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Forecasting the stock market is a challenging task because of its stochastic and complex nature. Various statistical models and data mining techniques have been developed in the recent years and applied to stock market forecasting. A review of the relevant literature shows that only a very few studies have applied high frequency data to forecast the stock market and among these studies, only one or two have applied data mining techniques. There are no studies on forecasting high frequency data of stock index using multivariate adaptive regression splines. In this paper we study the applicability of the following four data mining techniques: backpropagation neural network (BPNN), support vector regression (SVR), multivariate adaptive regression splines (MARS) and Markov chain incorporated into fuzzy stochastic (MF), for one-stepahead forecast of S&P CNX Nifty index of India and Nasdaq composite index of USA with every sixtieth minute data. The results of the study shows that SVR is better than the others for forecasting high frequency data of both indices with an accuracy of 99.7 %.

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In the design and development of high-speed tracked vehicles, it is necessary to have an understanding of the interrelationship between the terrain factors and the vehicle characteristics during steering. The handling behavior of skidsteered tracked vehicles is more complex than that of wheeled vehicles because of non-linear characteristics arising from the sliding interface between the track and the ground. In the present work, a five degree-of-freedom (DOF) steering model of a tracked vehicle is developed, and the handling behavior during non-stationary motion is studied when operating at high and low speeds. It is demonstrated that the inclusion of roll and pitch DOF changes the steering response when compared to the response from three DOF models proposed earlier by several researchers. This is due to the strong coupling between the pitch and yaw motions. The effect of the initial forward velocities on the trajectory of the vehicle during non-stationary motion is also studied. It is observed from the results that the stability is influenced by the type of steering input, steering ratio and vehicle forward speed.

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