diff --git a/doc/examples/quick_start_example.ipynb b/doc/examples/quick_start_example.ipynb
index 76992e831..7e94dc0b9 100644
--- a/doc/examples/quick_start_example.ipynb
+++ b/doc/examples/quick_start_example.ipynb
@@ -18,7 +18,9 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -74,7 +76,9 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# this is a dataset from R\n",
@@ -110,7 +114,9 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from pyramid.arima import ARIMA\n",
@@ -128,7 +134,9 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "fit = ARIMA(order=(1, 1, 1), seasonal_order=None).fit(y=wineind)"
@@ -146,8 +154,70 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting ARIMA: order=(1, 1, 2) seasonal_order=(0, 1, 1, 12); AIC=3066.771, BIC=3085.794\n",
+      "Fitting ARIMA: order=(1, 1, 1) seasonal_order=(0, 1, 1, 12); AIC=3066.811, BIC=3082.663\n",
+      "Fitting ARIMA: order=(1, 1, 1) seasonal_order=(1, 1, 1, 12); AIC=3067.666, BIC=3086.688\n",
+      "Fitting ARIMA: order=(1, 1, 2) seasonal_order=(1, 1, 1, 12); AIC=3068.086, BIC=3090.280\n",
+      "Fitting ARIMA: order=(1, 1, 2) seasonal_order=(0, 1, 2, 12); AIC=3067.766, BIC=3089.959\n",
+      "Fitting ARIMA: order=(1, 1, 3) seasonal_order=(0, 1, 1, 12); AIC=3068.842, BIC=3091.036\n",
+      "Fitting ARIMA: order=(1, 1, 1) seasonal_order=(2, 1, 1, 12); AIC=3070.302, BIC=3092.495\n",
+      "Fitting ARIMA: order=(1, 1, 1) seasonal_order=(1, 1, 2, 12); AIC=3068.757, BIC=3090.951\n",
+      "Fitting ARIMA: order=(1, 1, 1) seasonal_order=(0, 1, 2, 12); AIC=3067.669, BIC=3086.692\n",
+      "Fitting ARIMA: order=(1, 1, 3) seasonal_order=(1, 1, 1, 12); AIC=3070.214, BIC=3095.578\n",
+      "Fitting ARIMA: order=(1, 1, 2) seasonal_order=(2, 1, 1, 12); AIC=3069.948, BIC=3095.312\n",
+      "Fitting ARIMA: order=(2, 1, 1) seasonal_order=(0, 1, 1, 12); AIC=3067.485, BIC=3086.508\n",
+      "Fitting ARIMA: order=(1, 1, 2) seasonal_order=(1, 1, 2, 12); AIC=3069.717, BIC=3095.081\n",
+      "Fitting ARIMA: order=(2, 1, 1) seasonal_order=(1, 1, 1, 12); AIC=3068.672, BIC=3090.865\n",
+      "Fitting ARIMA: order=(1, 1, 1) seasonal_order=(2, 1, 2, 12); AIC=3070.103, BIC=3095.467\n",
+      "Fitting ARIMA: order=(2, 1, 1) seasonal_order=(0, 1, 2, 12); AIC=3068.503, BIC=3090.696\n",
+      "Fitting ARIMA: order=(1, 1, 3) seasonal_order=(0, 1, 2, 12); AIC=3069.854, BIC=3095.218\n",
+      "Fitting ARIMA: order=(2, 1, 2) seasonal_order=(0, 1, 1, 12); AIC=3068.701, BIC=3090.895\n",
+      "Fitting ARIMA: order=(2, 1, 1) seasonal_order=(2, 1, 1, 12); AIC=3070.641, BIC=3096.005\n",
+      "Fitting ARIMA: order=(2, 1, 2) seasonal_order=(1, 1, 1, 12); AIC=3070.025, BIC=3095.389\n",
+      "Fitting ARIMA: order=(1, 1, 3) seasonal_order=(2, 1, 1, 12); AIC=3072.048, BIC=3100.582\n",
+      "Fitting ARIMA: order=(1, 1, 3) seasonal_order=(1, 1, 2, 12); AIC=3071.758, BIC=3100.293\n",
+      "Fitting ARIMA: order=(1, 1, 2) seasonal_order=(2, 1, 2, 12); AIC=3069.992, BIC=3098.526\n",
+      "Fitting ARIMA: order=(2, 1, 1) seasonal_order=(1, 1, 2, 12); AIC=3070.503, BIC=3095.866\n",
+      "Fitting ARIMA: order=(2, 1, 3) seasonal_order=(0, 1, 1, 12); AIC=3070.642, BIC=3096.006\n",
+      "Fitting ARIMA: order=(2, 1, 2) seasonal_order=(0, 1, 2, 12); AIC=3069.726, BIC=3095.090\n",
+      "Fitting ARIMA: order=(2, 1, 3) seasonal_order=(1, 1, 1, 12); AIC=3071.523, BIC=3100.057\n",
+      "Fitting ARIMA: order=(2, 1, 2) seasonal_order=(2, 1, 1, 12); AIC=3071.901, BIC=3100.435\n",
+      "Fitting ARIMA: order=(2, 1, 2) seasonal_order=(1, 1, 2, 12); AIC=3071.683, BIC=3100.218\n",
+      "Fitting ARIMA: order=(1, 1, 3) seasonal_order=(2, 1, 2, 12); AIC=3072.042, BIC=3103.747\n",
+      "Fitting ARIMA: order=(3, 1, 1) seasonal_order=(0, 1, 1, 12); AIC=3069.393, BIC=3091.586\n",
