Decision making process in stock trading is a complex one. Stock market is a key factor of monetary markets and signs of economic growth. In some circumstances, traditional forecasting methods cannot contract with determining and sometimes data consist of uncertain and imprecise properties which are not handled by quantitative models. In order to achieve the main objective, accuracy and efficiency of time series forecasting, we move towards the fuzzy time series modeling. Fuzzy time series is different from other time series as it is represented in linguistics values rather than a numeric value. The Fuzzy set theory includes many types of membership functions. In this study, we will utilize the Fuzzy approach and trapezoidal membership function to develop the fuzzy generalized auto regression conditional heteroscedasticity (FGARCH) model by using the fuzzy least square techniques to forecasting stock exchange market prices. The experimental results show that the proposed forecasting system can accurately forecast stock prices. The accuracy measures RMSE, MAD, MAPE, MSE, and Theil-U-Statistics have values of 18.17, 15.65, 2.339, 301.998, and 0.003212, respectively, which confirmed that the proposed system is considered to be useful for forecasting the stock index prices, which outperforms conventional GARCH models.
A time series is a sequence of elements with numerical data in sequential order and having regular intervals. Time series are used in statistics, enrollments, signal processing, econometrics, mathematical finance, and weather forecasting. It helps us to forecast and predict the time series data in different domains. There are many methods to forecast the enrollments in literature which have large applications and are presented in the field of statistics and econometrics. One of the robust methods, we used in our research, is moving average. It helps to forecast and predict the data whenever the fuzziness occurs in time series data, which is not appropriate in crisp time series forecasting. To get rid of this problem, the fuzzy interval partitioning method proved to be more appropriate to generate accurate results. This research will focus to overcome the failure of the crisp method and to show the use of a fuzzy interval partitioning method. The fuzzy interval partitioning method is different from another interval partition schemes because it specifies the linguistic values rather than numerical values. It is also used to deal with uncertain conditions. So, fuzzy interval partitioning improves data utilization and also calculates the higher predicted accuracy rate. Besides this research, we use a quantitative method and a fuzzy moving average with the interval partitioning method. Then we compare the efficiency of moving average model and moving average with fuzzy interval partitioning method for forecasting the enrollments.
Acoustic signals travels rapidly in water without attenuating fish telemetry. The digital sonar and passive acoustic has been used for fish monitoring and fish feeding. However, it is an urgent need to introduce new techniques in order to monitor the growth rate of fish during harvesting and without causing adverse effects to the harvested fish. Therefore, a novel technique was introduced to probe the acoustic signal frequency ratio in absence and presence of the fish in tanks, which basically uses an acoustic sensor (hydrophone), acoustic signal processing system (scope meter), and a signal monitoring system (fluke view). Acoustic signals were selected from 48-52 Hz frequency, measure of dispersion of frequency signal represented as a function of time via Xlstat software. Measure of dispersion displayed a significant effect of acoustic signal in the presence and absence of the fish in tanks. These optimised protocols of this study will help to control and prevent excessive wastage of feed and enhance proper utilization of feed that chiefly enhance fish growth in aquaculture
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