diff --git a/src/signalcorr.jl b/src/signalcorr.jl
index 06c83ba1d..1553a5c37 100644
--- a/src/signalcorr.jl
+++ b/src/signalcorr.jl
@@ -629,3 +629,77 @@ end
 function pacf(x::AbstractVector{<:Real}, lags::AbstractVector{<:Integer}; method::Symbol=:regression)
     vec(pacf(reshape(x, length(x), 1), lags, method=method))
 end
+
+
+"""
+    yule_walker(x::Vector{<:Real};
+                order::Int64=1,
+                method="adjusted",
+                df::Union{Nothing,Int64}=nothing,
+                inv=false,
+                demean=true,
+    )
+
+Estimate AutoRegressive (AR) parameters using the Yule-Walker equations. This function
+estimates AR parameters using the Yule-Walker equations. It supports different ACF
+estimation methods (adjusted or maximum likelihood) and can optionally return the inverse
+of the Toeplitz matrix.
+
+Example:
+```julia
+data = [0.1, 0.2, 0.3, 0.4, 0.5]
+order = 2
+rho, sigma = yule_walker(data, order=order, method="mle")
+```
+"""
+function yule_walker(
+    x::Vector{<:Real};
+    order::Int64=1,
+    method="adjusted",
+    df::Union{Nothing,Int64}=nothing,
+    inv=false,
+    demean=true,
+)
+    method in ("adjusted", "mle") ||
+        throw(ArgumentError("ACF estimation method must be 'adjusted' or 'MLE'"))
+
+    x = copy(x)
+    if demean
+        x .-= mean(x)
+    end
+    n = isnothing(df) ? length(x) : df
+
+    adj_needed = method == "adjusted"
+
+    if ndims(x) > 1 || size(x, 2) != 1
+        throw(ArgumentError("Expecting a vector to estimate AR parameters"))
+    end
+
+    r = zeros(Float64, order + 1)
+    r[1] = sum(x .^ 2) / n
+    for k in 1:order
+        r[k + 1] = sum(x[1:(end - k)] .* x[(k + 1):end]) / (n - k * adj_needed)
+    end
+    R = Toeplitz(r[1:(end - 1)], conj(r[1:(end - 1)]))
+
+    rho = 0
+    try
+        rho = R \ r[2:end]
+    catch err
+        if occursin("Singular matrix", string(err))
+            @warn "Matrix is singular. Using pinv."
+            rho = pinv(R) * r[2:end]
+        else
+            throw(err)
+        end
+    end
+
+    sigmasq = r[1] - dot(r[2:end], rho)
+    sigma = isnan(sigmasq) || sigmasq <= 0 ? NaN : sqrt(sigmasq)
+
+    if inv
+        return rho, sigma, inv(R)
+    else
+        return rho, sigma
+    end
+end
diff --git a/test/signalcorr.jl b/test/signalcorr.jl
index bfbe90fed..8660df4c3 100644
--- a/test/signalcorr.jl
+++ b/test/signalcorr.jl
@@ -144,3 +144,86 @@ rpacfy = [-0.221173011668873,
           -0.175020669835420]
 
 @test pacf(x[:,1], 1:4, method=:yulewalker) ≈ rpacfy
+
+rho, sigma = yule_walker([1.0, 2, 3]; order=1)
+@test rho == [0.0]
+
+rho, sigma = yule_walker([1.0, 2, 3]; order=2)
+@test rho == [0.0, -1.5]
+
+x = [0.9901178, -0.74795127, 0.44612542, 1.1362954, -0.04040932]
+rho, sigma = yule_walker(x; order=3, method="mle")
+@test rho ≈ [-0.9418963, -0.90335955, -0.33267884]
+@test sigma ≈ 0.44006365345695164
+
+rho, sigma = yule_walker(x; order=3)
+@test isapprox(rho, [0.10959317, 0.05242324, 1.06587676], atol=tol)
+@test isapprox(sigma, 0.15860522671108127, atol=tol)
+
+rho, sigma = yule_walker(x; order=5, method="mle")
+@test isapprox(
+    rho, [-1.24209771, -1.56893346, -1.16951484, -0.79844781, -0.27598787], atol=tol
+)
+@test isapprox(sigma, 0.3679474002175471, atol=tol)
+
+x = [
+    0.9901178,
+    -0.74795127,
+    0.44612542,
+    1.1362954,
+    -0.04040932,
+    0.28625813,
+    0.88901716,
+    -0.1079814,
+    -0.33231995,
+    0.4607741,
+]
+
+rho, sigma = yule_walker(x; order=3, method="mle")
+@test isapprox(
+    rho, [-0.4896151627237206, -0.5724647370433921, 0.09083516892540627], atol=tol
+)
+@test isapprox(sigma, 0.4249693094713215, atol=tol)
+
+x = [
+    0.9901178,
+    -0.74795127,
+    0.44612542,
+    1.1362954,
+    -0.04040932,
+    0.28625813,
+    0.88901716,
+    -0.1079814,
+    -0.33231995,
+    0.4607741,
+    0.7729643,
+    -1.0998684,
+    1.098167,
+    1.0105597,
+    -1.3370227,
+    1.239718,
+    -0.01393661,
+    -0.4790918,
+    1.5009186,
+    -1.1647809,
+]
+
+rho, sigma = yule_walker(x; order=3, method="mle")
+@test isapprox(rho, [-0.82245705, -0.57029742, 0.12166898], atol=tol)
+@test isapprox(sigma, 0.5203501608988023, atol=tol)
+
+rho, sigma = yule_walker(x; order=3)
+@test isapprox(rho, [-0.93458149, -0.68653741, 0.10161722], atol=tol)
+@test isapprox(sigma, 0.4269012058667671, atol=tol)
+
+rho, sigma = yule_walker(x; order=5, method="mle")
+@test isapprox(
+    rho, [-0.83107755, -0.56407764, 0.20950143, 0.1232321, 0.10249279], atol=tol
+)
+@test isapprox(sigma, 0.5172269743102993, atol=tol)
+
+rho, sigma = yule_walker(x; order=5)
+@test isapprox(
+    rho, [-0.96481241, -0.65359486, 0.31587079, 0.28403115, 0.1913565], atol=tol
+)
+@test isapprox(sigma, 0.41677565377507053, atol=tol)