+      "Fitting ARIMA: order=(3, 1, 1) seasonal_order=(1, 1, 1, 12); AIC=3070.667, BIC=3096.031\n",
+      "Fitting ARIMA: order=(2, 1, 1) seasonal_order=(2, 1, 2, 12); AIC=3071.047, BIC=3099.581\n",
+      "Fitting ARIMA: order=(3, 1, 1) seasonal_order=(0, 1, 2, 12); AIC=3070.436, BIC=3095.800\n",
+      "Fitting ARIMA: order=(2, 1, 3) seasonal_order=(0, 1, 2, 12); AIC=3071.600, BIC=3100.135\n",
+      "Fitting ARIMA: order=(3, 1, 2) seasonal_order=(0, 1, 1, 12); AIC=nan, BIC=nan\n",
+      "Fitting ARIMA: order=(3, 1, 2) seasonal_order=(0, 1, 2, 12); AIC=nan, BIC=nan\n",
+      "Fitting ARIMA: order=(3, 1, 2) seasonal_order=(1, 1, 1, 12); AIC=nan, BIC=nan\n",
+      "Fitting ARIMA: order=(3, 1, 2) seasonal_order=(1, 1, 2, 12); AIC=nan, BIC=nan\n",
+      "Fitting ARIMA: order=(3, 1, 2) seasonal_order=(2, 1, 1, 12); AIC=nan, BIC=nan\n",
+      "Fitting ARIMA: order=(3, 1, 2) seasonal_order=(2, 1, 2, 12); AIC=nan, BIC=nan\n",
+      "Fitting ARIMA: order=(3, 1, 3) seasonal_order=(0, 1, 1, 12); AIC=3072.626, BIC=3101.160\n",
+      "Fitting ARIMA: order=(3, 1, 1) seasonal_order=(2, 1, 1, 12); AIC=3072.617, BIC=3101.151\n",
+      "Fitting ARIMA: order=(3, 1, 1) seasonal_order=(1, 1, 2, 12); AIC=3072.395, BIC=3100.930\n",
+      "Fitting ARIMA: order=(2, 1, 2) seasonal_order=(2, 1, 2, 12); AIC=3072.043, BIC=3103.748\n",
+      "Fitting ARIMA: order=(3, 1, 3) seasonal_order=(1, 1, 1, 12); AIC=3073.539, BIC=3105.244\n",
+      "Fitting ARIMA: order=(2, 1, 3) seasonal_order=(1, 1, 2, 12); AIC=3073.554, BIC=3105.259\n",
+      "Fitting ARIMA: order=(2, 1, 3) seasonal_order=(2, 1, 1, 12); AIC=3073.517, BIC=3105.222\n",
+      "Fitting ARIMA: order=(3, 1, 3) seasonal_order=(0, 1, 2, 12); AIC=3073.580, BIC=3105.285\n",
+      "Fitting ARIMA: order=(2, 1, 3) seasonal_order=(2, 1, 2, 12); AIC=3074.239, BIC=3109.114\n",
+      "Fitting ARIMA: order=(3, 1, 1) seasonal_order=(2, 1, 2, 12); AIC=3072.623, BIC=3104.328\n",
+      "Fitting ARIMA: order=(3, 1, 3) seasonal_order=(2, 1, 1, 12); AIC=3075.530, BIC=3110.405\n",
+      "Fitting ARIMA: order=(3, 1, 3) seasonal_order=(1, 1, 2, 12); AIC=3075.561, BIC=3110.437\n",
+      "Fitting ARIMA: order=(3, 1, 3) seasonal_order=(2, 1, 2, 12); AIC=3077.492, BIC=3115.537\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -163,7 +233,7 @@
        "  <th>Date:</th>                   <td>Fri, 02 Jun 2017</td>        <th>  AIC                </th> <td>3066.771</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                       <td>11:21:21</td>            <th>  BIC                </th> <td>3085.794</td> \n",
+       "  <th>Time:</th>                       <td>12:36:29</td>            <th>  BIC                </th> <td>3085.794</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Sample:</th>                         <td>0</td>               <th>  HQIC               </th> <td>3074.487</td> \n",
@@ -212,17 +282,51 @@
        "  <th>Prob(H) (two-sided):</th>    <td>0.54</td>  <th>  Kurtosis:          </th> <td>4.31</td> \n",
        "</tr>\n",
        "</table>"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                                 Statespace Model Results                                 \n",
+       "==========================================================================================\n",
+       "Dep. Variable:                                  y   No. Observations:                  176\n",
+       "Model:             SARIMAX(1, 1, 2)x(0, 1, 1, 12)   Log Likelihood               -1527.386\n",
+       "Date:                            Fri, 02 Jun 2017   AIC                           3066.771\n",
+       "Time:                                    12:36:29   BIC                           3085.794\n",
