This paper explores the power of stock trading trend using an integrated New Threshold Fuzzy Cognitive Maps (NTFCMs) Markov chain model. This new model captures the positive as well as the negative jumps and predicts the trend for different stocks over 4 months which follow an uptrend, downtrend and a mixed trend. The mean absolute per cent error (MAPE) tolerance limits, the root mean square error (RMSE) tolerance limits are determined for various stock indices over a multi-timeframe period and observed for the existing methods lying within the defined limits. The results show for every ‘n’ number of predictions made, the predicted close value of the day’s stock price was within tolerance limit with 0 % error and with 100% accuracy in predicting the future trend.

American scientific publishing group

Accurate energy forecasting is essential for the efficient operation of smart grids and renewable energy systems. This study presents an optimized energy forecasting model that combines a Hidden Markov Model (HMM) with transformed fuzzy relational matrices (TFRM), enhanced by a genetic algorithm (GA) and particle swarm optimization (PSO). The HMM effectively captures the temporal dependencies in energy consumption data, while the TFRM handles uncertainties and imprecisions. The genetic algorithm refines the fuzzy relational matrices, and PSO further optimizes the model parameters to ensure convergence to the best possible solution.The proposed model's performance is validated using real-world energy consumption datasets, showcasing its accuracy and robustness compared to traditional forecasting methods. Experimental results demonstrate that this hybrid approach successfully addresses the nonlinear and non-stationary characteristics of energy data, leading to more reliable and precise forecasts. This innovative method holds significant promise for enhancing energy management and optimization in various applications, contributing to the development of smarter and more efficient energy systems.

Price movements of stock market are not totally random. In fact, what drives the financial market and what pattern financial time series follows have long been the interest that attracts economists, mathematicians and most recently computer scientists [17]. This paper gives an idea about the trend analysis of stock market behaviour using Hidden Markov Model (HMM). The trend once followed over a particular period will sure repeat in future. The one day difference in close value of stocks for a certain period is found and its corresponding steady state probability distribution values are determined. The pattern of the stock market behaviour is then decided based on these probability values for a particular time. The goal is to figure out the hidden state sequence given the observation sequence so that the trend can be analyzed using the steady state probability distribution( ) values. Six optimal hidden state sequences are generated and compared. The one day difference in close value when considered is found to give the best optimum state sequence.