A pre-proof study in Pattern Recognition has proposed a novel motion-blurred image restoration framework based on blur parameter estimation and fuzzy inference neural networks. The proposed restoration framework produces high-quality image restoration results.
Projects with new seed funding from Princeton University’s Schmidt DataX Fund include one to improve the safety of autonomous driving and another to explore the dynamics of human thought.
Stochastic processes are employed in this paper to capture the evolution of daily mean temperatures, with the goal of pricing temperature-based weather options. A stochastic harmonic oscillator model is proposed for the temperature dynamics and results of numerical simulations and parameter estimation are presented. The temperature model is used to price a one-month call option and a sensitivity analysis is undertaken to examine how call option prices are affected when the model parameters are varied.
(1)
. Therefore, only q errors will affect the existing level, but higher order errors do not affect
. This indicates that it is a short memory model.
Auto-Regression (AR)
p, an AR (
(2)
The model is described in terms of past values and therefore we would like to estimate the coefficients
, and use the model for forecasting. All previous values will have cumulative effects on the existing level, which is a long-run memory model.
Autoregressive Integrated Moving Average (ARIMA) Process
ARIMA modeling methods were used in this study based on a common method available for modeling and forecasting the time series data. ARIMA is the most common class of time series models which can be made “stationary” by differencing (if necessary), possibly in combination with non-linear transformations such as logging or deflating (if necessary)