Association rules and multiple variables in complex times series forecasting
- Authors: Bertoli, Marcello , Stranieri, Andrew , Banerjee, Arunava
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at the First International Workshop on Intelligent Finance, IWIF1, Melbourne : 13th December, 2004
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000847
Forecasting on complex datasets with association rules
- Authors: Bertoli, Marcello , Stranieri, Andrew
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at Knowledge-Based Intelligent Information & Engineering Systems: 8th International Conference, KES 2004, Proceedings, Part I, Wellington, New Zealand : 21st September, 2004
- Full Text: false
- Reviewed:
- Description: Forecasting in complex fields such as financial markets or national economies is made difficult by the impact of numerous variables with unknown inter-dependencies. A forecasting approach is presented that produces forecasts on a variable based on past values for that variable and other, possibly inter-dependent variables. The approach is based on the intuition that the next value in a series depends on the last value and the last two values and the last three values and so on. Furthermore, the next value depends also on past values on other variables. No assumptions about the form of functions underpinning a dataset are made. Rather, evidence for each possible next value is collected by combining confidence values of numerous association rules. The approach has been evaluated by forecasting values in a hypothetical dataset and by forecasting the direction of the Australian stock market index with favorable results.
- Description: E1
- Description: 2003000849
Automatic sleep stage identification: difficulties and possible solutions
- Authors: Sukhorukova, Nadezda , Stranieri, Andrew , Ofoghi, Bahadorreza , Vamplew, Peter , Saleem, Muhammad Saad , Ma, Liping , Ugon, Adrien , Ugon, Julien , Muecke, Nial , Amiel, Hélène , Philippe, Carole , Bani-Mustafa, Ahmed , Huda, Shamsul , Bertoli, Marcello , Levy, P , Ganascia, J.G
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: The diagnosis of many sleep disorders is a labour intensive task that involves the specialised interpretation of numerous signals including brain wave, breath and heart rate captured in overnight polysomnogram sessions. The automation of diagnoses is challenging for data mining algorithms because the data sets are extremely large and noisy, the signals are complex and specialist's analyses vary. This work reports on the adaptation of approaches from four fields; neural networks, mathematical optimisation, financial forecasting and frequency domain analysis to the problem of automatically determing a patient's stage of sleep. Results, though preliminary, are promising and indicate that combined approaches may prove more fruitful than the reliance on a approach.