Abstract Post-hoc interpretability methods are critical tools to explain neural-network results. Several post-hoc have emerged in recent years but they produce different results when applied a given task, raising the question of which method is most suitable provide accurate interpretability. To understand performance each method, quantitative evaluation essential; however, currently available ...

We introduce in this survey the major concepts, models, and algorithms proposed so far to infer causal relations from observational time series, a task usually referred as discovery series. To do so, after description of underlying concepts modelling assumptions, we present different methods according family approaches they belong to: Granger causality, constraint-based approaches, noise-based ...

Clustering is one of the known techniques in the field of data mining where data with similar properties is within the set of categories. K-means algorithm is one the simplest clustering algorithms which have disadvantages sensitive to initial values of the clusters and converging to the local optimum. In recent years, several algorithms are provided based on evolutionary algorithms for cluster...

fuzzy time series have been developed during the last decade to improve the forecast accuracy. many algorithms have been applied in this approach of forecasting such as high order time invariant fuzzy time series. in this paper, we present a hybrid algorithm to deal with the forecasting problem based on time variant fuzzy time series and particle swarm optimization algorithm, as a highly effici...

The sales proceeds are the most important factors for keeping alive profitable companies. So sales and budget sales are considered as important parameters influencing all other decision variables in an organization. Therefore, poor forecasting can lead to great loses in organization caused by inaccurate and non-comprehensive production and human resource planning. In this research a coherent so...