22: Darts_Library.ipynb

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        "## Time Series Using Darts Library"
      ]
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        "id": "1aH2M_mvJPix"
      },
      "source": [
        "Darts documentation : https://pypi.org/project/darts/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JrV6QYnIb6N5"
      },
      "source": [
        "!pip install darts"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o1zu31VHdNOK"
      },
      "source": [
        "from darts.datasets import AirPassengersDataset, MonthlyMilkDataset"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
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      },
      "source": [
        "AirPassengersDataset().load()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
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              "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (Month: 144, component: 1, sample: 1)&gt;\n",
              "array([[[112.]],\n",
              "\n",
              "       [[118.]],\n",
              "\n",
              "       [[132.]],\n",
              "\n",
              "       [[129.]],\n",
              "\n",
              "       [[121.]],\n",
              "\n",
              "       [[135.]],\n",
              "\n",
              "       [[148.]],\n",
              "\n",
              "       [[148.]],\n",
              "\n",
              "       [[136.]],\n",
              "\n",
              "       [[119.]],\n",
              "\n",
              "...\n",
              "\n",
              "       [[419.]],\n",
              "\n",
              "       [[461.]],\n",
              "\n",
              "       [[472.]],\n",
              "\n",
              "       [[535.]],\n",
              "\n",
              "       [[622.]],\n",
              "\n",
              "       [[606.]],\n",
              "\n",
              "       [[508.]],\n",
              "\n",
              "       [[461.]],\n",
              "\n",
              "       [[390.]],\n",
              "\n",
              "       [[432.]]])\n",
              "Coordinates:\n",
              "  * Month      (Month) datetime64[ns] 1949-01-01 1949-02-01 ... 1960-12-01\n",
              "  * component  (component) object &#x27;#Passengers&#x27;\n",
              "Dimensions without coordinates: sample</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>Month</span>: 144</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b9f23501-da83-4296-9037-46e743939aff' class='xr-array-in' type='checkbox' checked><label for='section-b9f23501-da83-4296-9037-46e743939aff' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>112.0 118.0 132.0 129.0 121.0 135.0 ... 606.0 508.0 461.0 390.0 432.0</span></div><div class='xr-array-data'><pre>array([[[112.]],\n",
              "\n",
              "       [[118.]],\n",
              "\n",
              "       [[132.]],\n",
              "\n",
              "       [[129.]],\n",
              "\n",
              "       [[121.]],\n",
              "\n",
              "       [[135.]],\n",
              "\n",
              "       [[148.]],\n",
              "\n",
              "       [[148.]],\n",
              "\n",
              "       [[136.]],\n",
              "\n",
              "       [[119.]],\n",
              "\n",
              "...\n",
              "\n",
              "       [[419.]],\n",
              "\n",
              "       [[461.]],\n",
              "\n",
              "       [[472.]],\n",
              "\n",
              "       [[535.]],\n",
              "\n",
              "       [[622.]],\n",
              "\n",
              "       [[606.]],\n",
              "\n",
              "       [[508.]],\n",
              "\n",
              "       [[461.]],\n",
              "\n",
              "       [[390.]],\n",
              "\n",
              "       [[432.]]])</pre></div></div></li><li class='xr-section-item'><input id='section-8bed8a95-7d3a-4289-bf7f-71c7e82a25c9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8bed8a95-7d3a-4289-bf7f-71c7e82a25c9' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Month</span></div><div class='xr-var-dims'>(Month)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1949-01-01 ... 1960-12-01</div><input id='attrs-da8ed524-6294-4868-9297-92a57801d53b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-da8ed524-6294-4868-9297-92a57801d53b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0e5df34-57d3-4a13-a477-ee4046119977' class='xr-var-data-in' type='checkbox'><label for='data-a0e5df34-57d3-4a13-a477-ee4046119977' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1949-01-01T00:00:00.000000000&#x27;, &#x27;1949-02-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1949-03-01T00:00:00.000000000&#x27;, &#x27;1949-04-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1949-05-01T00:00:00.000000000&#x27;, &#x27;1949-06-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1949-07-01T00:00:00.000000000&#x27;, &#x27;1949-08-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1949-09-01T00:00:00.000000000&#x27;, &#x27;1949-10-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1949-11-01T00:00:00.000000000&#x27;, &#x27;1949-12-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1950-01-01T00:00:00.000000000&#x27;, &#x27;1950-02-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1950-03-01T00:00:00.000000000&#x27;, &#x27;1950-04-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1950-05-01T00:00:00.000000000&#x27;, &#x27;1950-06-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1950-07-01T00:00:00.000000000&#x27;, &#x27;1950-08-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1950-09-01T00:00:00.000000000&#x27;, &#x27;1950-10-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1950-11-01T00:00:00.000000000&#x27;, &#x27;1950-12-01T00:00:00.000000000&#x27;,\n",
