Rita Palivonaite

Short-term time series forecasting based on the identification of skeleton algebraic sequences

A new short-term time series forecasting method based on the identification of skeleton algebraic sequences is proposed in this paper. The concept of the rank of the Hankel matrix is exploited to detect a base fragment of the time series. Particle swarm optimization and evolutionary algorithms are then used to remove the noise and identify the skeleton algebraic sequence. Numerical experiments with an artificially generated and a real-world time series are used to illustrate the functionality of the proposed method.

Short-term time series algebraic forecasting with internal smoothing

A new algebraic forecasting method with internal smoothing is proposed for short-term time series prediction. The concept of the H-rank of a sequence is exploited for the detection of a base algebraic fragment of the time series. Evolutionary algorithms are exploited for the identification of the set of corrections which are used to perturb the original time series. The proposed forecasting method is constructed to find a near-optimal balance between the variability of algebraic predictors and the smoothness of averaging methods. Numerical experiments with an artificially generated and real-world time series are used to illustrate the potential of the proposed method.

Short-term time series algebraic forecasting with mixed smoothing

Short-term time series algebraic prediction technique with mixed smoothing is presented in this paper. Evolutionary algorithms are employed for the identification of a near-optimal algebraic skeleton from the available data. Direct algebraic predictions are conciliated by internal errors of interpolation and external differences from the moving average. Computational experiments with real world time series are used to demonstrate the effectiveness of the proposed forecasting algorithm.

Algebraic segmentation of short nonstationary time series based on evolutionary prediction algorithms

Algebraic segmentation of short nonstationary time series is presented in this paper. The proposed algorithm is based on the algebraic one step-forward predictor which is used to identify a temporal near-optimal algebraic model of the real-world time series. A combinatorial algorithm is used to identify intervals where prediction errors are lower than a predefined level of acceptable accuracy. Special deterministic strategy is developed for the selection of this acceptable level of prediction accuracy and is individually determined for every time series. The nonparametric identification of quasistationary segments is performed without the employment of any statistical estimator. Several standard real-world time series are used to demonstrate the efficiency of the proposed technique.

Image hiding in time-averaged deformable moiré gratings

A new image hiding technique based on time-averaged moire fringes is proposed in this paper. The secret image is embedded into a single cover image which is constructed as a deformable stochastic moire grating. The secret image is leaked in the form of a time-averaged fringe when the cover image is deformed according to a predetermined periodic law of motion. The proposed image hiding approach opens new possibilities for the optical control of vibrating deformable structures.

Near-Optimal Moire Grating for Chaotic Dynamic Visual Cryptography

Image hiding based on chaotic oscillations and near-optimal moire gratings is presented in this paper. The secret image is embedded into a single cover image. The encrypted secret image appears in a form of time-averaged moire fringes when the cover image is oscillated in a predefined direction, according to a chaotic law of motion. The criterion of the optimality of a moire grating is based on the absolute difference between the standard deviation of time-averaged images of near-optimal moire gratings in the background and in the zones associated to the secret image. Genetic algorithms are used for the identification of a nearoptimal set of moire gratings for image hiding applications. Numerical experiments are used to illustrate the functionality of the method.

Algebraic Level-Set Approach for the Segmentation of Financial Time Series

Adaptive algebraic level-set segmentation algorithm of financial time series is presented in this paper. The proposed algorithm is based on the algebraic one step-forward predictor with internal smoothing, which is used to identify a near optimal algebraic model. Particle swarm optimization algorithm is exploited for the detection of a base algebraic fragment of the time series. A combinatorial algorithm is used to detect intervals where predictions are lower than a predefined level. Moreover, the combinatorial algorithm does assess the simplicity of the identified near optimal algebraic model. Automatic adaptive identification of quasi-stationary segments can be employed for complex financial time series.

Visual Cryptography Based on Optical Image Projection

A visual cryptography scheme based on optical image projection is proposed in this paper. Initially the secret image is split into two shares. Then, such digital images are constructed in share’s planes that their projections in the projection screen would correspond to each of the appropriate shares. Geometrical parameters describing the location of shares’ planes and focus points of projectors are additional security parameters of the encoded image. Direct overlapping of the reconstructed shares does not leak any information on the encrypted image. The original image can be interpreted by a naked eye when appropriate projectors are placed at predefined locations of the geometrical setup.