The finite difference is the discrete analog of the derivative. The finite forward difference of a function is defined as
(1)

and the finite backward difference as
(2)

The forward finite difference is implemented in the Wolfram Language as DifferenceDelta[f, i].
If the values are tabulated at spacings , then the notation
(3)

is used. The th forward difference would then be written as , and similarly, the th backward difference as .
However, when is viewed as a discretization of the continuous function , then the finite difference is sometimes written
(4)
 
(5)

where denotes convolution and is the odd impulse pair. The finite difference operator can therefore be written
(6)

An th power has a constant th finite difference. For example, take and make a difference table,
(7)

The column is the constant 6.
Finite difference formulas can be very useful for extrapolating a finite amount of data in an attempt to find the general term. Specifically, if a function is known at only a few discrete values , 1, 2, ... and it is desired to determine the analytical form of , the following procedure can be used if is assumed to be a polynomial function. Denote the th value in the sequence of interest by . Then define as the forward difference , as the second forward difference , etc., constructing a table as follows
(8)
 
(9)
 
(10)
 
(11)

Continue computing , , etc., until a 0 value is obtained. Then the polynomial function giving the values is given by
(12)
 
(13)

When the notation , , etc., is used, this beautiful equation is called Newton's forward difference formula. To see a particular example, consider a sequence with first few values of 1, 19, 143, 607, 1789, 4211, and 8539. The difference table is then given by
(14)

Reading off the first number in each row gives , , , , . Plugging these in gives the equation
(15)
 
(16)

which indeed fits the original data exactly.
Formulas for the derivatives are given by
(17)
 
(18)
 
(19)
 
(20)
 
(21)
 
(22)
 
(23)
 
(24)
 
(25)
 
(26)
 
(27)

(Beyer 1987, pp. 449451; Zwillinger 1995, p. 705).
Formulas for integrals of finite differences
(28)

are given by Beyer (1987, pp. 455456).
Finite differences lead to difference equations, finite analogs of differential equations. In fact, umbral calculus displays many elegant analogs of wellknown identities for continuous functions. Common finite difference schemes for partial differential equations include the socalled CrankNicolson, Du FortFrankel, and Laasonen methods.