Sentence examples for linear forecast from inspiring English sources

According to the limitations of the original algorithm presented, chaotic local weighted linear forecast algorithm based on the angle cosine is proposed, which replaces Euclidean distance by cosine in the measurement of the similarity between phase points.

It is known that these matrices have a wide range of applications in signal processing, coding theory, image processing, digital image disposal, linear forecast and design of self-regress.

A data-driven approach with two regression-based linear forecasting models is proposed in [21], and the MAPE is 9.67%.

We finally demonstrate that the sufficient forecasting improves upon the linear forecasting in both simulation studies and an empirical study of forecasting macroeconomic variables.

In this paper, we make a novel information assumption that private agents cannot observe aggregate fundamental shocks, and use simple linear forecasting rules for learning.

The original time series cannot be distinguished from their linear surrogates when using nonlinear test statistics, nor is there an improvement in forecast error for nonlinear over linear forecasting methods.

For example: it contributes to overcoming cognitive biases and linear forecasting; it helps to detect weak signals, those even experts may not be aware of; it provides a way to see through technological hypes, promises and interests.

Moreover, the linear forecasts based on a simple-to-implement 'average' (or 'subsampled') estimator obtained by averaging standard sparsely sampled realized volatility measures generally perform on par with the best alternative robust measures.

The performance of the FFNN model was compared to the newly proposed combined WT FFNN model and also the conventional linear forecasting method of ARIMAX (Auto Regressive Integrated Moving Average with exogenous input).

Using the estimated parameters to form the best linear forecasts for future volatility we find that the behavioral model generates sensible forecasts that get close to those of a standard GARCH 1,1) model in their overall performance, and often provide useful information on top of the information incorporated in the GARCH forecasts.

BP neural network is a neural network model that is most widely used in non-linear forecast.