diff --git a/src/armax.jl b/src/armax.jl
index 9773a2620d586277be05febc79fe84f216a68846..8ce9e24e156550e615dbc37ad823acca67a3d065 100644
--- a/src/armax.jl
+++ b/src/armax.jl
@@ -7,19 +7,22 @@ function ar(y::Vector{Float64}, na; λ = 0, normtype=2, verbose=false)
     if normtype == 2
         if λ == 0
             w = A\y_train
+            method = :LS
         else
             w = (A'A + λ*eye(size(A,2)))\A'y_train
+            method = :LS_reg
         end
     elseif normtype == 1
         w = Variable(size(A,2),1)
         problem = minimize(sum(abs(A*w-y_train )) + λ*norm(w))
         solve!(problem, SCSSolver(verbose=Int(verbose)))
         w = w.value[:]
+        method = :L1
     end
     prediction = A*w
     error = y_train - prediction
     model = AR(w,na)
-    result = FitResult(y_train,prediction,na, λ>0?(:LS_reg) :(:LS),λ)
+    result = FitResult(y_train,prediction,na, method,λ)
     return model, result
 end
 ar(iddata::IdData, na; λ = 0) = ar(iddata.y, na; λ = 0)
@@ -31,14 +34,17 @@ function arx(y::Vector{Float64}, u::VecOrMat{Float64}, na, nb; λ = 0, normtype=
     if normtype == 2
         if λ == 0
             w = A\y_train
+            method = :LS
         else
             w = (A'A + λ*eye(size(A,2)))\A'y_train
+            method = :LS_reg
         end
     elseif normtype == 1
         w = Variable(size(A,2),1)
         problem = minimize(sum(abs(A*w-y_train )) + λ*norm(w))
         solve!(problem, SCSSolver(verbose=Int(verbose)))
         w = w.value[:]
+        method = :L1
     end
     prediction = A*w
     error = y_train - prediction
@@ -51,7 +57,7 @@ function arx(y::Vector{Float64}, u::VecOrMat{Float64}, na, nb; λ = 0, normtype=
         si += nb[i]
     end
     model = ARX(w[1:na],b,na,nb)
-    result = FitResult(y_train,prediction,na, λ>0?(:LS_reg) :(:LS), λ)
+    result = FitResult(y_train,prediction,na, method, λ)
     return model, result
 end
 arx(iddata::IdData, na, nb; λ = 0) = arx(iddata.y, iddata.u, na, nb; λ = 0)