arXiv subjects

Mahdi Abolghasemi

Publications and source records attributed to Mahdi Abolghasemi.

At least 19 recordsLinked to original sources

arXiv2026

Hierarchical Bayes meets hierarchical forecasting: A flexible framework for level-focused forecasts

Decision-making in hierarchical systems requires probabilistic forecasts at all cross-sectional levels. Current hierarchical forecasting methods typically generate independent forecasts at each level and reconcile them post hoc to ensure coherence between upper and lower levels. Such post hoc corrections do not incorporate hierarchical structure or decision goals into the underlying parameter estimation. We propose a fully Bayesian hierarchical forecasting framework that shares information more effectively between and across levels than reconciliation alone. Our approach has the flexibility to softly penalise incoherence, subject to model specification, and to focus the global model and coherence update on hierarchical levels most relevant to decision outcomes. This yields parameter estimates that are focused towards the forecasting goals and capture the requirement for coherency, removing the need to estimate covariance matrices for multi-step forecasting horizons. We demonstrate improvements in predictive accuracy metrics on both simulated data and Australian domestic tourism forecasting.

stat.ME↗

arXiv2026

Detection of Anomalous Network Nodes via Hierarchical Prediction and Extreme Value Theory

Continuously evolving cyber-attacks against industrial networks reduce the effectiveness of signature-based detection methods. Once malware has infiltrated a network (for example, entering via an unsecured device), it can infect further network nodes and carry out malicious activity. Infected nodes can exhibit unusual behaviour in their use of Address Resolution Protocol (ARP) calls within the network. In order to detect such anomalous nodes, we propose a two-stage method: (i) modelling of ARP call behaviour via hierarchical time series prediction methods, and (ii) exploiting Extreme Value Theory (EVT) to robustly detect whether deviations from expected behaviour are anomalous. EVT is able to handle heavy-tailed distributions which are exhibited by internet traffic. Empirical evaluations on a real-life dataset containing over 10M ARP calls from 362 nodes show that the proposed method results in considerably reduced number of false positives, addressing the problem of alert fatigue commonly reported by security professionals.

cs.CR↗

arXiv2025

Acceleration Techniques for Learning Optimal Classification Trees with Integer Programming

Decision trees are a popular machine learning model which are traditionally trained by heuristic methods. Massive improvements in computing power and optimisation techniques has led to renewed interest in learning globally optimal decision trees. Empirical evidence shows that optimal classification trees (OCTs) have better out-of-sample performance than heuristic methods. The dominant optimisation paradigms for training OCTs are mixed-integer programming (MIP) and dynamic programming (DP). MIP formulations offer flexibility in the objectives and constraints that are modelled, but suffer from poor scaling in the size of the training dataset and the maximum tree depth. DP models represent the state of the art in scaling for OCTs, but lack some of the flexibility of MIP models. In this paper we present progress on using advanced integer programming methods to integrate ideas from DP models into MIP formulations to begin bridging the scaling gap. Using the existing BendOCT model from the literature as a base model, we introduce valid inequalities, cutting planes, and a primal heuristic to improve the scaling of MIP formulations. We show that these techniques significantly improve the ability of BendOCT to find provably optimal solutions over a wide range of datasets.

math.OC↗

arXiv2025

Improving the forecast accuracy of wind power by leveraging multiple hierarchical structure

Renewable energy generation is of utmost importance for global decarbonization. Forecasting renewable energies, particularly wind energy, is challenging due to the inherent uncertainty in wind energy generation, which depends on weather conditions. Recent advances in hierarchical forecasting through reconciliation have demonstrated a significant increase in the quality of wind energy forecasts for short-term periods. We leverage the cross-sectional and temporal hierarchical structure of turbines in wind farms and build cross-temporal hierarchies to further investigate how integrated cross-sectional and temporal dimensions can add value to forecast accuracy in wind farms. We found that cross-temporal reconciliation was superior to individual cross-sectional reconciliation at multiple temporal aggregations. Additionally, machine learning based forecasts that were cross-temporally reconciled demonstrated high accuracy at coarser temporal granularities, which may encourage adoption for short-term wind forecasts. Empirically, we provide insights for decision-makers on the best methods for forecasting high-frequency wind data across different forecasting horizons and levels.