+       "Sample:                                         0   HQIC                          3074.487\n",
+       "                                            - 176                                         \n",
+       "Covariance Type:                              opg                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "intercept   -100.7446     72.306     -1.393      0.164    -242.462      40.973\n",
+       "ar.L1         -0.5139      0.390     -1.319      0.187      -1.278       0.250\n",
+       "ma.L1         -0.0791      0.403     -0.196      0.844      -0.869       0.710\n",
+       "ma.L2         -0.4438      0.223     -1.988      0.047      -0.881      -0.006\n",
+       "ma.S.L12      -0.4021      0.054     -7.448      0.000      -0.508      -0.296\n",
+       "sigma2      7.663e+06    7.3e+05     10.500      0.000    6.23e+06    9.09e+06\n",
+       "===================================================================================\n",
+       "Ljung-Box (Q):                       48.66   Jarque-Bera (JB):                21.62\n",
+       "Prob(Q):                              0.16   Prob(JB):                         0.00\n",
+       "Heteroskedasticity (H):               1.18   Skew:                            -0.61\n",
+       "Prob(H) (two-sided):                  0.54   Kurtosis:                         4.31\n",
+       "===================================================================================\n",
+       "\n",
+       "Warnings:\n",
+       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
+       "[2] Covariance matrix is singular or near-singular, with condition number 8.15e+14. Standard errors may be unstable.\n",
+       "\"\"\""
       ]
      },
-     "output_type": "execute_result",
-     "metadata": {}
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "from pyramid.arima import auto_arima\n",
     "\n",
     "fit = auto_arima(wineind, start_p=1, start_q=1, max_p=3, max_q=3, m=12,\n",
-    "                 start_P=0, seasonal=True, n_jobs=-1, d=1, D=1,\n",
+    "                 start_P=0, seasonal=True, n_jobs=-1, d=1, D=1, trace=True,\n",
     "                 error_action='ignore',  # don't want to know if an order does not work\n",
     "                 suppress_warnings=True)  # don't want convergence warnings\n",
     "\n",
@@ -240,8 +344,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -255,9 +361,9 @@
        "        16734.91315808])"
       ]
      },
-     "execution_count": 9,
-     "output_type": "execute_result",
-     "metadata": {}
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -274,8 +380,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -283,15 +391,102 @@
        "\n",
        "    <div class=\"bk-root\">\n",
        "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
-       "        <span id=\"a51e80eb-f243-47bc-b27f-f826f0820889\">Loading BokehJS ...</span>\n",
+       "        <span id=\"f70c02e8-0cb9-476a-ae7e-b303861dadc3\">Loading BokehJS ...</span>\n",
        "    </div>"
       ]
      },
-     "output_type": "execute_result",
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "metadata": {}
+     "data": {
+      "application/javascript": [
+       "\n",
+       "(function(global) {\n",
+       "  function now() {\n",
+       "    return new Date();\n",
+       "  }\n",
+       "\n",
+       "  if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n",
+       "    window._bokeh_onload_callbacks = [];\n",
+       "  }\n",
+       "\n",
+       "  function run_callbacks() {\n",
+       "    window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
+       "    delete window._bokeh_onload_callbacks\n",
+       "    console.info(\"Bokeh: all callbacks have finished\");\n",
