Financial market time series exhibit high degrees of non-linear variability, and frequently have fractal properties. When the fractal dimension of a time series is non-integer, this is associated with two features: (1) inhomogeneity— extreme fluctuations at irregular intervals, and (2) scaling symmetries— proportionality relationships between fluctuations over different separation distances. In...

We evaluate and compare several forecasting techniques using no exogenous inputs for predicting the solar power output of a 1 MWp, single-axis tracking, photovoltaic power plant operating in Merced, California. The production data used in this work corresponds to hourly averaged power collected from November 2009 to August 2011. Data prior to January 2011 is used to train the several forecastin...

the present research was planned to evaluate the skill of linear stochastic models known as arima and multiplicative seasonal autoregressive integrated moving average (sarima) model in the quantitative forecasting of the standard runoff index (sri) in karkheh basin. to this end, sri was computed in monthly and seasonal time scales in 10 hydrometric stations in 1974-75 to 2012-13 period of time ...

agricultural prices have a high fluctuation and forecasting may help decision making effectively. the aim of this study was to forecast the nominal and real prices of sugar beet and to recognize the appropriate forecasting model. initially the stationary of the series was tested. in order to investigate whether the series are stochastic, the nonparametric test of vald-wulfowitz and parametric t...

The focus in this work is to assess which method allows a better forecasting of malaria cases in Bujumbura ( Burundi) when taking into account association between climatic factors and the disease. For the period 1996-2007, real monthly data on both malaria epidemiology and climate in Bujumbura are described and analyzed. We propose a hierarchical approach to achieve our objective. We first fit ...

This paper proposes the deterministic generation of auxiliary variables, which outline the seasonal, cyclic and trend components of the time series associated with tourism demand for the machine learning models. To test the contribution of the deterministically generated auxiliary variables, we have employed multilayer perceptron (MLP) regression, and support vector regression (SVR) models, whi...

Solar photovoltaic power (PV) generation has increased constantly in several countries in the last ten years becoming an important component of a sustainable solution of the energy problem. In this paper, a methodology to 24-hour or 48-hour photovoltaic power forecasting based on a Neural Network, trained in a Bayesian framework, is proposed. More specifically, an multi-ahead prediction Multi-L...

Structural breaks and existence of outliers in time series variables results in misleading forecasts. We forecast wheat and rice prices by capturing the exogenous breaks and outliers using Automatic modeling. The procedure identifies the outliers as the observations with large residuals. The suggested model is compared on the basis of Root Mean Square Error (RMSE) and Mean Absolute Percentage E...

Goodness-of-t is the most popular criterion for neural network time series forecasting. In the context of nancial time series forecasting, we are not only concerned at how good the forecasts t their targets, but we are more interested in proots. In order to increase the forecastability in terms of proot earning, we propose a proot based adjusted weight factor for backpropagation network trainin...

This paper presents a small-scale macroeconometric time-series model that can be used to generate short-term forecasts for U.S. output, inflation, and the rate of unemployment. Drawing on both the Bayesian VAR and vector error corrections (VEC) literature, I specify the baseline model as a Bayesian VEC. I document the model’s forecasting ability over various periods, examine its impulse respons...