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Combining Interval Time Series Forecasts. A First Step in a Long Way (Research Agenda)
La combinación de predicciones de series temporales de intervalo (STI)
DOI:
https://doi.org/10.15446/rce.v44n1.85116Keywords:
efficient market hypothesis, equal weights, financial markets, forecast combination, optimal weight, random walk model (en)hipótesis de mercado eficiente, ponderaciones iguales, mercados financieros, combinación de pronósticos, peso óptimo, modelo de caminata aleatoria (es)
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We observe every day a world more complex, uncertain, and riskier than the world of yesterday. Consequently, having accurate forecasts in economics, finance, energy, health, tourism, and so on; is more critical than ever. Moreover, there is an increasing requirement to provide other types of forecasts beyond point ones such as interval forecasts. After more than 50 years of research, there are two consensuses, “combining forecasts reduces the final forecasting error” and “a simple average of several forecasts often outperforms complicated weighting schemes”, which was named “forecast combination puzzle (FCP)”. The introduction of intervalvalued time series (ITS) concepts and several forecasting methods has been proposed in different papers and gives answers to some big data challenges. Hence, one main issue is how to combine several forecasts obtained for one ITS. This paper proposes some combination schemes with a couple or various ITS forecasts. Some of them extend previous crisp combination schemes incorporating as a novelty the use of Theil’s U. The FCP under the ITS forecasts framework will be analyzed in the context of different accuracy measures and some guidelines will be provided. An agenda for future research in the field of combining forecasts obtained for ITS will be outlined.
Cada día observamos un mundo más complejo, incierto y con mayor riesgo que el mundo de ayer. Luego, tener pronósticos precisos en economía,
finanzas, energía, salud, turismo, etc.; es más crítico que nunca. Además, existe un requisito creciente de proporcionar otro tipo de pronósticos más
allá de los puntuales, como los pronósticos de intervalos. Después de másde 50 años de investigación, hay dos consensos, “combinar pronósticos reduce el error de pronóstico final” y “un promedio simple de varios pronósticos a menudo supera complicados esquemas de ponderación”, que
se denominó “rompecabezas de combinación de pronósticos (FCP)”. La introducción de los conceptos de series de tiempo de intervalo (ITS) y varios métodos de pronóstico se han propuesto y dan respuestas a algunos desafíos de los grandes datos. Entonces, un problema es cómo combinar varios pronósticos obtenidos para una ITS. Este documento propone algunos esquemas combinados con un par o varios pronósticos ITS. Algunos extienden esquemas previos para datos puntuales, incorporando como novedad la U de Theil. El FCP en el marco de pronósticos ITS se analizará con diferentes medidas de exactitud y se proporcionarán algunas pautas. Se describirá una agenda para futuras investigaciones en la combinación de pronósticos obtenidos para ITS.
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1. Carlos Maté, Lucía Jimeńez. (2021). Forecasting exchange rates with the iMLP: New empirical insight on one multi-layer perceptron for interval time series (ITS). Engineering Applications of Artificial Intelligence, 104, p.104358. https://doi.org/10.1016/j.engappai.2021.104358.
2. Carlos G. Maté. (2022). Forecasting in FOREX the spot price interval of tomorrow with the same information of today. An analysis of the seven majors using a linear regression model based on interval arithmetic. Knowledge-Based Systems, 258, p.109923. https://doi.org/10.1016/j.knosys.2022.109923.
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