The variation and dependency on different parameters of stock market makes prediction a complex process. Artificial neural Networks have been proven to be useful in such cases to predict the stock values.The parameters involved and the commonly used algorithms are discussed and compared in this paper. In case of backpropagation algorithm, a feed forward network is present and weights are modified by back propagating the error. Similarly, significant modification is introduced in Sup-port Vector Machines Algorithm(SVMA) which results in higher accuracy rates. Presence of kernel and other parameters make it more flexible. Long Short-Term Memory(LSTM), another commonly used time series forecasting algorithm, is a special type of Recurrent Neural Network(RNN) that uses gradient descent algorithm. This paper provides a comparative analysis between these algorithms on the basis of accuracy, variation and time required for different number of epochs. The T-test hypothesis test was used for further analysis to test the reliability of each algorithm.
Orthogonal Frequency Division Multiple Access (OFDMA) is the preferred multiple access scheme for future broadband cellular systems as it provides high spectral efficiency. The choice of OFDMA scheduler and the amount of channel quality feedback from users together determine the achievable throughput and fairness. Channel quality reporting by all active users across the entire carrier bandwidth may result in significant uplink overhead. This paper discusses the performance of two possible OFDMA schedulers based on the class and channel condition weighted proportionally fair scheduler. The impact of different scheduler parameters are investigated. The paper then analyzes the performance of the top-M scheme, which is a candidate channel quality overhead reduction proposal being discussed in 3GPP Long Term Evolution. It is shown that the top-M scheme, with significantly lower uplink overhead, provides similar performance as the case where the network has complete channel quality information.
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