Abstract: A system and method for automated stock market investment. In an embodiment, the method includes: i) inputting M previous time period values for the stock into a M-order finite impulse response (FIR) filter, the M-order finite impulse filter having a filter order M, a least mean square (LMS) prediction algorithm with step-size mu, and M adjustable filter coefficients; ii) obtaining an output from the M-order FIR filter, the output from the M-order FIR filter being a predicted next time period value for the stock; iii) comparing the predicted next time period value for the stock with an actual next time period value for the stock to calculate a prediction error; iv) inputting the calculated prediction error into an adaptive algorithm to obtain an adjustment for the at least one adjustable filter coefficient; and v) applying the adjustment for the at least one adjustable filter coefficient and repeating all steps until halted.

Abstract: A periodical expense planning system, method and data model considers income, predicted and unpredicted expenses, in order to maximize final balance and profit for the analysis period. It applies multi-period linear optimization techniques, analyzing the expenses of a house cell over N periods for every period within N. The model analyzes the predicted expenses (known costs, but not necessarily the same amount on every period) of the k future periods to come, and also takes into account the actual costs incurred in the r previous periods (having that k+r=N). By doing that, the model calculates the optimal amount that can be taken away from the model in each period (i.e., money to be invested and thus give the user earnings) is achieved, and a planning for the expenses' payment is given. The variables under analysis for each period are how much to save for the next period, how much can be taken away from the model (i.e., the money to be invested), how much should be put into the system (i.e.