FOG COMPUTING BASED BEARING REMAINING USEFUL LIFE PROGNOSIS USING TIME SERIES NORMALIZED SIMILARITY AND RECURRENT NEURAL NETWORKS

Techniques are described for determining a remaining useful life (RUL) prognosis of bearings using a feature extraction module for extracting time series normalized similarity (TSNS) features for vibration data normalization and a prediction module utilizing a deep learning model, known as an independently recurrent neural network (IndRNN), for predicting bearing RUL. The feature extraction module and prediction module are deployed on a fog computing platform as services for determining the RUL prognosis of bearings

TM-vector: A Novel Forecasting Approach for Market stock movement with a Rich Representation of Twitter and Market data

Stock market forecasting has been a challenging part for many analysts and researchers. Trend analysis, statistical techniques, and movement indicators have traditionally been used to predict stock price movements, but text extraction has emerged as a promising method in recent years. The use of neural networks, especially recurrent neural networks, is abundant in the literature. In most studies, the impact of different users was considered equal or ignored, whereas users can have other effects. In the current study, we will introduce TM-vector and then use this vector to train an IndRNN and ultimately model the market users' behaviour. In the proposed model, TM-vector is simultaneously trained with both the extracted Twitter features and market information. Various factors have been used for the effectiveness of the proposed forecasting approach, including the characteristics of each individual user, their impact on each other, and their impact on the market, to predict market direction more accurately. Dow Jones 30 index has been used in current work. The accuracy obtained for predicting daily stock changes of Apple is based on various models, closed to over 95\% and for the other stocks is significant. Our results indicate the effectiveness of TM-vector in predicting stock market direction.Comment: 24 pag