Reducing the blunders in forecasting
Abstract Forecasting is going through a crisis, partly attributable to its failure to anticipate so many developments (including the present recession). Specific problems that need tackling by the research community include the tendency to exclude the less quantifiable factors; the difficulties of identifying, relating and foreseeing explanatory variables; assessing degrees of uncertainty; and making the best use of consultants. Prospective analysis, in which forecasts are generated in the light of scenarios, can assist those concerned with actions and the future.
Long-term metals forecasting: The track record: 1910–1964☆
Abstract In 90 forecasts published in journals, about 70% of the individual predictions proved correct; partly because they were fairly vague. However, two major reports dealing with long-term metals forecasting were much less accurate. Unexpectedly high rates of economic growth and technological change (since 1952) contributed to the Paley Commission's inaccuracy; whereas the failure of expected developments to occur (after 1964) upset the predictions of Resources in America's Future .
Early Stage Diagnosis of Parkinson’s Disease Using HOS Features of EEG Signals
Parkinson’s disease (PD) is a common neurodegenerative disease that causes involuntary muscle movements and tremor among other symptoms. One approach to diagnosing this disease is by analyzing the electroencephalography (EEG) signals of the patients. However, due to the complexity of this type of signal, more advanced feature extraction and classification methods are required. The goal of this study is to combine six well-known features in EEG analysis with eight higher order statistical features and use them for classification of early stage PD (1st and 2nd stage) from a healthy control group. After extracting the required features, eight classifiers are employed to classify the signals.