Air temperature and the incidence of fall-related hip fracture hospitalisations in older people
- Authors: Turner, R. M. , Hayen, Andrew , Dunsmuir, William , Finch, Caroline
- Date: 2011
- Type: Text , Journal article
- Relation: Osteoporosis International Vol. 22, no. 4 (2011), p. 1183-1189
- Relation: http://purl.org/au-research/grants/nhmrc/565900
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- Description: Observation-driven Poisson regression models were used to investigate mean daily air temperature and fall-related hip fracture hospitalisations. After adjustment for season, day-of-week effects, long-term trend and autocorrelation, hip fracture rates are higher in both males and females aged 75+ years when there is a lower air temperature. This study investigated whether there was an association between fall-related hip fracture hospitalisations and air temperature at a day-to-day level, after accounting for seasonal trend and autocorrelation. Observation-driven Poisson regression models were used to investigate mean daily air temperature and fall-related hip fracture hospitalisations for the period 1 July 1998 to 31 December 2004, inclusive, in the Sydney region of New South Wales, Australia, which has a population of 4 million people. Lower daily air temperature was significantly associated with higher fall-related hip fracture hospitalisations in 75+-year-olds: men aged 75-84 years, rate ratio (RR) for a 1A degrees C increase in temperature of 0.98 with 95% confidence interval (0.96, 0.99), men 85+ years RR = 0.98 (0.96, 1.00), women 75-84 years RR = 0.99 (0.98, 1.00), women 85+ years RR = 0.98 (0.97, 0.99). Moreover, there were fewer hospitalisations on weekends compared to weekdays ranging from RR = 0.81 (0.73, 0.90) in women aged 65-74 years to RR = 0.89 (0.80, 0.98) in men aged 85+ years. After adjustment for season, day-of-week effects, long-term trend and autocorrelation, fall-related hip fracture hospitalisation rates are higher in both males and females aged 75+ years when there is a lower air temperature.
Forecasting model for crude oil prices based on artificial neural networks
- Authors: Haidar, Imad , Kulkarni, Siddhivinayak , Pan, Heping
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2008, Sydney, New South Wales : 15th-18th December 2008 p. 103-108
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- Description: This paper presents short-term forecasting model for crude oil prices based on three layer feedforward neural network. Careful attention was paid on finding the optimal network structure. Moreover, a number of features were tested as an inputs such as crude oil futures prices, dollar index, gold spot price, heating oil spot price and S&P 500 index. The results show that with adequate network design and appropriate selection of the training inputs, feedforward networks are capable of forecasting noisy time series with high accuracy.
- Description: 2003006659
PU-shapelets : Towards pattern-based positive unlabeled classification of time series
- Authors: Liang, Shen , Zhang, Yanchun , Ma, Jiangang
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019; Chiang Mai, Thailand; 22nd-25th April 2019; part of the Lecture Notes in Computer Science book series, also part of the Information Systems and Applications, incl. Internet/Web and HCI sub series Vol. 11446 LNCS, p. 87-103
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- Description: Real-world time series classification applications often involve positive unlabeled (PU) training data, where there are only a small set PL of positive labeled examples and a large set U of unlabeled ones. Most existing time series PU classification methods utilize all readings in the time series, making them sensitive to non-characteristic readings. Characteristic patterns named shapelets present a promising solution to this problem, yet discovering shapelets under PU settings is not easy. In this paper, we take on the challenging task of shapelet discovery with PU data. We propose a novel pattern ensemble technique utilizing both characteristic and non-characteristic patterns to rank U examples by their possibilities of being positive. We also present a novel stopping criterion to estimate the number of positive examples in U. These enable us to effectively label all U training examples and conduct supervised shapelet discovery. The shapelets are then used to build a one-nearest-neighbor classifier for online classification. Extensive experiments demonstrate the effectiveness of our method.
- Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Does an aging population influence stock markets? Evidence from New Zealand
- Authors: Hettihewa, Samanthala , Saha, Shrabani , Zhang, Hanxiong
- Date: 2018
- Type: Text , Journal article
- Relation: Economic Modelling Vol. 75, no. (2018), p. 142-158
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- Description: The effect of the aging baby-boom-cohort on asset values is extensively studied. While that effect varies by country, there are likely to be commonalities. Thus, research on a relatively small advanced open economy like New Zealand can provide insight into the general effect. In this study monthly data from 1991 to 2017 is used to examine how aging population in New Zealand affects its stock market considering key demographic and non-demographic macroeconomic variables and a new focus on fast-and-slow-moving institutional change. The results suggest that the net effect of an aging population on stock markets is insignificant. However, real GDP and foreign portfolio investment (FPI) show a positive relationship with the stock market. The findings reveal that FPI can mitigate possible negative effects from aging in an open economy. Moreover, the policy implications of the study suggest that international-factor mobility, skilled-migration policies, and technology-based productivity growth can boost stock markets.