diff --git a/docs/src/weights.md b/docs/src/weights.md
index e8322bfaa..424d207f9 100644
--- a/docs/src/weights.md
+++ b/docs/src/weights.md
@@ -41,6 +41,16 @@ w = ProbabilityWeights([0.2, 0.1, 0.3])
 w = pweights([0.2, 0.1, 0.3])
 ```
 
+### `ExponentialWeights`
+
+Exponential weights are a common form of temporal weights which assign exponentially decreasing
+weight to past observations.
+
+```julia
+w = ExponentialWeights([0.1837, 0.2222, 0.2688, 0.3253])
+w = eweights(4, 0.173) # construction based on length and rate parameter
+```
+
 ### `Weights`
 
 The `Weights` type describes a generic weights vector which does not support all operations possible for `FrequencyWeights`, `AnalyticWeights` and `ProbabilityWeights`.
@@ -66,9 +76,11 @@ The following constructors are provided:
 AnalyticWeights
 FrequencyWeights
 ProbabilityWeights
+ExponentialWeights
 Weights
 aweights
 fweights
 pweights
+eweights
 weights
-```
\ No newline at end of file
+```
diff --git a/src/StatsBase.jl b/src/StatsBase.jl
index af6a47aa8..46cd90aad 100644
--- a/src/StatsBase.jl
+++ b/src/StatsBase.jl
@@ -30,10 +30,12 @@ export
     AnalyticWeights,    # to represent an analytic/precision/reliability weight vector
     FrequencyWeights,   # to representing a frequency/case/repeat weight vector
     ProbabilityWeights, # to representing a probability/sampling weight vector
+    ExponentialWeights, # to represent an exponential weight vector
     weights,            # construct a generic Weights vector
     aweights,           # construct an AnalyticWeights vector
     fweights,           # construct a FrequencyWeights vector
     pweights,           # construct a ProbabilityWeights vector
+    eweights,           # construct an ExponentialWeights vector
     wsum,               # weighted sum with vector as second argument
     wsum!,              # weighted sum across dimensions with provided storage
     wmean,              # weighted mean
diff --git a/src/weights.jl b/src/weights.jl
index 174753370..0e2f6af2e 100644
--- a/src/weights.jl
+++ b/src/weights.jl
@@ -193,9 +193,59 @@ pweights(vs::RealArray) = ProbabilityWeights(vec(vs))
     end
 end
 
+@weights ExponentialWeights
+
+@doc """
+    ExponentialWeights(vs, wsum=sum(vs))
+
+Construct an `ExponentialWeights` vector with weight values `vs`.
+A precomputed sum may be provided as `wsum`.
+
+Exponential weights are a common form of temporal weights which assign exponentially
+decreasing weight to past observations, which in this case corresponds to the front of
+the vector. That is, newer observations are assumed to be at the end.
+""" ExponentialWeights
+
+"""
+    eweights(n, λ)
+
+Construct an [`ExponentialWeights`](@ref) vector with length `n`,
+where each element in position ``i`` is set to ``λ (1 - λ)^{1 - i}``.
+
+``λ`` is a smoothing factor or rate parameter such that ``0 < λ \\leq 1``.
+As this value approaches 0, the resulting weights will be almost equal,
+while values closer to 1 will put greater weight on the tail elements of the vector.
+
+# Examples
+
+```julia-repl
+julia> eweights(10, 0.3)
+10-element ExponentialWeights{Float64,Float64,Array{Float64,1}}:
+ 0.3
+ 0.42857142857142855
+ 0.6122448979591837
+ 0.8746355685131197
+ 1.249479383590171
+ 1.7849705479859588
+ 2.549957925694227
+ 3.642797036706039
+ 5.203995766722913
+ 7.434279666747019
+```
+"""
+function eweights(n::Integer, λ::Real)
+    n > 0 || throw(ArgumentError("cannot construct exponential weights of length < 1"))
+    0 < λ <= 1 || throw(ArgumentError("smoothing factor must be between 0 and 1"))
+    w0 = map(i -> λ * (1 - λ)^(1 - i), 1:n)
+    s = sum(w0)
+    ExponentialWeights{typeof(s), eltype(w0), typeof(w0)}(w0, s)
+end
+
+# NOTE: No variance correction is implemented for exponential weights
+
 ##### Equality tests #####
 
-for w in (AnalyticWeights, FrequencyWeights, ProbabilityWeights, Weights)
+for w in (AnalyticWeights, FrequencyWeights, ProbabilityWeights, ExponentialWeights, Weights)
     @eval begin
         Base.isequal(x::$w, y::$w) = isequal(x.sum, y.sum) && isequal(x.values, y.values)
         Base.:(==)(x::$w, y::$w)   = (x.sum == y.sum) && (x.values == y.values)
diff --git a/test/weights.jl b/test/weights.jl
index fa8f40be4..ecd07f90d 100644
--- a/test/weights.jl
+++ b/test/weights.jl
@@ -2,6 +2,8 @@ using StatsBase
 using LinearAlgebra, Random, SparseArrays, Test
 
 @testset "StatsBase.Weights" begin
+# NOTE: Do not add eweights here, as its methods don't match those of the others, so the
+# tests below don't make sense for it
 weight_funcs = (weights, aweights, fweights, pweights)
 
 # Construction
@@ -447,4 +449,22 @@ end
     @test round(mean(Union{Int,Missing}[1,2], weights([1,2])), digits=3) ≈ 1.667
 end
 
+@testset "ExponentialWeights" begin
+    @testset "Basic Usage" begin
+        θ = 5.25
+        λ = 1 - exp(-1 / θ)     # simple conversion for the more common/readable method
+
+        v = [λ*(1-λ)^(1-i) for i = 1:4]
+        w = ExponentialWeights(v)
+
+        @test round.(w, digits=4) == [0.1734, 0.2098, 0.2539, 0.3071]
+        @test eweights(4, λ) ≈ w
+    end
+
+    @testset "Failure Conditions" begin
+        @test_throws ArgumentError eweights(0, 0.3)
+        @test_throws ArgumentError eweights(1, 1.1)
+    end
+end
+
 end # @testset StatsBase.Weights