Destination prediction has attracted widespread attention because it can help vehicle-aid systems recommend related services in advance to improve user driving experience.However,the relevant research is mainly based ...Destination prediction has attracted widespread attention because it can help vehicle-aid systems recommend related services in advance to improve user driving experience.However,the relevant research is mainly based on driving trajectory of vehicles to predict the destinations,which is challenging to achieve the early destination prediction.To this end,we propose a model of early destination prediction,DP-BPR,to predict the destinations by users’travel time and locations.There are three challenges to accomplish the model:1)the extremely sparse historical data make it challenge to predict destinations directly from raw historical data;2)the destinations are related to not only departure points but also departure time so that both of them should be taken into consideration in prediction;3)how to learn destination preferences from historical data.To deal with these challenges,we map sparse high-dimensional data to a dense low-dimensional space through embedding learning using deep neural networks.We learn the embeddings not only for users but also for locations and time under the supervision of historical data,and then use Bayesian personalized ranking(BPR)to learn to rank destinations.Experimental results on the Zebra dataset show the effectiveness of DP-BPR.展开更多
The Myers Briggs Type Indicator （MBTI） test has been widely used in schools and career placement organizations to counsel individuals into compatible career choices. The test has also been utilized in academia to en...The Myers Briggs Type Indicator （MBTI） test has been widely used in schools and career placement organizations to counsel individuals into compatible career choices. The test has also been utilized in academia to enhance instructor＇s knowledge of the different learning styles and thus allows them to develop strategies to increase students＇ learning. The test is a forced-choice self-reporting exam comprised of 126 questions. Based on Jung＇s theory of personality type, the test seeks to categorize personality types into 16 discrete groups based on the four preference poles （Myers, 1962）. The poles are based on the preference for： （1） introversion （I） or extroversion （E）; （2） sensing （S） or intuition （N）; （3） thinking （T） or feeling （F）; and （4）judging （J） or perception （P）. Laribee （1994） studied American accounting students and found that certain personality traits were over represented in upper-level accounting courses, while Macdaid, McCaulley, and Kainz （1986） found that the same personality trait groups were over-represented in the profession. Oswick and Barber （1998）, however, found no significant relationship between the grade earned in an introductory accounting course and the personality traits as identified by the MBTI with 344 UK-based accounting students. This study investigates the relationship between a student＇s academic success in a financial accounting principles course and the MBTI personality type indicators. The type distribution of 59 historically black colleges and universities＇ （HBCU） business administration majors was analyzed and separated into two groups. The groups were then tested to determine if there was a significant difference in the mean grade of the groups in accounting principles.展开更多
There is still an obstacle to prevent neural network from wider and more effective applications, i.e., the lack of effective theories of models identification. Based on information theory and its generalization, this ...There is still an obstacle to prevent neural network from wider and more effective applications, i.e., the lack of effective theories of models identification. Based on information theory and its generalization, this paper introduces a universal method to achieve nonlinear models identification. Two key quantities, which are called nonlinear irreducible auto-correlation (NIAC) and generalized nonlinear irreducible auto-correlation (GNIAC), are defined and discussed. NIAC and GNIAC correspond with intrinstic irreducible auto-(dependency) (IAD) and generalized irreducible auto-(dependency) (GIAD) of time series respectively. By investigating the evolving trend of NIAC and GNIAC, the optimal auto-regressive order of nonlinear auto-regressive models could be determined naturally. Subsequently, an efficient algorithm computing NIAC and GNIAC is discussed. Experiments on simulating data sets and typical nonlinear prediction models indicate remarkable correlation between optimal auto-regressive order and the highest order that NIAC-GNIAC have a remarkable non-zero value, therefore demonstrate the validity of the proposal in this paper.展开更多
Reliable forecasts of the price of oil are of interest for a wide range of applications. For example, central banks and private sector forecasters view the price of oil as one of the key variables in generating macroe...Reliable forecasts of the price of oil are of interest for a wide range of applications. For example, central banks and private sector forecasters view the price of oil as one of the key variables in generating macroeconomic projections and in assessing macroeconomic risks. Of particular interest is the question of the extent to which the price of oil is helpful in predicting recessions. This paper presents a statistical learning method （SLM） based on combined fuzzy system （FS）, artificial neural network （ANN）, and support vector regression （SVR） to cope with optimum long-term oil price forecasting in noisy, uncertain, and complex environments. A number of quantitative factors were discovered from this model and used as the input. For verification and testing, the West Texas Intermediate （WT1） crude oil spot price is used to test the effectiveness of the proposed learning methodology. Empirical results reveal that the proposed SLM-based forecasting can model the nonlinear relationship between the input variables and price very well. Furthermore, in-sample and out-of-sample prediction performance also demonstrates that the proposed SLM model can produce more accurate prediction results than other nonlinear models.展开更多