cs.LG↗

arXiv2025

Predict+Optimize Problem in Renewable Energy Scheduling

Predict+Optimize frameworks integrate forecasting and optimization to address real-world challenges such as renewable energy scheduling, where variability and uncertainty are critical factors. This paper benchmarks solutions from the IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling, focusing on forecasting renewable production and demand and optimizing energy cost. The competition attracted 49 participants in total. The top-ranked method employed stochastic optimization using LightGBM ensembles, and achieved at least a 2% reduction in energy costs compared to deterministic approaches, demonstrating that the most accurate point forecast does not necessarily guarantee the best performance in downstream optimization. The published data and problem setting establish a benchmark for further research into integrated forecasting-optimization methods for energy systems, highlighting the importance of considering forecast uncertainty in optimization models to achieve cost-effective and reliable energy management. The novelty of this work lies in its comprehensive evaluation of Predict+Optimize methodologies applied to a real-world renewable energy scheduling problem, providing insights into the scalability, generalizability, and effectiveness of the proposed solutions. Potential applications extend beyond energy systems to any domain requiring integrated forecasting and optimization, such as supply chain management, transportation planning, and financial portfolio optimization.

cs.AI↗

arXiv2024

Balancing Accuracy and Costs in Cross-Temporal Hierarchies: Investigating Decision-Based and Validation-Based Reconciliation

Wind power forecasting is essential for managing daily operations at wind farms and enabling market operators to manage power uncertainty effectively in demand planning. This paper explores advanced cross-temporal forecasting models and their potential to enhance forecasting accuracy. First, we propose a novel approach that leverages validation errors, rather than traditional in-sample errors, for covariance matrix estimation and forecast reconciliation. Second, we introduce decision-based aggregation levels for forecasting and reconciliation where certain horizons are based on the required decisions in practice. Third, we evaluate the forecasting performance of the models not only on their ability to minimize errors but also on their effectiveness in reducing decision costs, such as penalties in ancillary services. Our results show that statistical-based hierarchies tend to adopt less conservative forecasts and reduce revenue losses. On the other hand, decision-based reconciliation offers a more balanced compromise between accuracy and decision cost, making them attractive for practical use.

stat.ME↗

arXiv2024

Local vs. Global Models for Hierarchical Forecasting

Hierarchical time series forecasting plays a crucial role in decision-making in various domains while presenting significant challenges for modelling as they involve multiple levels of aggregation, constraints, and availability of information. This study explores the influence of distinct information utilisation on the accuracy of hierarchical forecasts, proposing and evaluating locals and a range of Global Forecasting Models (GFMs). In contrast to local models, which forecast each series independently, we develop GFMs to exploit cross-series and cross-hierarchies information, improving both forecasting performance and computational efficiency. We employ reconciliation methods to ensure coherency in forecasts and use the Mean Absolute Scaled Error (MASE) and Multiple Comparisons with the Best (MCB) tests to assess statistical significance. The findings indicate that GFMs possess significant advantages for hierarchical forecasting, providing more accurate and computationally efficient solutions across different levels in a hierarchy. Two specific GFMs based on LightGBM are introduced, demonstrating superior accuracy and lower model complexity than their counterpart local models and conventional methods such as Exponential Smoothing (ES) and Autoregressive Integrated Moving Average (ARIMA).

cs.LG↗

arXiv2024

Digital Twins for forecasting and decision optimisation with machine learning: applications in wastewater treatment

Prediction and optimisation are two widely used techniques that have found many applications in solving real-world problems. While prediction is concerned with estimating the unknown future values of a variable, optimisation is concerned with optimising the decision given all the available data. These methods are used together to solve problems for sequential decision-making where often we need to predict the future values of variables and then use them for determining the optimal decisions. This paradigm is known as forecast and optimise and has numerous applications, e.g., forecast demand for a product and then optimise inventory, forecast energy demand and schedule generations, forecast demand for a service and schedule staff, to name a few. In this extended abstract, we review a digital twin that was developed and applied in wastewater treatment in Urban Utility to improve their operational efficiency. While the current study is tailored to the case study problem, the underlying principles can be used to solve similar problems in other domains.

cs.LG↗

arXiv2023

Approximating Solutions to the Knapsack Problem using the Lagrangian Dual Framework

