This study uses two different time series forecasting approaches (parametric and non-parametric) to assess a frequency magnitude of earthquakes above Mw 4.0 in Northeastern Algeria. The Autoregressive Integrated Moving Average (ARIMA) model encompasses the parametric approach, while non-parametric method employs Singular Spectrum Analysis (SSA) approach. ARIMA SSA models were then used train fo...

This formalization is an extension to [3]. In their work, the authors have shown that Braun et al.’s static single assignment (SSA) construction algorithm [1] produces minimal SSA form for input programs with a reducible control flow graph (CFG). However Braun et al. also proposed an extension to their algorithm that they claim produces minimal SSA form even for irreducible CFGs. In this formal...

An exact method for stochastic simulation of chemical reaction networks, which accelerates the stochastic simulation algorithm (SSA), is proposed. The present "ER-leap" algorithm is derived from analytic upper and lower bounds on the multireaction probabilities sampled by SSA, together with rejection sampling and an adaptive multiplicity for reactions. The algorithm is tested on a number of wel...

A procedure is defined to be strict if every variable is defined before it is used along every path of program execution. A regular program is a strict procedure in Static Single Assignment (SSA) Form. Recently, it has been proven that the interference graph for regular program is a chordal graph. This yielded an optimal polynomial-time algorithm for register allocation for high-level synthesis...

Paper aims To predict monthly corn, soybean, and sugar spot prices in Brazil using hybrid forecasting techniques. Originality This study combines the Singular Spectrum Analysis with different methods. Research method paper presents a set of approaches combining (SSA) univariate time series methods, ranging from complex seasonality methods to machine learning autoregressive models Brazil. We car...

Abstract To improve the accuracy of wind power forecasting, improved ACA (Ant Colony Algorithm) is used to optimize GRU (Gated Recurrent Unit) model. First, original generation data normalized; Second, neural network model established, and ant colony algorithm it; Finally, optimized non-optimized are predict short-term output, prediction results compared verify that ACA-GRU has higher for output.

Partial Redundancy Elimination (PRE) is an effective optimization for eliminating partially redundant expressions and includes the effects of common subexpression elimination and hoisting loop invariant expressions. There have been some previous attempts to realize PRE on the Static Single Assignment (SSA) form, which is a suitable intermediate form for optimization. However, such attempts are ...

In order to forecast the stock market more accurately, according to the dynamic property for the stock market, propose the real time modeling forecast via dynamic recurrent neural network and use GA to study online, then it improves the network performance and better describes the dynamic characteristic of stock market. By forecasting Shanghai negotiable securities index, it shows better validi...

The SSA-form uses a notational abstractions called φ-functions. These instructions have no analogous in actual machine instruction sets, and they must be replaced by ordinary instructions at some point of the compilation path. This process is called SSA elimination. Compilers usually performs SSA elimination before register allocation. But the order could as well be the opposite: our puzzle bas...

Abstract: Forecasting the future evolution of a system based only on past information comprises a central scientific problem. In this work we investigate the comparative performance of recurrent multi–layer perceptrons, trained through backpropagation through time and the differential evolution algorithm, to perform one–step–ahead predictions for the laser time series (Data set A) from the Sant...