT obtains 0.7548 MAE versus 0.7694 for SOFTS. A single 5.3M-parameter model produces 156 aligned forecasts without company-specific fitting, providing one forecasting layer for integrated planning, liquidity, and working-capital analysis.", "url": "https://wpnews.pro/news/accounting-graph-transformer-for-short-history-multi-kpi-forecasting-in-small", "canonical_source": "https://www.machinebrief.com/news/accounting-graph-transformer-for-short-history-multi-kpi-for-et83", "published_at": "2026-08-10 04:00:00+00:00", "updated_at": "2026-08-10 05:12:06.235173+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence"], "entities": ["Accounting Graph Transformer", "LightGBM", "TimeMixer", "SOFTS", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/accounting-graph-transformer-for-short-history-multi-kpi-forecasting-in-small", "markdown": "https://wpnews.pro/news/accounting-graph-transformer-for-short-history-multi-kpi-forecasting-in-small.md", "text": "https://wpnews.pro/news/accounting-graph-transformer-for-short-history-multi-kpi-forecasting-in-small.txt", "jsonld": "https://wpnews.pro/news/accounting-graph-transformer-for-short-history-multi-kpi-forecasting-in-small.jsonld"}}