An Event-Based Predictive Modelling Approach: An Application in Macroeconomics

Núñez, Marlon; Fidalgo-Merino, Raul; Morales, Rafael

doi:10.1109/smc.2018.00206

Cited by 2 publications

(3 citation statements)

References 21 publications

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“…In this section, we discuss the experimental results on real world financial time series. In the first part, we examine the use of the proposed methods from an alternative perspective other than tracking error; specifically, we examine the impact of our proposed method from the lens of time to event (TTE), which is critical in certain financial applications [50], [51]. In the second part, we discuss the practical issues, such as the performance of the proposed methods on different types of time series and under different lengths of the forecast horizon.…”

Section: Resultsmentioning

confidence: 99%

“…As an addition to the traditional econometric approaches, alternative methods [46]- [48] have been proposed to achieve robust parameter estimation, where vector auto-regressive models [45], [48], [49] suffer from an out-of-sample prediction performance in the mid-long horizon. The intrinsic problem related to such application is that directional guidance and timing become more important issues than tracking error [50], [51]; therefore, alternative techniques that can leverage alternative utility functions of the forecast values should be highlighted [52].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Learning to Rank for Multi-Step Ahead Time-Series Forecasting

Duan¹,

Kashima

2021

IEEE Access

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Time-series forecasting is a fundamental problem associated with a wide range of engineering, financial, and social applications. The challenge arises from the complexity due to the timevariant property of time series and the inevitable diminishing utility of predictive models. Therefore, it is generally difficult to accurately predict values, especially in a multi-step ahead setting. However, in domains such as financial time series forecasting, an ex-ante prediction of the relative order of values in the near future is sufficient; i.e., the next 100 days can help make meaningful investment decisions. In this paper, we propose a dynamic prediction framework that makes it possible to make an ex-ante forecast of time series with a special focus on the relative ordering of the forecast within a forwardlooking time horizon. Through the lens of the concordance index (CI), we compare the proposed method with conventional regression-based time-series forecasting methods, discriminative learning methods and hybrid methods. Moreover, we discuss the use of the proposed framework for different types of time series and under a variety of conditions. Extensive experimental results on financial time series across a majority of liquid asset classes show that the proposed framework outperforms the benchmark methods significantly.INDEX TERMS ranking estimation, time series, multi-step forecasting, concordance index, asset pricing, investment strategy.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning to Rank for Multi-Step Ahead Time-Series Forecasting

Duan¹,

Kashima

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…As an addition to the traditional econometric approaches, alternative methods [46]- [48] have been proposed to achieve robust parameter estimation, where vector auto-regressive models [45], [48], [49] suffer from an outof-sample prediction performance in the mid-long horizon. VOLUME 9, 2021 The intrinsic problem related to such application is that directional guidance and timing become more important issues than tracking error [50], [51]; therefore, alternative techniques that can leverage alternative utility functions of the forecast values should be highlighted [52].…”

Section: Introductionmentioning

confidence: 99%

Multi-step Forecasting with Large Vector Autoregressions

Pick¹,

Carpay²

2022

Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling

View full text Add to dashboard Cite

Time-series forecasting is a fundamental problem associated with a wide range of engineering, financial, and social applications. The challenge arises from the complexity due to the time-variant property of time series and the inevitable diminishing utility of predictive models. Therefore, it is generally difficult to accurately predict values, especially in a multi-step ahead setting. However, in domains such as financial time series forecasting, an ex-ante prediction of the relative order of values in the near future is sufficient; i.e., the next 100 days can help make meaningful investment decisions. In this paper, we propose a dynamic prediction framework that makes it possible to make an ex-ante forecast of time series with a special focus on the relative ordering of the forecast within a forward-looking time horizon. Through the lens of the concordance index (CI), we compare the proposed method with conventional regression-based time-series forecasting methods, discriminative learning methods and hybrid methods. Moreover, we discuss the use of the proposed framework for different types of time series and under a variety of conditions. Extensive experimental results on financial time series across a majority of liquid asset classes show that the proposed framework outperforms the benchmark methods significantly.

show abstract

An Event-Based Predictive Modelling Approach: An Application in Macroeconomics

Cited by 2 publications

References 21 publications

Learning to Rank for Multi-Step Ahead Time-Series Forecasting

Learning to Rank for Multi-Step Ahead Time-Series Forecasting

Multi-step Forecasting with Large Vector Autoregressions

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