budgeting and forecasting

This study addresses the finite-horizon budget allocation problem under non-stationary changes in return efficiency by formulating it as a closed-loop economic control problem. The authors employ a receding-horizon model predictive control (MPC) approach to dynamically optimize budget allocation, accounting for execution noise and operational constraints. Through comparison with reactive strategies, the research demonstrates that non-stationarity alone is insufficient for MPC to outperform reactive methods; MPC achieves significant and sustained superiority only when the return efficiency exhibits predictable structures that the model can effectively capture, thereby enabling advantageous intertemporal trade-offs. In contrast, under scenarios of random drift or stationarity, MPC offers no notable performance advantage over reactive approaches.

Large-scale manufacturers face challenges in after-sales demand forecasting, including difficulty in fusing heterogeneous multi-source signals, weak modeling of COVID-19 disruptions, imbalanced prediction accuracy for long-tail versus high-revenue items, and insufficient business interpretability. Method: We propose an end-to-end interpretable ensemble forecasting framework integrating statistical models, deep learning, and large language models (LLMs). Key innovations include Pareto-aware segmented forecasting, horizon-aware weighted ensemble integration, LLM-driven automated attribution narrative generation, plus integrated change-point detection and WMAPE-optimized calibration. Contribution/Results: The system delivers city-item-level calibrated probabilistic forecasts across 90+ countries and 6,000 SKUs, simultaneously improving prediction accuracy, stability, and operational alignment. Through a performance scorecard and trend attribution module, it shifts evaluation from static accuracy metrics to a dynamic, intervention-oriented decision loop.

研究通过FWBench工具评估了语言模型在成本限制下选择和使用时间序列预测进行决策的能力，测试了包括小型语言模型在内的十种配置。

This study addresses how agents can efficiently allocate external state observation resources under a shared budget. We propose BudgetPM, the first framework to formally define resource allocation in this setting. By leveraging a logistic scorer and full-episode hindsight distillation to train a lightweight policy network, combined with a hard-budget executor, our approach dynamically optimizes the timing of external inspections intended for memory storage. Furthermore, we devise differentiated strategies distinguishing between capacity-sufficient and capacity-scarce scenarios. Experimental results demonstrate that our method reduces observation overhead by 42–54% while maintaining near-perfect task performance. Under severely constrained budgets, it achieves substantial improvements in F1 score alongside a 16–33% reduction in observation volume.

To address challenges in commercial management system development—including poor alignment between process models and execution platforms, low model reusability, and suboptimal development efficiency—this paper proposes a metamodel-based Model-Driven Development (MDD) approach. We design an evolvable and extensible business process metamodel framework and introduce a staged model transformation mechanism supporting QVT/ATL, enabling automated adaptation of extended BPMN models to diverse execution platforms. Crucially, we deeply integrate MDD into BPM system construction, establishing business models as the authoritative source governing system behavior. Experimental evaluation demonstrates significant improvements in development productivity and model consistency, robust cross-platform model reuse, and validates the metamodel’s effectiveness and flexibility in extended application scenarios such as resource management and customer relationship management.

This work addresses the challenge of maximizing end-to-end success probability in structured agent workflows under hard constraints on budget and deadline. The authors propose Monte Carlo Combinatorial Planning (MCPP), a lightweight closed-loop planner that dynamically replans during execution in response to observations. MCPP employs a finite-horizon stochastic online allocation model with parallel sampling and leverages Monte Carlo simulation to estimate, in real time, the probability of successful task completion under the given constraints. Experimental results demonstrate that MCPP significantly outperforms strong baseline methods on the CodeFlow and ProofFlow benchmarks, consistently achieving higher task completion rates across diverse budget–deadline configurations. These findings validate MCPP’s effectiveness and robustness in resource-constrained scenarios.

Traditional revenue forecasting approaches struggle to uncover the underlying customer behavioral drivers—such as customer acquisition, repeat purchase rates, and average transaction value—that influence revenue dynamics. To address this limitation, this work proposes the Customer-Based Multi-Task Transformer (CBMT), which uniquely integrates multi-task learning with a Transformer architecture to jointly model customer behavioral metrics and total revenue through shared representations. Furthermore, CBMT incorporates a downstream alignment mechanism to enhance both interpretability and predictive accuracy. Empirical evaluation on real-world customer transaction panel data demonstrates that CBMT outperforms existing methods across 23 out of 24 evaluation metrics, achieving a 30% reduction in total sales prediction error compared to the strongest baseline and significantly surpassing single-task models employed by 74.3% of firms.

本文提出Phorecaster365架构，通过结合企业资源规划数据和机器学习模型，为药品销售预测提供支持，并强调了人工监督的重要性。

This work addresses the challenge of balancing emission constraints, operational cost, and service quality in dynamic power grids where carbon intensity varies over time. Traditional fixed emission rate strategies prove inadequate under such conditions. To overcome this limitation, the authors propose a time-window-based emission budgeting mechanism that replaces static rates, enabling applications to accrue emission allowances during low-carbon periods and flexibly consume them during high-carbon intervals. Integrated within a MAPE-K adaptive control architecture, the approach leverages real-time monitoring of grid carbon intensity and system power consumption to dynamically schedule resources while adhering to long-term emission caps. Simulations using six weeks of real-world data from Germany, France, and Poland demonstrate that the method improves task completion rates by up to 36% in volatile grids while matching the performance of existing approaches in stable grids, achieving effective co-optimization of emissions, cost, and performance.

This study addresses the challenge of predicting individual task labor demand under variable forecasting horizons caused by heterogeneous task durations in construction projects, where predictions must adhere to a predefined total labor constraint. To this end, the authors propose the Constraint-Preserving Residual Allocation Forecasting (CP-RAF) method, which encodes historical labor sequences into temporal shape coefficient vectors and generates a labor distribution profile for the remaining duration by retrieving and similarity-weighting completed tasks. This profile dynamically allocates the total labor quota and adjusts the prediction horizon accordingly. CP-RAF is the first approach to explicitly embed the total labor constraint directly into the forecasting process, balancing operational feasibility with predictive accuracy. Experimental results on real-world construction site data demonstrate that CP-RAF significantly outperforms eight baseline models, achieving consistently low prediction errors across both medium- and long-term forecasting scenarios with fixed and variable horizons.