Keyword: multistep-forecasting

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  Summary of the strategies in MSF. In bold are our contributions. We extend the single-output Rectify strategy into its multi-output [13] variant, analogous to RecMO, DirMO [10], and DirRecMO [9]. Stratify is a framework which generalises all existing strategies and introduces novel strategies with improved performance. Lines show the evolution and fusion of previous strategies to form new ones.

  Stratify: Unifying Multi-Step Forecasting Strategies
  

  R. Green, G. Stevens, Z. Abdallah, T. de Menezes e Silva Filho

  Arxiv Preprint
  PDF DOI BIB ABSTRACT

  Keywords: time-series, multistep-forecasting, regression
  

  A key aspect of temporal domains is the ability to make predictions multiple time steps into the future, a process known as multi-step forecasting (MSF). At the core of this process is selecting a forecasting strategy, however, with no existing frameworks to map out the space of strategies, practitioners are left with ad-hoc methods for strategy selection. In this work, we propose Stratify, a parameterised framework that addresses multi-step forecasting, unifying existing strategies and introducing novel, improved strategies. We evaluate Stratify on 18 benchmark datasets, five function classes, and short to long forecast horizons (10, 20, 40, 80). In over 84% of 1080 experiments, novel strategies in Stratify improved performance compared to all existing ones. Importantly, we find that no single strategy consistently outperforms others in all task settings, highlighting the need for practitioners explore the Stratify space to carefully search and select forecasting strategies based on task-specific requirements. Our results are the most comprehensive benchmarking of known and novel forecasting strategies. We make code available to reproduce our results.

  @misc{green2024stratify, title={Stratify: Unifying Multi-Step Forecasting Strategies}, author={{Green}, R. and {Stevens}, G. and {Abdallah}, Z. and {de Menezes e Silva Filho}, T.}, year={2024}, eprint={2412.20510} }
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  The top-1 accuracy (the proportion of within-task instances where a strategy is optimal) aggregated over all datasets and task settings. DIRMO and RECMO include all σ parameters.

  Time-Series Classification for Dynamic Strategies in Multi-Step Forecasting
  

  R. Green, G. Stevens, T. de Menezes e Silva Filho, Z. Abdallah

  Arxiv Preprint
  PDF DOI BIB ABSTRACT

  Keywords: time-series, multistep-forecasting, regression
  

  Multi-step forecasting (MSF) in time-series, the ability to make predictions multiple time steps into the future, is fundamental to almost all temporal domains. To make such forecasts, one must assume the recursive complexity of the temporal dynamics. Such assumptions are referred to as the forecasting strategy used to train a predictive model. Previous work shows that it is not clear which forecasting strategy is optimal a priori to evaluating on unseen data. Furthermore, current approaches to MSF use a single (fixed) forecasting strategy. In this paper, we characterise the instance-level variance of optimal forecasting strategies and propose Dynamic Strategies (DyStrat) for MSF. We experiment using 10 datasets from different scales, domains, and lengths of multi-step horizons. When using a random-forest-based classifier, DyStrat outperforms the best fixed strategy, which is not knowable a priori, 94% of the time, with an average reduction in mean-squared error of 11%. Our approach typically triples the top-1 accuracy compared to current approaches. Notably, we show DyStrat generalises well for any MSF task.

  @misc{green2024timeseries, title={Time-Series Classification for Dynamic Strategies in Multi-Step Forecasting}, author={{Green}, R. and {Stevens}, G. and {Abdallah}, Z. and {de Menezes e Silva Filho}, T.}, year={2024}, eprint={2402.08373} }