Dear Ulrich Eckhardt and Jean-Michel Pichavant! First of all thank you for your attention. I'have never expected to receive response.
Actually, I am doing my internship in Marketing Division in small company., I got this assignment yesterday morning. My boss wants perfect technology diffusion based forecasting model. I found the required model, modified it..but cannot solve it (university friend suggested Python because it had special tools for optimization). I will appreciate if you help me to find right tools and give some more advises. Thank you for your precious time. As to problem, I should use nonlinear least-square estimation methodology (to estimate p_i, q_i, and β parameters) where the objective of the estimation procedure is minimization of the sum of squared error. Here in problem: F_i (t) the cumulative density function at time t for technology generation i f_i (t) the probability density function at time t for technology generation i p_i the proportion of mass media communication for generation i q_i the proportion of word of mouth for generation i μ_i (t) the market share at time t for generation i (data exists) M_i total market potential for generation i, (data exists) S_i total sales potential for generation i, S_i=μ_i (t)M_i τ_i the introduction time for generation i, τ_i≥1 (data exists) s_i (t) the actual sales of products at time t for generation i (data exists) s ̂_i (t) the estimated sales of products at time t for generation i X_i (t) the cumulative market effects β the effectiveness of the price 〖pr〗_i (t) the price at time t for generation i (data exists) α_t the seasonal factor at time t (data exists) g_t the growth rate at time t (data exists) n the number of generations (data exists) l the number of periods (data exists) C_i (t) the capacity restriction regarding the product at time t for generation i (data exists) -- http://mail.python.org/mailman/listinfo/python-list
