Criteria for classifying forecasting methods

Januschowski, Tim; Gasthaus, Jan; Wang, Yuyang; Salinas, David; Flunkert, Valentín; Bohlke-Schneider, Michael; Callot, Laurent

doi:10.1016/j.ijforecast.2019.05.008

Cited by 139 publications

(95 citation statements)

References 36 publications

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“…Previous literature on comparing the education effects of multiple schools has shown that incorporating the nested structure of the state, school, and class in the model had substantial improvement in terms of accuracy and interpretability. 17 Januschowski et al 18 have classified methods in the predicting domain into two: global and local. Global methods jointly learn parameters using all available time series while the local methods learn independently from each time series.…”

Section: Hierarchical Modelmentioning

confidence: 99%

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Hierarchical spline for time series prediction: An application to naval ship engine failure rate

Moon

Choi

2021

Applied AI Letters

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Predicting equipment failure is important because it could improve availability and cut down the operating budget. Previous literature has attempted to model failure rate with bathtub-formed function, Weibull distribution, Bayesian network, or analytic hierarchy process. But these models perform well with a sufficient amount of data and could not incorporate the two salient characteristics: imbalanced category and sharing structure. Hierarchical model has the advantage of partial pooling. The proposed model is based on Bayesian hierarchical B-spline. Time series of the failure rate of 99 Republic of Korea Naval ships are modeled hierarchically, where each layer corresponds to ship engine, engine type, and engine archetype. As a result of the analysis, the suggested model predicted the failure rate of an entire lifetime accurately in multiple situational conditions, such as prior knowledge of the engine.

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Section: Hierarchical Modelmentioning

confidence: 99%

“…Januschowski et al 18 have classified methods in the predicting domain into two: global and local. Global methods jointly learn parameters using all available time series while the local methods learn independently from each time series.…”

Section: Literature Reviewmentioning

confidence: 99%

Hierarchical spline for time series prediction: An application to naval ship engine failure rate

Moon

Choi

2021

Applied AI Letters

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“…There is also a vast literature on the use of deep learning in the context of time series forecasting [29,6,27,5]. Although it is fairly straightforward to use classic MLP ANN on large data sets, its use on medium-sized time series is more difficult due to the high risk of overfitting.…”

Section: Deep Machine Learningmentioning

confidence: 99%

Data Science Technologies in Economics and Finance: A Gentle Walk-In

Barbaglia

Consoli

Manzan

et al. 2021

Data Science for Economics and Finance

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This chapter is an introduction to the use of data science technologies in the fields of economics and finance. The recent explosion in computation and information technology in the past decade has made available vast amounts of data in various domains, which has been referred to as Big Data. In economics and finance, in particular, tapping into these data brings research and business closer together, as data generated in ordinary economic activity can be used towards effective and personalized models. In this context, the recent use of data science technologies for economics and finance provides mutual benefits to both scientists and professionals, improving forecasting and nowcasting for several kinds of applications. This chapter introduces the subject through underlying technical challenges such as data handling and protection, modeling, integration, and interpretation. It also outlines some of the common issues in economic modeling with data science technologies and surveys the relevant big data management and analytics solutions, motivating the use of data science methods in economics and finance.

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“…Previous literature on comparing the education effects of multiple schools has shown that incorporating the nested structure of the state, school, and class in the model had substantial improvement in terms of accuracy and interpretability (Rubin, 1981). Januschowski et al (2020) has classified methods in the forecasting domain into two: global and local.…”

Section: Hierarchical Modelmentioning

confidence: 99%

Hierarchical spline for time series forecasting: An application to Naval ship engine failure rate

Moon¹,

Choi²

2020

Preprint

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Predicting equipment failure is important because it could improve availability and cut down the operating budget. Previous literature has attempted to model failure rate with bathtub-formed function, Weibull distribution, Bayesian network, or AHP. But these models perform well with a sufficient amount of data and could not incorporate the two salient characteristics; imbalanced category and sharing structure. Hierarchical model has the advantage of partial pooling. The proposed model is based on Bayesian hierarchical B-spline. Time series of the failure rate of 99 Republic of Korea Naval ships are modeled hierarchically, where each layer corresponds to ship engine, engine type, and engine archetype. As a result of the analysis, the suggested model predicted the failure rate of an entire lifetime accurately in multiple situational conditions, such as prior knowledge of the engine.

show abstract

Criteria for classifying forecasting methods

Cited by 139 publications

References 36 publications

Hierarchical spline for time series prediction: An application to naval ship engine failure rate

Hierarchical spline for time series prediction: An application to naval ship engine failure rate

Data Science Technologies in Economics and Finance: A Gentle Walk-In

Hierarchical spline for time series forecasting: An application to Naval ship engine failure rate

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