14229561
Time dependent directional profit model for financial time series forecasting
Goodness-of-fit is the most popular criterion for neural network time series forecasting. In the context of financial time series forecasting, we are not only concerned at how good the forecasts fit their targets, but we are more interested in profits. In order to increase the forecastability in terms of profit earning, we propose a profit based adjusted weight factor for backpropagation network training. Instead of using the traditional least squares error, we add a factor which contains the profit, direction, and time information to the error function. The results show that this new approach does improve the forecastability of neural network models, for the financial application domain.
6507492
Induction of Classification Rules by Granular Computing
A granular computing model is used for learning classification rules by considering the two basic issues: concept formation and concept relationships identification. A classification rule induction method is proposed. Instead of focusing on the selection of a suitable partition, i.e., a family of granules defined by values of an attribute, in each step, we concentrate on the selection of a single granule. This leads to finding a covering of the universe, which is more general than partition based methods. For the design of granule selection heuristics, several measures on granules are suggested.
62113571
Learned probabilistic image motion models for event detection in videos
We present new probabilistic motion models of interest for the detection of relevant dynamic contents (or events) in videos. We separately handle the dominant image motion assumed to be due to the camera motion and the residual image motion related to scene motion. These two motion components are then represented by different probabilistic models which are further recombined for the event detection task. The motion models associated to pre-identified classes of meaningful events are learned from a training set of video samples. The event detection scheme proceeds in two steps which exploit different kinds of information and allow us to progressively select the video segments of interest using maximum likelihood (ML) criteria. The efficiency of the proposed approach is demonstrated on sports videos.
41338736
An approach to identification of variances for radar tracking systems
An approach for the identification of the variances of the process noise W(k) and the measurement noise V(k) for radar tracking systems is presented. In the proposed algorithm, the variances are expressed as a linear combination of the covariances of the third-order difference of the measurement data. The variances can then be estimated by estimating the covariances of the third-order difference and solving the matrix equation. The two cases for the measurement noise being white and color are considered in this algorithm. Simulation results show that for both cases a steady estimation is reached after 20 samples.
108001
Cournot Equilibrium in Price-Capped Two-Settlement Electricity Markets
We compare two alternative mechanisms for capping prices in two-settlement electricity markets. With sufficient lead time and competitive entry opportunities, forward market prices are implicitly capped by competitive pressure of potential entry that will occur when forward prices rise above a certain level. Another more direct approach is to cap spot prices through regulatory intervention. In this paper we explore the implications of the two alternative mechanisms in a two settlement Cournot equilibrium framework. We formulate the market equilibrium as a stochastic equilibrium problem with equilibrium constraints (EPEC) capturing congestion effects, probabilistic contingencies and market power. As an illustrative test case we use the 53-bus Belgian electricity network with representative generator cost but hypothetical demand and ownership assumptions. When compared to two-settlement systems without price caps we find that either of the price capping alternatives results in reduced forward contracting. Furthermore the reduction in spot prices due to forward contracting is smaller.
8612227
Mixed-State Markov Random Fields for Motion Texture Modeling and Segmentation
The aim of this work is to model the apparent motion in image sequences depicting natural dynamic scenes. We adopt the mixed-state Markov random fields (MRF) models recently introduced to represent so-called motion textures. The approach consists in describing the spatial distribution of some motion measurements which exhibit mixed-state nature: a discrete component related to the absence of motion and a continuous part for measurements different from zero. We propose several significative extensions to this model. We define an original motion texture segmentation method which does not assume conditional independence of the observations for each texture and normalizing factors are properly handled. Results on real examples demonstrate the accuracy and efficiency of our method.