Time series prediction is a problem with a wide range of applications, including energy systems planning, currency forecasting, stock exchange operations or traffic prediction. Accordingly, a number of different prediction approaches have been proposed such as linear models, Feedforward Neural network models, Recurrent Neural networks or Fuzzy Neural Models. In this paper one presents a predict...

This paper presents a time series forecasting methodology and applies it to generate multiple–step–ahead predictions for the direction of change of the daily exchange rate of the Japanese Yen against the US Dollar. The proposed methodology draws from the disciplines of chaotic time series analysis, clustering, and artificial neural networks. In brief, clustering is applied to identify neighborh...

To feed the execution core with uninterrupted instruction streams, modern processors access complex branch predictors every clock cycle, incurring high energy consumption. In this paper, we conduct a systematic study on the energy consumption of representative branch predictors, and identify several sources of energy inefficiency. Based on this study, we introduce an improved branch predictor d...

Computational intelligence approaches to multiple-step-ahead forecasting rely either on iterated one-step-ahead predictors or direct predictors. In both cases the predictions are obtained by means of multi-input single-output modeling techniques. This paper discusses the limits of single-output approaches when the predictor is expected to return a long series of future values and presents a mul...

A basic rule in computer architecture is that a processor cannot execute an application faster than it fetches its instructions. This paper presents a novel cost-eeective mechanism called the two-block ahead branch predictor. Information from the current instruction block is not used for predicting the address of the next instruction block, but rather for predicting the block following the next...

A multi-step ahead predictive filter for missing data handling is presented in this paper. This is a simple FIR filter, useful for time and memory critical applications. Furthermore, our proposed algorithm is formulated in such a way that only one set of FIR weights are tuned (by steepest gradient descent method), memorised and used by the system for different numbers of steps ahead in predicti...

Ambient air temperature prediction is of a concern in environment, industry and agriculture. The increase of average temperature results in natural disasters, higher energy consumption, damage to plants and animals and global warming. Ambient air temperature predictions are notoriously complex and stochastic models are not able to learn the non-linear relationships among the considered variable...

The prediction by partial matching (PPM) algorithm has been well known for its high prediction accuracy. Recent proposals of PPM-like predictors confirm its effectiveness on branch prediction. In this paper, we introduce a new branch prediction algorithm, named Prediction by combining Multiple Partial Matches (PMPM). The PMPM algorithm selectively combines multiple matches instead of using the ...

This paper proposes an intelligent forecasting system based on a feedforward-neural-network-aided grey model (FNAGM), which integrates a first-order single variable grey model (GM(1,1)) and a feedforward neural network. There are three phases in the system process, including initialization phase, GM(1,1) prediction phase and FNAGM prediction phase. First, some parameters required in the FNAGM a...

this study predicts corporate bankruptcy five years before its occurrence using financial ratios introduced in altman’s z-score model and current ratio. three estimation methods namely; liner probability, logit and probit models have chosen for model estimation. the sample contains 134 companies in tehran stock exchange during 2003. the precision of prediction of the estimated models for the ma...