126 research outputs found
Approaches for multi-step density forecasts with application to aggregated wind power
The generation of multi-step density forecasts for non-Gaussian data mostly
relies on Monte Carlo simulations which are computationally intensive. Using
aggregated wind power in Ireland, we study two approaches of multi-step density
forecasts which can be obtained from simple iterations so that intensive
computations are avoided. In the first approach, we apply a logistic
transformation to normalize the data approximately and describe the transformed
data using ARIMA--GARCH models so that multi-step forecasts can be iterated
easily. In the second approach, we describe the forecast densities by truncated
normal distributions which are governed by two parameters, namely, the
conditional mean and conditional variance. We apply exponential smoothing
methods to forecast the two parameters simultaneously. Since the underlying
model of exponential smoothing is Gaussian, we are able to obtain multi-step
forecasts of the parameters by simple iterations and thus generate forecast
densities as truncated normal distributions. We generate forecasts for wind
power from 15 minutes to 24 hours ahead. Results show that the first approach
generates superior forecasts and slightly outperforms the second approach under
various proper scores. Nevertheless, the second approach is computationally
more efficient and gives more robust results under different lengths of
training data. It also provides an attractive alternative approach since one is
allowed to choose a particular parametric density for the forecasts, and is
valuable when there are no obvious transformations to normalize the data.Comment: Corrected version includes updated equation (18). Published in at
http://dx.doi.org/10.1214/09-AOAS320 the Annals of Applied Statistics
(http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics
(http://www.imstat.org
Open-source software for generating electrocardiogram signals
ECGSYN, a dynamical model that faithfully reproduces the main features of the
human electrocardiogram (ECG), including heart rate variability, RR intervals
and QT intervals is presented. Details of the underlying algorithm and an
open-source software implementation in Matlab, C and Java are described. An
example of how this model will facilitate comparisons of signal processing
techniques is provided.Comment: 10 pages, 5 figure
Review of "System Modeling in Cellular Biology: From Concepts to Nuts and Bolts" by Szallasi, Stelling and Periwal
"System Modeling in Cellular Biology: From Concepts to Nuts and Bolts" by Szallasi, Stelling and Periwal introduces the relevant concepts, terminology, and techniques of this field of science. It emphasises the modelling and computational challenges of taking a multidisciplinary approach to biology. This book provides a comprehensive introduction to systems biology and will form a valuable resource for students, teachers and researchers from both experimental and theoretical disciplines
The power of twitter on predicting box office revenues
Over the last few years there has been an extraordinary surge of social networking and microblogging services. Twitter is a social network that focuses on social and news media. The Twitter data stream allows access to tweets, timestamps and locations of users. This enables us to capture the trends and patterns of rapidly evolving worldwide events. We use the Twitter data stream for the prediction of consumer preferences in the movie industry and estimate how successful the movie will be in the first and second weekends since its release date. The study provides evidence to suggest that frequencies of contemporaneous tweets and a consensus measure of public sentiment are useful for predicting box-office revenues, implying that any publicity is good publicity in word-of-mouth (WOM) and online viral marketing. Sentiment analysis based on tweets suggests that more extreme sentiment has more impact, and that the more negative the tweets about a movie are, the higher its revenue will be, in contrast with the classic theory of diffusion in news media
Objective Classification of Rainfall in Northern Europe for Online Operation of Urban Water Systems Based on Clustering Techniques
This study evaluated methods for automated classification of rain events into groups of “high” and “low” spatial and temporal variability in offline and online situations. The applied classification techniques are fast and based on rainfall data only, and can thus be applied by, e.g., water system operators to change modes of control of their facilities. A k-means clustering technique was applied to group events retrospectively and was able to distinguish events with clearly different temporal and spatial correlation properties. For online applications, techniques based on k-means clustering and quadratic discriminant analysis both provided a fast and reliable identification of rain events of “high” variability, while the k-means provided the smallest number of rain events falsely identified as being of “high” variability (false hits). A simple classification method based on a threshold for the observed rainfall intensity yielded a large number of false hits and was thus outperformed by the other two methods
Stochastic power generation
International audienceOur path towards decarbonisation involves the large-scale use of renewable sources - the most prominent contributions being from wind and solar, followed by biomass - to gradually replace fossil fuels for energy production, mainly in the form of heat and electricity. By June 2012, cumulative installed wind power capacity worldwide had reached 254 GW and was still increasing rapidly gives an extensive introduction to various forms of renewable energy sources among our potential options for the future ..
A Comparative Study of the Magnitude, Frequency and Distribution of Intense Rainfall in the United Kingdom
During the 1960s, a study was made of the magnitude, frequency and distribution of intense rainfall over the UK, employing data from more than 120 daily-read rain gauges covering the period 1911 to 1960. Using the same methodology, that study was recently updated utilizing data for the period 1961 to 2006 for the same gauges, or from those nearby. This paper describes the techniques applied to ensure consistency of data and statistical modelling. It presents a comparison of patterns of extreme rainfalls for the two periods and discusses the changes that have taken place. Most noticeably, increases up to 20% have occurred in the north west of the country and in parts of East Anglia. There have also been changes in other areas, including decreases of the same magnitude over central England. The implications of these changes are considered
Can index based insurance reduce the vulnerability of farmers to weather?
An IGC study reveals that index insurance has the potential to reduce the vulnerability of farmers to weather. This is dependent on data quality and model accuracy, with the highest predictive capacity involving a combination of satellite datasets. Even so, variation in agricultural production remains a challenge. Furthermore, the insurance needs to be credible and reliable, and accompanied by substantial training, to ensure farmers have adequate knowledge to make informed decisions
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