With the Gaussian Process model, the predictive distribution of the output corresponding to a new given input is Gaussian. But if this input is uncertain or noisy, the predictive distribution becomes non-Gaussian. We present an analytical approach that consists of computing only the mean and variance of this new distribution (Gaussian approximation). We show how, depending on the form of the co...

We have studied neural networks as models for time series forecasting, and our research compares the Box-Jenkins method against the neural network method for long and short term memory series. Our work was inspired by previously published works that yielded inconsistent results about comparative performance. We have since experimented with 16 time series of di ering complexity using neural netw...

Abstract This paper revisits real-time forecasting of U.S. inflation based on Phillips curve-inspired linear regression models. Our innovation is to allow for both structural breaks in the regression parameters and the variance as well as uncertainty regarding which set of predictor variables one can include in these regressions (‘model uncertainty’). Structural breaks are described by occasion...

In this paper, we show that the Chapman-Kolmogorov formula could be used as a recursive formula for computing the m-step-ahead conditional density of a Markov bilinear model. The stationary marginal probability density function of the model may be approximated by the m-step-ahead conditional density for sufficiently large m.

BACKGROUND Any improvement in the forecast accuracy of life expectancy would be beneficial for policy decision regarding the allocation of current and future resources. In this paper, I revisit some methods for forecasting age-specific life expectancies. OBJECTIVE This paper proposes a model averaging approach to produce accurate point forecasts of age-specific life expectancies. METHODS Illust...

Accurate wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. Particularly, reliable short-term wind speed prediction can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, this task remains challenging due to the strong stochastic nature and dynamic uncertainty of wind speed....

State-of-the-art multivariate forecasting methods are restricted to low dimensional tasks, linear dependencies and short horizons. The technological advances (notably the Big data revolution) instead shifting focus problems characterized by a large number of variables, non-linear long In last few years, majority best performing techniques for have been based on deep-learning models. However, su...

Accurate multi-step PM2.5 (particulate matter with diameters ?2.5um) concentration prediction is critical for humankinds’ health and air population management because it could provide strong evidence decision-making. However, very challenging due to its randomness variability. This paper proposed a novel method based on convolutional neural network (CNN) long-short-term memory (LSTM) space-shar...

Accurate streamflow prediction is significant when developing water resource management and planning, forecasting floods, mitigating flood damage. This research developed a novel methodology that involves data pre-processing an artificial neural network (ANN) optimised with the coefficient-based particle swarm optimisation chaotic gravitational search algorithm (CPSOCGSA-ANN) to forecast monthl...