background and objective: scorpion sting and snakebite are the important problems in some area such as iran that must be addressed. this study was to investigate temporal pattern of scorpion sting and snakebite incidence in patients referred to masjedsoleiman’s main hospital, during 24 months from 21 march 2008 to 20 march 2009. materials and methods: it was an analytical study to scrutiny of m...

The Illain purpose of this paper is to provide a brief survey of what has CODle to be known as "prophet inequalities" or ~~prophet problerns" in the t heory of optirnal stopping. rrhis survpy includes surnrnaries of the basic results, subs e­ quent extensioIls and variations of these results, Blain proof tools an d techniques (with concrete exarllples), a,ad a list of open problenls. Although t...

Forecasting daily and weekly passenger demand is a key fundamental process used by existing urban rail transit (URT) station authorities to diagnose operational problems make decisions about train schedule patterns improve efficiency, increase revenue management, driving safety. The accuracy of the forecast results will directly affect operation planning (URT). Therefore, based on collected inb...

As with most computer science problems, representation of the data is the key to ecient and eective solutions. Piecewise linear representation has been used for the representation of the data. This representation has been used by various researchers to support clustering, classication, indexing and association rule mining of time series data. A variety of algorithms have been proposed to obtain...

Firms use time-series forecasting methods to predict sales. However, it is still a question which method forecaster best, if only single forecast needed. This study investigates and evaluates different sales methods: multiplicative Holt-Winters (HW), additive HW, seasonal auto regressive integrated moving average (SARIMA) [a variant of (ARIMA)], long short-term memory (LSTM) recurrent neural ne...

Delay Tolerant Networks (DTN) very often suffers from route failures due to the store-carry and forward approach and also due to the mobility of nodes. The problem is delay at each node until the message carrying node doesn’t find the receiver node. This situation limits the applicability of traditional routing techniques which categorize lack of path as failure of nodes and try to seek for exi...

PRoPHET (probability routing protocol using history of encounters and transitivity) reduces the invalid copy of message, so that effectively reduce the network overhead and the traffic load in the network. However, the calculation of probability in Prophet does not consider the network conditions. In this paper, we improve the performance of PRoPHET protocol by taking into account connection st...

This paper presents a comparison of statistical classical methods and machine learning algorithms for time series forecasting notably the Exponential Smoothing, hybrid ARIMA-GARCH model, K-Nearest Neighbors (KNN), Prophet, Long-Short Term Memory (LSTM). The data set used in this study is related to US inflation covers period from 1965 2021. performance models was evaluated using different metri...

Abstract Accurate and effective power system load forecasting is an important prerequisite for the safe stable operation of grid normal production society. In recent years, convolutional neural networks (CNNs) have been widely used in time series prediction due to their parallel computing other characteristics, but it difficult CNNs capture relationship sequence context meanwhile, easily leads ...

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...