Probabilistic time series forecasting is crucial in many application domains, such as retail, ecommerce, finance, and biology. With the increasing availability of large volumes data, a number neural architectures have been proposed for this problem. In particular, Transformer-based methods achieve state-of-the-art performance on real-world benchmarks. However, these require parameters to be lea...

In this paper, we introduce a dynamical model for the time evolution of probability density functions incorporating uncertainty in parameters. The follows stochastic processes, thereby defining new class processes with values space densities. purpose is to quantify that can be used probabilistic forecasting. Starting from set traded prices equity indices, do some empirical studies. We apply our...

Forecasting Project Progress and Early Warning of Project Overruns with Probabilistic Methods. (December 2007) Byung Cheol Kim, B.S., Seoul National University; M.S., Seoul National University Chair of Advisory Committee: Dr. Kenneth F. Reinschmidt Forecasting is a critical component of project management. Project managers must be able to make reliable predictions about the final duration and c...

We consider forecasting in systems whose underlying laws are uncertain, while contextual information suggests that future system properties will differ from the past. We consider linear discrete-time systems, and use a non-probabilistic info-gap model to represent uncertainty in the future transition matrix. The forecaster desires the average forecast of a specific state variable to be within a...

This paper considers the correction of deterministic forecasts given by a flood forecasting model. A stochastic correction based on the evolution of an adaptive, multiplicative, gain is presented. A number of models for the evolution of the gain are considered and the quality of the resulting probabilistic forecasts assessed. The techniques presented offer a computationally efficient method for...

To make optimal decisions, end-users of decision support systems require information accurately describing the uncertainty of the underlying weather forecasts. Air temperature, dew point temperature, and wind speed are critical surface weather variables in many economic sectors. The generation of sharp and calibrated probabilistic forecasts and their effective presentation to decision makers ar...

time series forecasting is an active research area that has drawn considerable attention for applications in a variety of areas. forecasting accuracy is one of the most important features of forecasting models. nowadays, despite the numerous time series forecasting models which have been proposed in several past decades, it is widely recognized that financial markets are extremely difficult to ...