منابع مشابه
Forecasting Using Consistent Experts
Combining forecasts from different models has shown to perform better than single forecasts in most times series. In this paper new techniques for combining a large number of forecasting models in order to achieve better forecasting performance are introduced. This class of new techniques for combining is based on using consistent experts for forecasting.
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– We study the effect of decomposing a series into multiple components and performing forecasts on each component separately. The focus here is on sales data-most of the series considered display both seasonality and trend. Hence the original series is decomposed into trend, seasonality and an irregular component. Multiple forecasting 'experts' are used to forecast each component series. These ...
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We present a framework for predicting the conditional distributions of future observations that is well suited for skewed, fat-tailed, and multi-modal time series. This framework allows to address questions about the nature of an observed time series, such as: Are there discrete subprocesses underlying the observed data? If so, do they exhibit a hidden Markov structure, or are they better descr...
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Judgmental bootstrapping is a type of expert system. It translates an experts' rules into a quantitative model by regressing the experts' forecasts against the information that he used. Bootstrapping models apply an experts' rules consistently, and many studies have shown that decisions and predictions from bootstrapping models are similar to those from the experts. Three studies showed that bo...
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Location getLocation(int timeout)Description: Retrieves a Location with the constraints given by the Crite-ria associated with this class. If no result could be retrieved,a LocationException is thrown. If the location can't be deter-mined within the timeout period specified in the parameter,the method shall throw a LocationException. If the provider is temporarily unavailabl...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 2018
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-018-05774-y