The Knapsack Problem is a classic problem in combinatorial optimisation. Solving these problems may be computationally expensive. Recent years have seen a growing interest in the use of deep learning methods to approximate the solutions to such problems. A core problem is how to enforce or encourage constraint satisfaction in predicted solutions. A promising approach for predicting solutions to constrained optimisation problems is the Lagrangian Dual Framework which builds on the method of Lagrangian Relaxation. In this paper we develop neural network models to approximate Knapsack Problem solutions using the Lagrangian Dual Framework while improving constraint satisfaction. We explore the problems of output interpretation and model selection within this context. Experimental results show strong constraint satisfaction with a minor reduction of optimality as compared to a baseline neural network which does not explicitly model the constraints.

cs.LG↗

arXiv2022

The intersection of machine learning with forecasting and optimisation: theory and applications

Forecasting and optimisation are two major fields of operations research that are widely used in practice. These methods have contributed to each other growth in several ways. However, the nature of the relationship between these two fields and integrating them have not been explored or understood enough. We advocate the integration of these two fields and explore several problems that require both forecasting and optimisation to deal with the uncertainties. We further investigate some of the methodologies that lie at the intersection of machine learning with prediction and optimisation to address real-world problems. Finally, we provide several research directions for those interested to work in this domain.

cs.LG↗

arXiv2022

How to predict and optimise with asymmetric error metrics

In this paper, we examine the concept of the predict and optimise problem with specific reference to the third Technical Challenge of the IEEE Computational Intelligence Society. In this competition, entrants were asked to forecast building energy use and solar generation at six buildings and six solar installations, and then use their forecast to optimize energy cost while scheduling classes and batteries over a month. We examine the possible effect of underforecasting and overforecasting and asymmetric errors on the optimisation cost. We explore the different nature of loss functions for the prediction and optimisation phase and propose to adjust the final forecasts for a better optimisation cost. We report that while there is a positive correlation between these two, more appropriate loss functions can be used to optimise the costs associated with final decisions.

cs.LG↗

arXiv2021

The value of point of sales information in upstream supply chain forecasting: an empirical investigation

Traditionally, manufacturers use past orders (received from some downstream supply chain level) to forecast future ones, before turning such forecasts into appropriate inventory and production optimization decisions. With recent advances in information sharing technologies, upstream supply chain (SC) companies may have access to downstream point of sales (POS) data. Such data can be used as an alternative source of information for forecasting. There are a few studies that investigate the benefits of using orders versus POS data in upstream SC forecasting; the results are mixed and empirical evidence is lacking, particularly in the context of multi-echelon SCs and in the presence of promotions. We investigate an actual three-echelon SC with 684 series where the manufacturer aims to forecast orders received from distribution centers (DCs) using either aggregated POS data at DC level or historical orders received from the DCs. Our results show that the order-based methods outperform the POS-based ones by 6\%-15\%. We find that low values of mean, variance, non-linearity and entropy of POS data, and promotion presence negatively impact the performance of the POS-based forecasts. Such findings are useful for determining the appropriate source of data and the impact of series characteristics for order forecasting in SCs.

stat.AP↗

arXiv2021

Forecasting sales with Bayesian networks: a case study of a supermarket product in the presence of promotions

Sales forecasting is the prerequisite for a lot of managerial decisions such as production planning, material resource planning and budgeting in the supply chain. Promotions are one of the most important business strategies that are often used to boost sales. While promotions are attractive for generating demand, it is often difficult to forecast demand in their presence. In the past few decades, several quantitative models have been developed to forecast sales including statistical and machine learning models. However, these methods may not be adequate to account for all the internal and external factors that may impact sales. As a result, qualitative models have been adopted along with quantitative methods as consulting experts has been proven to improve forecast accuracy by providing contextual information. Such models are being used extensively to account for factors that can lead to a rapid change in sales, such as during promotions. In this paper, we aim to use Bayesian Networks to forecast promotional sales where a combination of factors such as price, type of promotions, and product location impacts sales. We choose to develop a BN model because BN models essentially have the capability to combine various qualitative and quantitative factors with causal forms, making it an attractive tool for sales forecasting during promotions. This can be used to adjust a company's promotional strategy in the context of this case study. We gather sales data for a particular product from a retailer that sells products in Australia. We develop a Bayesian Network for this product and validate our results by empirical analysis. This paper confirms that BNs can be effectively used to forecast sales, especially during promotions. In the end, we provide some research avenues for using BNs in forecasting sales.

cs.LG↗

arXiv2021

State-of-the-art predictive and prescriptive analytics for IEEE CIS 3rd Technical Challenge

