Prediction regions for interval‐valued time series

González‐Rivera, Gloria; Luo, Yi; Ruiz, Esther

doi:10.1002/jae.2754

Cited by 10 publications

(2 citation statements)

References 49 publications

(48 reference statements)

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“…To efficiently use the potential information contained in interval data, Han and Wang (2012), Han et al (2016), Wang et al (2016), Sun, Han, et al (2018), Sun, Zhang, et al (2019), Sun et al (2021), Sun, Bao, et al (2020) and He et al (2021) established a set‐based interval regression method. Wu and Perloff (2005), Arroyo et al (2010), González‐Rivera and Lin, (2013), Lin and González‐Rivera (2016), Gonzalez‐Rivera et al (2020), and Chang et al (2022) have proposed inference methods for interval‐valued regression to analyze the interval distribution of random variables. González‐Rivera and Arroyo (2012); Golan and Ullah (2017); Buansing et al (2020); Guo et al (2021); and Hao et al (2022) developed information‐theoretical methods for interval analysis.…”

Section: Resultsmentioning

confidence: 99%

Interval time series forecasting: A systematic literature review

Wang,

Gurmani,

Tao

et al. 2023

Journal of Forecasting

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Interval time series forecasting can be used for forecasting special symbolic data comprising lower and upper bounds and plays an important role in handling the complexity, instability, and uncertainty of observed objects. The purpose of this research is to identify the most widely used definition of interval time series; classify existing research into mature research, current research focus, and research gaps within the defined framework; and recommend future directions for interval forecasting research. To achieve this goal, we have conducted a systematic literature review, comprising search strategy planning, screening mechanism determination, document analysis, and report generation. During the search strategy planning stage, eight literature search libraries are selected to obtain the most extensive studies (total of 525 targets). In the screening‐mechanism determination stage, through the inclusion and exclusion mechanism, the literature that is repetitive, of low‐relevance, and from other fields are discarded, and 125 studies are finally selected. In the document analysis stage, tag‐based methods and classification grids are selected to analyze the shortlisted studies. The results show that there are still numerous research gaps in interval time series forecasting, such as the establishment of hybrid models, application of multisource information, development and application of evaluation techniques, and expansion of application scenarios. In the report‐generation stage, the problems that have been solved and encountered in interval forecasting are summarized, and future research directions are proposed. Finally, the most significant contribution of this research is to provide an overview of interval time series forecasting for easy reference by researchers and to facilitate further research.

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Section: Resultsmentioning

confidence: 99%

Interval time series forecasting: A systematic literature review

Wang,

Gurmani,

Tao

et al. 2023

Journal of Forecasting

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“…Finally, defining the running level process as

T_{t} = (M_{t} + M_{t}^{-}) / 2

, one could think of modelling the bivariate time series

(T_{t}, R_{t})

as in Gonzalez‐Rivera et al . (2020)…”

Section: Discussionmentioning

confidence: 99%

Peaks, gaps, and time‐reversibility of economic time series

Proietti

2022

Journal Time Series Analysis

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By locating the running maxima and minima of a time series, and measuring the current deviation from them, it is possible to generate processes that are relevant for the analysis of the business cycle and for characterizing bull and bear phases in financial markets. First, the measurement of the time distance from the running peak originates a first-order Markov chain, whose characteristics can be used for testing time-reversibility of economic dynamics and specific types of asymmetries in financial markets. Second, the gap processes can be combined to provide a non-parametric measure of the growth cycle. The article derives the time series properties of the gap process and other related processes that arise from the same measurement context, and proposes new non-parametric tests of time-reversibility and new measures of the output gap.

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