Modeling implied volatility surface (IVS) is of paramount importance to price and hedge an option. We contribute the literature by modeling entire IVS using convolutional long-short-term memory (ConvLSTM) (LSTM) neural networks produce multivariate multistep forecasts S&P 500 IVS. Using daily SPX options data (2002–2019), we find that both LSTM ConvLSTM fit training extremely well with mean a...

This study explores the modeling of share telecommunication revenues in gross domestic product from year 2000 to 2018 for 5 countries including France, Germany, Italy, Turkey, UK, and OECD average. First, a new mathematical model based on Fractional Calculus Least Square Method is proposed. Later, GDP dataset modeled. Further, we compare approach classical Polynomial three different settings. T...

Jumlah pengangguran yang tinggi dapat menghambat proses pembangunan ekonomi, sehingga diperlukan sistem peramalan untuk mengetahui jumlah di Asahan berpengaruh terhadap kebijakan pemerintah dalam pengambilan keputusan sebelum terjadinya peningkatan pengangguran. Peramalan ini berdasarkan data aktual kurun waktu 12 tahun menggunakan metode Double Exponential Smoothing (DES). Metode akurasi digun...

Electricity is becoming an important commodity in Cameroon. Within the years, its consumption and production have led to many studies. Hence, having idea on progression one of research’ concerns. Thus, this paper aims develop a model for forecasting electricity Cameroon based Long Short-Term Memory (LSTM). Indeed, LSTM approach, showing good ability grab long-term dependencies between time step...

In this study, an artificial neural network (ANN) model was developed to predict the gloss of thermally densified wood veneers. A custom application created with MATLAB codes employed for development multilayer feed-forward ANN model. The species, temperature, pressure, measurement direction, and angle incidence were considered as inputs, while output Model performance evaluated by using mean a...

Abstract—The aim of this paper is to select the most accurate forecasting method for predicting the future values of the unemployment rate in selected European countries. In order to do so, several forecasting techniques adequate for forecasting time series with trend component, were selected, namely: double exponential smoothing (also known as Holt`s method) and Holt-Winters` method which acco...

High blood pressure (BP) is associated with an increased risk of cardiovascular diseases. Therefore, optimal precision in measurement of BP is appropriate in clinical and research studies. In this work, anthropometric characteristics including age, height, weight, body mass index (BMI), and arm circumference (AC) were used as independent predictor variables for the prediction of BP reactivity t...

Forecasting is a systematic attempt to examine the future by inference from known facts. Sales forecasting is an ballpark figure of sales during a specified future period. Formerly, it was a manual process using the mathematical formulas. Due to the advent of computer the process of sale forecasting is fast and accurate. Machine learning, a subfield of Artificial Intelligence, has many algorith...

This paper presents an application of Artificial Neural Network (ANN) to forecast actual cost of a project based on the earned value management system (EVMS). For this purpose, some projects randomly selected based on the standard data set , and it is produced necessary progress data such as actual cost ,actual percent complete , baseline cost and percent complete for five periods of project. T...

Scheduling streaming applications in Data Stream Management Systems (DSMS) has been investigated for years. However, there lacks an intelligent system that is capable of monitoring application execution, modelling its resource usages, and then adjusting the scheduling plan under different sizes of inputs without requiring users’ intervention. In this paper, we model the scheduling problem as a ...