Local vs. Global Models for Hierarchical Forecasting

• URL: http://arxiv.org/abs/2411.06394v1
• Date: Sun, 10 Nov 2024 08:51:49 GMT
• Title: Local vs. Global Models for Hierarchical Forecasting
• Authors: Zhao Yingjie, Mahdi Abolghasemi,
• Abstract summary: This study explores the influence of distinct information utilisation on the accuracy of hierarchical forecasts. We develop Global Forecasting Models (GFMs) to exploit cross-series and cross-hierarchies information. Two specific GFMs based on LightGBM are introduced, demonstrating superior accuracy and lower model complexity.
• Score: 0.0
• License:
• Abstract: 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).

Related papers

• MITA: Bridging the Gap between Model and Data for Test-time Adaptation [68.62509948690698]
  Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. We propose Meet-In-The-Middle based MITA, which introduces energy-based optimization to encourage mutual adaptation of the model and data from opposing directions.
  arXiv  Detail & Related papers  (2024-10-12T07:02:33Z)
• Reducing Spurious Correlation for Federated Domain Generalization [15.864230656989854]
  In open-world scenarios, global models may struggle to predict well on entirely new domain data captured by certain media. Existing methods still rely on strong statistical correlations between samples and labels to address this issue. We introduce FedCD, an overall optimization framework at both the local and global levels.
  arXiv  Detail & Related papers  (2024-07-27T05:06:31Z)
• GenBench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models [56.63218531256961]
  We introduce GenBench, a benchmarking suite specifically tailored for evaluating the efficacy of Genomic Foundation Models. GenBench offers a modular and expandable framework that encapsulates a variety of state-of-the-art methodologies. We provide a nuanced analysis of the interplay between model architecture and dataset characteristics on task-specific performance.
  arXiv  Detail & Related papers  (2024-06-01T08:01:05Z)
• Boosted Control Functions [10.503777692702952]
  This work aims to bridge the gap between causal effect estimation and prediction tasks. We establish a novel connection between the field of distribution from machine learning, and simultaneous equation models and control function from econometrics. Within this framework, we propose a strong notion of invariance for a predictive model and compare it with existing (weaker) versions.
  arXiv  Detail & Related papers  (2023-10-09T15:43:46Z)
• DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup [58.894901088797376]
  federated learning has emerged as a promising approach for training a global model using data from multiple organizations without leaking their raw data. We propose a general framework to solve the above two challenges simultaneously. We provide comprehensive theoretical analysis including robustness analysis, convergence analysis, and generalization ability.
  arXiv  Detail & Related papers  (2022-04-16T08:08:29Z)
• LoMEF: A Framework to Produce Local Explanations for Global Model Time Series Forecasts [2.3096751699592137]
  Global Forecasting Models (GFM) that are trained across a set of multiple time series have shown superior results in many forecasting competitions and real-world applications. However, GFMs typically lack interpretability, especially towards particular time series. We propose a novel local model-agnostic interpretability approach to explain the forecasts from GFMs.
  arXiv  Detail & Related papers  (2021-11-13T00:17:52Z)
• Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization [89.73665256847858]
  We show that out-of-distribution performance is strongly correlated with in-distribution performance for a wide range of models and distribution shifts. Specifically, we demonstrate strong correlations between in-distribution and out-of-distribution performance on variants of CIFAR-10 & ImageNet. We also investigate cases where the correlation is weaker, for instance some synthetic distribution shifts from CIFAR-10-C and the tissue classification dataset Camelyon17-WILDS.
  arXiv  Detail & Related papers  (2021-07-09T19:48:23Z)
• Graphical Modeling for Multi-Source Domain Adaptation [56.05348879528149]
  Multi-Source Domain Adaptation (MSDA) focuses on transferring the knowledge from multiple source domains to the target domain. We propose two types of graphical models,i.e. Conditional Random Field for MSDA (CRF-MSDA) and Markov Random Field for MSDA (MRF-MSDA) We evaluate these two models on four standard benchmark data sets of MSDA with distinct domain shift and data complexity.
  arXiv  Detail & Related papers  (2021-04-27T09:04:22Z)
• Characterizing Fairness Over the Set of Good Models Under Selective Labels [69.64662540443162]
  We develop a framework for characterizing predictive fairness properties over the set of models that deliver similar overall performance. We provide tractable algorithms to compute the range of attainable group-level predictive disparities. We extend our framework to address the empirically relevant challenge of selectively labelled data.
  arXiv  Detail & Related papers  (2021-01-02T02:11:37Z)
• Ensembles of Localised Models for Time Series Forecasting [7.199741890914579]
  We study how ensembling techniques can be used with generic GFMs and univariate models to solve this issue. Our work systematises and compares relevant current approaches, namely clustering series and training separate submodels per cluster. We propose a new methodology of clustered ensembles where we train multiple GFMs on different clusters of series.
  arXiv  Detail & Related papers  (2020-12-30T06:33:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.