In mathematics, a **telescoping series** is a series whose partial sums eventually only have a fixed number of terms after cancellation. The cancellation technique, with part of each term cancelling with part of the next term, is known as the **method of differences**.

For example, the series

∑
n
=
1
∞
1
n
(
n
+
1
)
(the series of reciprocals of pronic numbers) simplifies as

∑
n
=
1
∞
1
n
(
n
+
1
)
=
∑
n
=
1
∞
(
1
n
−
1
n
+
1
)
=
lim
N
→
∞
∑
n
=
1
N
(
1
n
−
1
n
+
1
)
=
lim
N
→
∞
[
(
1
−
1
2
)
+
(
1
2
−
1
3
)
+
⋯
+
(
1
N
−
1
N
+
1
)
]
=
lim
N
→
∞
[
1
+
(
−
1
2
+
1
2
)
+
(
−
1
3
+
1
3
)
+
⋯
+
(
−
1
N
+
1
N
)
−
1
N
+
1
]
=
lim
N
→
∞
[
1
−
1
N
+
1
]
=
1.
Let
a
n
be a sequence of numbers. Then,

∑
n
=
1
N
(
a
n
−
a
n
−
1
)
=
a
N
−
a
0
,
and, if
a
n
→
0

∑
n
=
1
∞
(
a
n
−
a
n
−
1
)
=
−
a
0
.
Many trigonometric functions also admit representation as a difference, which allows telescopic cancelling between the consecutive terms.
Some sums of the form
where

*f* and

*g* are polynomial functions whose quotient may be broken up into partial fractions, will fail to admit summation by this method. In particular, one has
The problem is that the terms do not cancel.

Let *k* be a positive integer. Then
where

*H*_{k} is the

*k*th harmonic number. All of the terms after 1/(

*k* − 1) cancel.

In probability theory, a Poisson process is a stochastic process of which the simplest case involves "occurrences" at random times, the waiting time until the next occurrence having a memoryless exponential distribution, and the number of "occurrences" in any time interval having a Poisson distribution whose expected value is proportional to the length of the time interval. Let *X*_{t} be the number of "occurrences" before time *t*, and let *T*_{x} be the waiting time until the *x*th "occurrence". We seek the probability density function of the random variable *T*_{x}. We use the probability mass function for the Poisson distribution, which tells us that

Pr
(
X
t
=
x
)
=
(
λ
t
)
x
e
−
λ
t
x
!
,
where λ is the average number of occurrences in any time interval of length 1. Observe that the event {*X*_{t} ≥ x} is the same as the event {*T*_{x} ≤ *t*}, and thus they have the same probability. The density function we seek is therefore

f
(
t
)
=
d
d
t
Pr
(
T
x
≤
t
)
=
d
d
t
Pr
(
X
t
≥
x
)
=
d
d
t
(
1
−
Pr
(
X
t
≤
x
−
1
)
)
=
d
d
t
(
1
−
∑
u
=
0
x
−
1
Pr
(
X
t
=
u
)
)
=
d
d
t
(
1
−
∑
u
=
0
x
−
1
(
λ
t
)
u
e
−
λ
t
u
!
)
=
λ
e
−
λ
t
−
e
−
λ
t
∑
u
=
1
x
−
1
(
λ
u
t
u
−
1
(
u
−
1
)
!
−
λ
u
+
1
t
u
u
!
)
The sum telescopes, leaving

f
(
t
)
=
λ
x
t
x
−
1
e
−
λ
t
(
x
−
1
)
!
.
For other applications, see:

Grandi's series;
Proof that the sum of the reciprocals of the primes diverges, where one of the proofs uses a telescoping sum;
Order statistic, where a telescoping sum occurs in the derivation of a probability density function;
Lefschetz fixed-point theorem, where a telescoping sum arises in algebraic topology;
Homology theory, again in algebraic topology;
Eilenberg–Mazur swindle, where a telescoping sum of knots occurs;
Faddeev–LeVerrier algorithm;
Fundamental theorem of calculus, a continuous analog of telescoping series.