This paper proposes a Multiples Input-Multiples Ouput Autoregressive (MIMO-AR) model based on two stages to improve monthly anchovy catches forecasting of the coastal zone of Chile for periods from January 1958 to December 2011. In the first stage, the stationary wavelet transform (SWT) based on Fejer-Korovkin (FK) wavelet filter is used to separate the raw time series into a high frequency (HF...

One of the most important hopes in modern finance is finding the most accurate ways to forecast future values of exchange rates.The research provides some evidence about the efficiency of utilizing neural network models in forecasting foreign exchange rates. We used three distinct learning algorithms in our neural network models, namely,Scaled Conjugate Gradient (SCG), Standard Backpropagation ...

Artificial neural networks (ANNs) allow users to improve forecasts through pattern recognition. The purpose of this paper is to validate ANNs as a detection tool in four financial markets. This study investigates whether market inefficiencies exist using ANN as a model. It also investigates whether additional publicly available information can provide investors with a trading advantage. In fina...

With the increasing number of quantitative models available to forecast the volatility of crude oil prices, the assessment of the relative performance of competing models becomes a critical task. Our survey of the literature revealed that most studies tend to use several performance criteria to evaluate the performance of competing forecasting models; however, models are compared to each other ...

Many organizations have implemented Enterprise Resource Planning (ERP) systems, hoping to use the information integration these systems provide to improve process efficiency and effectiveness. In particular, ERP systems may be instrumental in realizing Cooperative Planning, Forecasting, and Replenishment (CPFR). In this paper, we present an experimental approach that will be used to assess the ...

The paper describes an ongoing work to define a framework to support monitoring procedures for distribution systems in smart cities. Specific use cases, involving data driven methods for energy monitoring, benchmarking and forecasting in urban scenarios are analysed. The objective is to identify services required in such framework to define the requirements of a reference architecture to suppor...

Artificial Neural Network (ANN) has been used in nonlinear systems modeling and simulation. One of the most useful and interesting factors of ANNs is forecasting. This paper discusses the application of ANNs to predict the long range energy consumption for a country. In this study the long-term energy consumption for the years ahead is predicted, exploiting ANN computational speed, ability to h...

In this work, we investigate a probabilistic method for electricity price forecasting, which overcomes traditional ones. We start considering statistical methods point forecast, comparing their performance in terms of efficiency, accuracy, and reliability, then exploit Neural Networks approaches to derive hybrid model type forecasting. show that our solution reaches the highest standard both ef...

Kernel covering algorithm combined the kernel function algorithm of SVM and the covering algorithm of constructive machine learning method, was applied in the forecast shot-time load. Constituting the kernel function based SVM, selecting the sample according to load day property, history temperature data and history load data. The algorithm achieved separating the sample set automatically and f...

Consistent transportation forecasting models that combine travel demand and network assignment are receiving more attention in recent years. A fixed point formulation for the general combined model is presented. Measures for solution accuracy are discussed. An origin-based algorithm for solving combined models is proposed. Experimental results demonstrate the efficiency of the algorithm in comp...