What is exponentional smoothing in series times ?
551595
Gaussian distribution
776321
the question is unclear.\nDo you want an explicit formula for Y[n or do you just want to write out the convolution]
879865
Not all functions can be accurately described with a line of best fit. Sometimes a curve of best fit models the data the best. There are also many different types of curves of best fit, of which one is expoential, that can model the data in different ways. If exponential matches the data points the best, it means that if you attempt to extend the graph to extrapolate data (estimate higher than the numbers in the function show by using the curve/line of best fit on the graph), it will be more accurate than using a simple straight line.
764897
use derivative rule for exponential as\n\nd/dx(A exp(bx))=(A*b)*exp(bx)\n\nyou got hint. No more Spoon Feeding
652846
The reason I can point out is that when you say 'x' is added 'x' times, it means that 'x' is a positive integer (e.g. you can have upto 5 terms, but not say upto 5.34 terms). This indicates that on a real number line, 'x' is just a set of discrete, discontinuous values, hence I think that differentiating a function that is discontinuous in any given interval yields incorrect results, when equated with a function like 'x^2' which happens to be continuous & differentiable everywhere - that is possibly the cause of the problem.
1086154
The reason we call ln(x) "natural" is twofold: first, expressions in which the unknown variable appears as the exponent of e occur much more often than exponents of 10 (because of the "natural" properties of the exponential function which allow it to describe growth and decay behaviors), and second, because the natural logarithm can be defined quite easily using a simple integral or Taylor series--which is not true of other logarithms.
782532
there is no built-in at-the-nth-power operator in c++,\n\nsimply use \n#include <math.h>\ndouble z = pow(3,2);
650155
simple\nfirst\nwhen we apply ∑ we can write the series as\n(a_2 - a_1) + (a_3 - a_2) + (a_4 - a_3).... (a_n - a_n-1) (a_n+1 - a_n)\n\nwhen u add \nthe terms starting frm a_2(except a_1 & a_n+1) get cancelled with the correseponding terms and u get\na_n+1 + a_1
1099598
If you mean an exponent, the function in C is pow(x,y)
447001
pie times radius squared
139157
Try to fit your data with Excel’s exponential fitting curves,\nIt will give you Coefficients which make the exponential looks like a line.\n\nOr try the experiment more rapidly.
152635
You program the data in to a calculator (like a TI-83), graph, and model the data points using an exponential profile.  It will then give you the form of the equation that best fits your data points in the form of a*e^(b*x), which you can then graph over your data points.  In your case, "b" should be negative to indicate decay.
1181156
It looks like that first 1 should be an i. In this case,\ni^z=exp(i*pi/2*z),\nwhere I am using the principal branch of the logarithm to define the exponential.\nThen the derivative will be \n(i*pi/2)*exp(i*pi/2*z)=\npi/2*i^(z+1)\n\nEasy.
620893
The best way to work this out would be to plot a speed time graph for the information.\n\nOr you could express it as a simultaneous equation.
259218
ARIMA combines autoregression--which fits the current data point to a linear function (usually) of some prior data points--and moving averages--adding together several consecutive data points and getting their mean, and then using that to compute estimations of the next value.\n\nThe advantage is that, with enough elements regressed and averaged, you can fit an approximation to almost any time series you like, to whatever precision you like.\n\nThe trouble, of course, is Slutzsky's theorem:  Slutzsky showed that, by using ARIMA type computation, and perhaps adding a trend line or two, you can take random noise into any time series you like...  The point?  Well, it basically means that you may fit the data magnificently, but the ARIMA fit could still be total nonsense...
346407
I think it is order(N squared), so not very good. It's a variant of bubble sort, which you can google. Plus you never know when you're done!
482206
Is exponentially proportionally to the load.