基金Project(2018YFF0214706)supported by the National Key Research and Development Program of ChinaProject(cstc2020jcyj-msxmX0690)supported by the Natural Science Foundation of Chongqing,China+1 种基金Project(2020CDJ-LHZZ-039)supported by the Fundamental Research Funds for the Central Universities of Chongqing,ChinaProject(cstc2019jscx-fxydX0012)supported by the Key Research Program of Chongqing Technology Innovation and Application Development,China。
文摘Destination prediction has attracted widespread attention because it can help vehicle-aid systems recommend related services in advance to improve user driving experience.However,the relevant research is mainly based on driving trajectory of vehicles to predict the destinations,which is challenging to achieve the early destination prediction.To this end,we propose a model of early destination prediction,DP-BPR,to predict the destinations by users’travel time and locations.There are three challenges to accomplish the model:1)the extremely sparse historical data make it challenge to predict destinations directly from raw historical data;2)the destinations are related to not only departure points but also departure time so that both of them should be taken into consideration in prediction;3)how to learn destination preferences from historical data.To deal with these challenges,we map sparse high-dimensional data to a dense low-dimensional space through embedding learning using deep neural networks.We learn the embeddings not only for users but also for locations and time under the supervision of historical data,and then use Bayesian personalized ranking(BPR)to learn to rank destinations.Experimental results on the Zebra dataset show the effectiveness of DP-BPR.
文摘The Myers Briggs Type Indicator （MBTI） test has been widely used in schools and career placement organizations to counsel individuals into compatible career choices. The test has also been utilized in academia to enhance instructor＇s knowledge of the different learning styles and thus allows them to develop strategies to increase students＇ learning. The test is a forced-choice self-reporting exam comprised of 126 questions. Based on Jung＇s theory of personality type, the test seeks to categorize personality types into 16 discrete groups based on the four preference poles （Myers, 1962）. The poles are based on the preference for： （1） introversion （I） or extroversion （E）; （2） sensing （S） or intuition （N）; （3） thinking （T） or feeling （F）; and （4）judging （J） or perception （P）. Laribee （1994） studied American accounting students and found that certain personality traits were over represented in upper-level accounting courses, while Macdaid, McCaulley, and Kainz （1986） found that the same personality trait groups were over-represented in the profession. Oswick and Barber （1998）, however, found no significant relationship between the grade earned in an introductory accounting course and the personality traits as identified by the MBTI with 344 UK-based accounting students. This study investigates the relationship between a student＇s academic success in a financial accounting principles course and the MBTI personality type indicators. The type distribution of 59 historically black colleges and universities＇ （HBCU） business administration majors was analyzed and separated into two groups. The groups were then tested to determine if there was a significant difference in the mean grade of the groups in accounting principles.
文摘There is still an obstacle to prevent neural network from wider and more effective applications, i.e., the lack of effective theories of models identification. Based on information theory and its generalization, this paper introduces a universal method to achieve nonlinear models identification. Two key quantities, which are called nonlinear irreducible auto-correlation (NIAC) and generalized nonlinear irreducible auto-correlation (GNIAC), are defined and discussed. NIAC and GNIAC correspond with intrinstic irreducible auto-(dependency) (IAD) and generalized irreducible auto-(dependency) (GIAD) of time series respectively. By investigating the evolving trend of NIAC and GNIAC, the optimal auto-regressive order of nonlinear auto-regressive models could be determined naturally. Subsequently, an efficient algorithm computing NIAC and GNIAC is discussed. Experiments on simulating data sets and typical nonlinear prediction models indicate remarkable correlation between optimal auto-regressive order and the highest order that NIAC-GNIAC have a remarkable non-zero value, therefore demonstrate the validity of the proposal in this paper.
文摘Reliable forecasts of the price of oil are of interest for a wide range of applications. For example, central banks and private sector forecasters view the price of oil as one of the key variables in generating macroeconomic projections and in assessing macroeconomic risks. Of particular interest is the question of the extent to which the price of oil is helpful in predicting recessions. This paper presents a statistical learning method （SLM） based on combined fuzzy system （FS）, artificial neural network （ANN）, and support vector regression （SVR） to cope with optimum long-term oil price forecasting in noisy, uncertain, and complex environments. A number of quantitative factors were discovered from this model and used as the input. For verification and testing, the West Texas Intermediate （WT1） crude oil spot price is used to test the effectiveness of the proposed learning methodology. Empirical results reveal that the proposed SLM-based forecasting can model the nonlinear relationship between the input variables and price very well. Furthermore, in-sample and out-of-sample prediction performance also demonstrates that the proposed SLM model can produce more accurate prediction results than other nonlinear models.