+       "  }\n",
+       "\n",
+       "  function load_libs(js_urls, callback) {\n",
+       "    window._bokeh_onload_callbacks.push(callback);\n",
+       "    if (window._bokeh_is_loading > 0) {\n",
+       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
+       "      return null;\n",
+       "    }\n",
+       "    if (js_urls == null || js_urls.length === 0) {\n",
+       "      run_callbacks();\n",
+       "      return null;\n",
+       "    }\n",
+       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
+       "    window._bokeh_is_loading = js_urls.length;\n",
+       "    for (var i = 0; i < js_urls.length; i++) {\n",
+       "      var url = js_urls[i];\n",
+       "      var s = document.createElement('script');\n",
+       "      s.src = url;\n",
+       "      s.async = false;\n",
+       "      s.onreadystatechange = s.onload = function() {\n",
+       "        window._bokeh_is_loading--;\n",
+       "        if (window._bokeh_is_loading === 0) {\n",
+       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
+       "          run_callbacks()\n",
+       "        }\n",
+       "      };\n",
+       "      s.onerror = function() {\n",
+       "        console.warn(\"failed to load library \" + url);\n",
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+       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "    }\n",
+       "  };\n",
+       "\n",
+       "  var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.12.0.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.0.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.12.0.min.js'];\n",
+       "\n",
+       "  var inline_js = [\n",
+       "    function(Bokeh) {\n",
+       "      Bokeh.set_log_level(\"info\");\n",
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+       "    \n",
+       "    function(Bokeh) {\n",
+       "      Bokeh.$(\"#f70c02e8-0cb9-476a-ae7e-b303861dadc3\").text(\"BokehJS successfully loaded\");\n",
+       "    },\n",
+       "    function(Bokeh) {\n",
+       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.0.min.css\");\n",
+       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.0.min.css\");\n",
+       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.0.min.css\");\n",
+       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.0.min.css\");\n",
+       "    }\n",
+       "  ];\n",
+       "\n",
+       "  function run_inline_js() {\n",
+       "    for (var i = 0; i < inline_js.length; i++) {\n",
+       "      inline_js[i](window.Bokeh);\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  if (window._bokeh_is_loading === 0) {\n",
+       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
+       "    run_inline_js();\n",
+       "  } else {\n",
+       "    load_libs(js_urls, function() {\n",
+       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
+       "      run_inline_js();\n",
+       "    });\n",
+       "  }\n",
+       "}(this));"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -301,8 +496,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -310,7 +507,7 @@
        "\n",
        "\n",
        "    <div class=\"bk-root\">\n",
-       "        <div class=\"plotdiv\" id=\"4037d22e-c612-4597-a8b2-8911338b5703\"></div>\n",
+       "        <div class=\"plotdiv\" id=\"cd9bef5d-c09a-4115-8533-42d7d05a8532\"></div>\n",
        "    </div>\n",
        "<script type=\"text/javascript\">\n",
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@@ -359,9 +556,9 @@
        "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
        "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
        "      }\n",
-       "    };var element = document.getElementById(\"4037d22e-c612-4597-a8b2-8911338b5703\");\n",
