Conformal prediction intervals for neural networks using cross validation

Neural networks are among the most powerful nonlinear models used to address supervised learning problems. Similar to most machine learning algorithms, neural networks produce point predictions and do not provide any prediction interval which includes an unobserved response value with a specified probability. In this creative component, we propose the k-fold prediction interval method to construct prediction intervals for neural networks based on k-fold cross validation. Simulation studies and analysis of 10 real datasets are used to compare the finite-sample properties of the prediction intervals produced by the proposed method and the split conformal (SC) method. The results suggest that the proposed method tends to produce narrower prediction intervals compared to the SC method while maintaining the same coverage probability. Our experimental results also reveal that the proposed k-fold prediction interval method produces effective prediction intervals and is especially advantageous relative to competing approaches when the number of training observations is limited

An enhanced fuzzy linguistic term generation and representation for time series forecasting

This paper introduces an enhancement to linguistic forecast representation using Triangular Fuzzy Numbers (TFNs) called Enhanced Linguistic Generation and Representation Approach (ElinGRA). Since there is always an error margin in the predictions, there is a need to define error bounds in the forecast. The interval of the proposed presentation is generated from a Fuzzy logic based Lower and Upper Bound Estimator (FLUBE) by getting the models of forecast errors. Thus, instead of a classical statistical approaches, the level of uncertainty associated with the point forecasts will be defined within the FLUBE bounds and these bound can be used for defining fuzzy linguistic terms for the forecasts. Here, ElinGRA is proposed to generate triangular fuzzy numbers (TFNs) for the predictions. In addition to opportunity to handle the forecast as linguistic terms which will increase the interpretability, ElinGRA improved forecast accuracy of constructed TFNs by adding an extra correction term. The results of the experiments, which are conducted on two data sets, show the benefit of using ElinGRA to represent the uncertainty and the quality of the forecast

Interval Prediction of the Safety Risk of Soy Sauce and Pot-Roast Meat Products Based on WPD-ARIMA-GARCH Model

In view of the drawback of traditional deterministic prediction that it cannot provide uncertainty information, this study proposed a prediction model that integrates point estimation and interval estimation, and innovatively applied it to the field of food safety risk pre-warning. In the point estimation, wavelet packet decomposition (WPD) was used to decompose the weekly risk level sequence and the autoregressive integrated moving average (ARIMA) model was used for prediction. In the interval estimation, the generalized autoregressive conditional heteroskedastic (GARCH) model was used to predict the residual. The WPD-ARIMA-GARCH model established in this study was applied to the safety risk prediction of soy sauce and pot-roast meat products from a certain region. The results showed that the safety risk of soy sauce and pot-roast meat products from this region was relatively high at the end of March and July in 2019, which was consistent with the actual situation. Meanwhile, in the risk prediction of soy sauce and pot-roast meat products from 10 different regions, the mean square error, mean absolute error, and mean absolute percentage error of the model were 1.626, 0.806, and 20.824, respectively, and the prediction interval normalized average and coverage width-based criterion values at the 90% confidence interval were both 0.024, which could cover all true values. Therefore, the model has high prediction accuracy and low error, is useful for risk control for the quality and safety of soy sauce and pot-roast meat products, and provide technical support for daily food safety supervision