Prediction techniques have the challenge of guaranteeing large horizons for chaotic time series. For instance, this paper shows that majority can predict one step ahead with relatively low root-mean-square error (RMSE) and Symmetric Mean Absolute Percentage Error (SMAPE). However, some based on neural networks more steps similar RMSE SMAPE values. In manner, work provides a summary prediction t...

The scheduling and operation of power system becomes prominently complex and uncertain, especially with the penetration of distributed power. Load forecasting matters to the effective operation of power system. This paper proposes a novel deep learning framework to forecast the short-term grid load. First, the load data is processed by Box-Cox transformation, and two parameters (electricity pri...

Virtual bids were introduced in U.S. wholesale electricity markets to exploit arbitrage opportunities arising from expected price differences between day-ahead and real-time energy markets. These financial instruments have interactions with other elements of the electricity market design. For instance, virtual bids could affect day-ahead market-clearing prices so as to enhance the value of Fina...

Traffic prediction constitutes a hot research topic of network metrology. MultiStep ahead prediction allows to predict more values in the future. Then, the result can be used to act proactively in many prediction applications. In this work, the AutoRegressive Integrated Moving Average (ARIMA) model and the linear minimum mean square error (LMMSE) are used for multiStep predicting. Via experimen...

We describe an eye-tracking experiment that tested the effect of syntactic predictability on skipping rates during reading. We found that plural noun phrases were skipped more often than singular noun phrases, in syntactic contexts which induced a high expectation for a plural. We interpret this effect as evidence that the plural noun phrase has been predicted ahead of time. The results indicat...

Model validation is an important step in system identification process. However, theoretical derivation of model validity tests for neural network such as RBF network is very complicated. The current study, investigate the capability of some of the model validity tests that are widely been used namely one step ahead prediction, model predicted output, means square error and correlation tests. T...

A simple recurrent neural network is trained on a one-step look ahead prediction task for symbol sequences of the context-sensitive a n b n c n language. Using an evolutionary hill climbing strategy for incremental learning the network learns to predict sequences of strings up to depth n = 12. Experiments and the algorithms used are described. The activation of the hidden units of the trained n...