144 research outputs found

    A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition

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    Multi-step ahead forecasting is still an open challenge in time series forecasting. Several approaches that deal with this complex problem have been proposed in the literature but an extensive comparison on a large number of tasks is still missing. This paper aims to fill this gap by reviewing existing strategies for multi-step ahead forecasting and comparing them in theoretical and practical terms. To attain such an objective, we performed a large scale comparison of these different strategies using a large experimental benchmark (namely the 111 series from the NN5 forecasting competition). In addition, we considered the effects of deseasonalization, input variable selection, and forecast combination on these strategies and on multi-step ahead forecasting at large. The following three findings appear to be consistently supported by the experimental results: Multiple-Output strategies are the best performing approaches, deseasonalization leads to uniformly improved forecast accuracy, and input selection is more effective when performed in conjunction with deseasonalization

    Combinations of time series forecasts: when and why are they beneficial?.

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    Time series forecasting has a long track record in many application areas. In forecasting research, it has been illustrated that finding an individual algorithm that works best for all possible scenarios is hopeless. Therefore, instead of striving to design a single superior algorithm, current research efforts have shifted towards gaining a deeper understanding of the reasons a forecasting method may perform well in some conditions whilst it may fail in others. This thesis provides a number of contributions to this matter. Traditional empirical evaluations are discussed from a novel point of view, questioning the benefit of using sophisticated forecasting methods without domain knowledge. An own empirical study focusing on relevant off-the shelf forecasting and forecast combination methods underlines the competitiveness of relatively simple methods in practical applications. Furthermore, meta-features of time series are extracted to automatically find and exploit a link between application specific data characteristics and forecasting performance using meta-learning. Finally, the approach of extending the set of input forecasts by diversifying functional approaches, parameter sets and data aggregation level used for learning is discussed, relating characteristics of the resulting forecasts to different error decompositions for both individual methods and combinations. Advanced combination structures are investigated in order to take advantage of the knowledge on the forecast generation processes. Forecasting is a crucial factor in airline revenue management; forecasting of the anticipated booking, cancellation and no-show numbers has a direct impact on general planning of routes and schedules, capacity control for fareclasses and overbooking limits. In a collaboration with Lufthansa Systems in Berlin, experiments in the thesis are conducted on an airline data set with the objective of improving the current net booking forecast by modifying one of its components, the cancellation forecast. To also compare results achieved of the methods investigated here with the current state-of-the-art in forecasting research, some experiments also use data sets of two recent forecasting competitions, thus being able to provide a link between academic research and industrial practice

    Meta-learning for time series forecasting and forecast combination

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    In research of time series forecasting, a lot of uncertainty is still related to the task of selecting an appropriate forecasting method for a problem. It is not only the individual algorithms that are available in great quantities; combination approaches have been equally popular in the last decades. Alone the question of whether to choose the most promising individual method or a combination is not straightforward to answer. Usually, expert knowledge is needed to make an informed decision, however, in many cases this is not feasible due to lack of resources like time, money and manpower. This work identifies an extensive feature pool describing both the time series and the ensemble of individual forecasting methods. The applicability of different meta-learning approaches are investigated, first to gain knowledge on which model works best in which situation, later to improve forecasting performance. Results show the superiority of a ranking-based combination of methods over simple model selection approaches

    Combinations of time series forecasts : when and why are they beneficial?

    Get PDF
    Time series forecasting has a long track record in many application areas. In forecasting research, it has been illustrated that finding an individual algorithm that works best for all possible scenarios is hopeless. Therefore, instead of striving to design a single superior algorithm, current research efforts have shifted towards gaining a deeper understanding of the reasons a forecasting method may perform well in some conditions whilst it may fail in others. This thesis provides a number of contributions to this matter. Traditional empirical evaluations are discussed from a novel point of view, questioning the benefit of using sophisticated forecasting methods without domain knowledge. An own empirical study focusing on relevant off-the shelf forecasting and forecast combination methods underlines the competitiveness of relatively simple methods in practical applications. Furthermore, meta-features of time series are extracted to automatically find and exploit a link between application specific data characteristics and forecasting performance using meta-learning. Finally, the approach of extending the set of input forecasts by diversifying functional approaches, parameter sets and data aggregation level used for learning is discussed, relating characteristics of the resulting forecasts to different error decompositions for both individual methods and combinations. Advanced combination structures are investigated in order to take advantage of the knowledge on the forecast generation processes. Forecasting is a crucial factor in airline revenue management; forecasting of the anticipated booking, cancellation and no-show numbers has a direct impact on general planning of routes and schedules, capacity control for fareclasses and overbooking limits. In a collaboration with Lufthansa Systems in Berlin, experiments in the thesis are conducted on an airline data set with the objective of improving the current net booking forecast by modifying one of its components, the cancellation forecast. To also compare results achieved of the methods investigated here with the current state-of-the-art in forecasting research, some experiments also use data sets of two recent forecasting competitions, thus being able to provide a link between academic research and industrial practice.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
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