Gregory Barco

def nelson_siegel_svensson(t, β0, β1, β2, β3, λ1, λ2): """Nelson-Siegel-Svensson yield curve model. Parameter t: Time to maturity in years""" return (β0 + β1 * (1 - np.exp(-t / λ1)) / (t / λ1) + β2 * ((1 - np.exp(-t / λ1)) / (t / λ1) - np.exp(-t / λ1)) + β3 * ((1 - np.exp(-t / λ2)) / (t / λ2) - np.exp(-t / λ2)))

# Plot the original monthly data and spline fitting curve monthly_spline_plot <- ggplot() + geom_line(data=spline_curve, aes(x=month, y=amount), color="red" , size=1) + geom_point(data=monthly_spending_data, aes(x=month, y=amount), color="blue" , size=2) + labs(title="Collected Data Monthly Spending Cubic Spline Interpolation" , x="Month" , y="Monthly Spending Amount ($)" ) + theme_minimal() + theme(plot.title=element_text(hjust=0.5)) # Monte Carlo simulation for spending trajectories # Function to generate one spending trajectory generate_trajectory <- function(num_months, noise_level) { trajectory <- numeric(num_months)

// Google Apps Script - Database sync function if (matchesSearch) { // Get ordered items const items = getOrderedItems(row, columnMap); const calculatedTotal = items.reduce((sum, item) => sum + (parseInt(item.quantity) || 0), 0);}