+       "    };var element = document.getElementById(\"cd9bef5d-c09a-4115-8533-42d7d05a8532\");\n",
        "    if (element == null) {\n",
-       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '4037d22e-c612-4597-a8b2-8911338b5703' but no matching script tag was found. \")\n",
+       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'cd9bef5d-c09a-4115-8533-42d7d05a8532' but no matching script tag was found. \")\n",
        "      return false;\n",
        "    }\n",
        "  \n",
@@ -370,8 +567,8 @@
        "    var inline_js = [\n",
        "      function(Bokeh) {\n",
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+       "            var render_items = [{\"docid\":\"e321d229-71bd-43d0-b814-72ff8f590688\",\"elementid\":\"cd9bef5d-c09a-4115-8533-42d7d05a8532\",\"modelid\":\"222caa20-8349-4d64-9f5a-b46abd000492\",\"notebook_comms_target\":\"b1fbb03a-a317-450a-8b04-343e19a1c5af\"}];\n",
        "            \n",
        "            Bokeh.embed.embed_items(docs_json, render_items);\n",
        "        });\n",
@@ -399,17 +596,21 @@
        "</script>"
       ]
      },
-     "output_type": "execute_result",
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "data": {
       "text/html": [
-       "<p><code>&lt;Bokeh Notebook handle for <strong>In[11]</strong>&gt;</code></p>"
+       "<p><code>&lt;Bokeh Notebook handle for <strong>In[8]</strong>&gt;</code></p>"
+      ],
+      "text/plain": [
+       "<bokeh.io._CommsHandle at 0x1162bff50>"
       ]
      },
-     "output_type": "execute_result",
-     "metadata": {}
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -453,11 +654,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
-   "source": [
-    ""
-   ]
+   "source": []
   }
  ],
  "metadata": {
@@ -470,7 +671,7 @@
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2.0
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
@@ -482,4 +683,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/pyramid/arima/auto.py b/pyramid/arima/auto.py
index f739f4dd9..bc9ccf442 100644
--- a/pyramid/arima/auto.py
+++ b/pyramid/arima/auto.py
@@ -413,14 +413,11 @@ def generator():
 def _fit_arima(x, xreg, order, seasonal_order, start_params, trend, method, transparams,
                solver, maxiter, disp, callback, fit_params, suppress_warnings, trace,
                error_action):
-    if trace:
-        print('Fitting ARIMA: %s' % _fmt_order_info(order, seasonal_order))
-
     try:
-        return ARIMA(order=order, seasonal_order=seasonal_order, start_params=start_params,
-                     trend=trend, method=method, transparams=transparams,
-                     solver=solver, maxiter=maxiter, disp=disp,
-                     callback=callback, suppress_warnings=suppress_warnings)\
+        fit = ARIMA(order=order, seasonal_order=seasonal_order, start_params=start_params,
+                    trend=trend, method=method, transparams=transparams,
+                    solver=solver, maxiter=maxiter, disp=disp,
+                    callback=callback, suppress_warnings=suppress_warnings)\
             .fit(x, exogenous=xreg, **fit_params)
 
     # for non-stationarity errors, return None
@@ -430,8 +427,16 @@ def _fit_arima(x, xreg, order, seasonal_order, start_params, trend, method, tran
         elif error_action == 'raise':
             raise v
         # otherwise it's 'ignore'
-        return None
+        fit = None
+
+    # do trace
+    if trace:
+        print('Fit ARIMA: %s; AIC=%.3f, BIC=%.3f'
+              % (_fmt_order_info(order, seasonal_order),
+                 fit.aic() if fit is not None else np.nan,
+                 fit.bic() if fit is not None else np.nan))
 
+    return fit
 
 def _fmt_order_info(order, seasonal_order):
     return 'order=(%i, %i, %i)%s' \