In this paper, we describe our proposed methodology to approach the predict+optimise challenge introduced in the IEEE CIS 3rd Technical Challenge. The predictive model employs an ensemble of LightGBM models and the prescriptive analysis employs mathematical optimisation to efficiently prescribe solutions that minimise the average cost over multiple scenarios. Our solutions ranked 1st in the optimisation and 2nd in the prediction challenge of the competition.

cs.LG↗

arXiv2021

How to effectively use machine learning models to predict the solutions for optimization problems: lessons from loss function

Using machine learning in solving constraint optimization and combinatorial problems is becoming an active research area in both computer science and operations research communities. This paper aims to predict a good solution for constraint optimization problems using advanced machine learning techniques. It extends the work of \cite{abbasi2020predicting} to use machine learning models for predicting the solution of large-scaled stochastic optimization models by examining more advanced algorithms and various costs associated with the predicted values of decision variables. It also investigates the importance of loss function and error criterion in machine learning models where they are used for predicting solutions of optimization problems. We use a blood transshipment problem as the case study. The results for the case study show that LightGBM provides promising solutions and outperforms other machine learning models used by \cite{abbasi2020predicting} specially when mean absolute deviation criterion is used.

cs.LG↗

arXiv2020

Model selection in reconciling hierarchical time series

Model selection has been proven an effective strategy for improving accuracy in time series forecasting applications. However, when dealing with hierarchical time series, apart from selecting the most appropriate forecasting model, forecasters have also to select a suitable method for reconciling the base forecasts produced for each series to make sure they are coherent. Although some hierarchical forecasting methods like minimum trace are strongly supported both theoretically and empirically for reconciling the base forecasts, there are still circumstances under which they might not produce the most accurate results, being outperformed by other methods. In this paper we propose an approach for dynamically selecting the most appropriate hierarchical forecasting method and succeeding better forecasting accuracy along with coherence. The approach, to be called conditional hierarchical forecasting, is based on Machine Learning classification methods and uses time series features as leading indicators for performing the selection for each hierarchy examined considering a variety of alternatives. Our results suggest that conditional hierarchical forecasting leads to significantly more accurate forecasts than standard approaches, especially at lower hierarchical levels.

cs.LG↗

arXiv2020

Hierarchical forecast reconciliation with machine learning

Hierarchical forecasting methods have been widely used to support aligned decision-making by providing coherent forecasts at different aggregation levels. Traditional hierarchical forecasting approaches, such as the bottom-up and top-down methods, focus on a particular aggregation level to anchor the forecasts. During the past decades, these have been replaced by a variety of linear combination approaches that exploit information from the complete hierarchy to produce more accurate forecasts. However, the performance of these combination methods depends on the particularities of the examined series and their relationships. This paper proposes a novel hierarchical forecasting approach based on machine learning that deals with these limitations in three important ways. First, the proposed method allows for a non-linear combination of the base forecasts, thus being more general than the linear approaches. Second, it structurally combines the objectives of improved post-sample empirical forecasting accuracy and coherence. Finally, due to its non-linear nature, our approach selectively combines the base forecasts in a direct and automated way without requiring that the complete information must be used for producing reconciled forecasts for each series and level. The proposed method is evaluated both in terms of accuracy and bias using two different data sets coming from the tourism and retail industries. Our results suggest that the proposed method gives superior point forecasts than existing approaches, especially when the series comprising the hierarchy are not characterized by the same patterns.

cs.LG↗

arXiv2019

Machine learning applications in time series hierarchical forecasting

Hierarchical forecasting (HF) is needed in many situations in the supply chain (SC) because managers often need different levels of forecasts at different levels of SC to make a decision. Top-Down (TD), Bottom-Up (BU) and Optimal Combination (COM) are common HF models. These approaches are static and often ignore the dynamics of the series while disaggregating them. Consequently, they may fail to perform well if the investigated group of time series are subject to large changes such as during the periods of promotional sales. We address the HF problem of predicting real-world sales time series that are highly impacted by promotion. We use three machine learning (ML) models to capture sales variations over time. Artificial neural networks (ANN), extreme gradient boosting (XGboost), and support vector regression (SVR) algorithms are used to estimate the proportions of lower-level time series from the upper level. We perform an in-depth analysis of 61 groups of time series with different volatilities and show that ML models are competitive and outperform some well-established models in the literature.

cs.LG↗