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              "       &#x27;1954-07-01T00:00:00.000000000&#x27;, &#x27;1954-08-01T00:00:00.000000000&#x27;,\n",
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              "       &#x27;1955-07-01T00:00:00.000000000&#x27;, &#x27;1955-08-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1955-09-01T00:00:00.000000000&#x27;, &#x27;1955-10-01T00:00:00.000000000&#x27;,\n",
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              "       &#x27;1956-03-01T00:00:00.000000000&#x27;, &#x27;1956-04-01T00:00:00.000000000&#x27;,\n",
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              "       &#x27;1956-07-01T00:00:00.000000000&#x27;, &#x27;1956-08-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1956-09-01T00:00:00.000000000&#x27;, &#x27;1956-10-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1956-11-01T00:00:00.000000000&#x27;, &#x27;1956-12-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1957-01-01T00:00:00.000000000&#x27;, &#x27;1957-02-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1957-03-01T00:00:00.000000000&#x27;, &#x27;1957-04-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1957-05-01T00:00:00.000000000&#x27;, &#x27;1957-06-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1957-07-01T00:00:00.000000000&#x27;, &#x27;1957-08-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1957-09-01T00:00:00.000000000&#x27;, &#x27;1957-10-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1957-11-01T00:00:00.000000000&#x27;, &#x27;1957-12-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1958-01-01T00:00:00.000000000&#x27;, &#x27;1958-02-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1958-03-01T00:00:00.000000000&#x27;, &#x27;1958-04-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1958-05-01T00:00:00.000000000&#x27;, &#x27;1958-06-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1958-07-01T00:00:00.000000000&#x27;, &#x27;1958-08-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1958-09-01T00:00:00.000000000&#x27;, &#x27;1958-10-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1958-11-01T00:00:00.000000000&#x27;, &#x27;1958-12-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1959-01-01T00:00:00.000000000&#x27;, &#x27;1959-02-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1959-03-01T00:00:00.000000000&#x27;, &#x27;1959-04-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1959-05-01T00:00:00.000000000&#x27;, &#x27;1959-06-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1959-07-01T00:00:00.000000000&#x27;, &#x27;1959-08-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1959-09-01T00:00:00.000000000&#x27;, &#x27;1959-10-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1959-11-01T00:00:00.000000000&#x27;, &#x27;1959-12-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1960-03-01T00:00:00.000000000&#x27;, &#x27;1960-04-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1960-05-01T00:00:00.000000000&#x27;, &#x27;1960-06-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1960-07-01T00:00:00.000000000&#x27;, &#x27;1960-08-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1960-09-01T00:00:00.000000000&#x27;, &#x27;1960-10-01T00:00:00.000000000&#x27;,\n",
              "       &#x27;1960-11-01T00:00:00.000000000&#x27;, &#x27;1960-12-01T00:00:00.000000000&#x27;],\n",
              "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;#Passengers&#x27;</div><input id='attrs-08848aa3-a706-4e05-ab0a-fa944e993b46' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08848aa3-a706-4e05-ab0a-fa944e993b46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc3a9c26-3ba8-4efa-b26e-77b44f40f43a' class='xr-var-data-in' type='checkbox'><label for='data-dc3a9c26-3ba8-4efa-b26e-77b44f40f43a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;#Passengers&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-674e19e6-fc1b-4c37-b765-a7c04b0e5af1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-674e19e6-fc1b-4c37-b765-a7c04b0e5af1' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
            ],
            "text/plain": [
              "<TimeSeries (DataArray) (Month: 144, component: 1, sample: 1)>\n",
              "array([[[112.]],\n",
              "\n",
              "       [[118.]],\n",
              "\n",
              "       [[132.]],\n",
              "\n",
              "       [[129.]],\n",
              "\n",
              "       [[121.]],\n",
              "\n",
              "       [[135.]],\n",
              "\n",
              "       [[148.]],\n",
              "\n",
              "       [[148.]],\n",
              "\n",
              "       [[136.]],\n",
              "\n",
              "       [[119.]],\n",
              "\n",
              "...\n",
              "\n",
              "       [[419.]],\n",
              "\n",
              "       [[461.]],\n",
              "\n",
              "       [[472.]],\n",
              "\n",
              "       [[535.]],\n",
              "\n",
              "       [[622.]],\n",
              "\n",
              "       [[606.]],\n",
              "\n",
              "       [[508.]],\n",
              "\n",
              "       [[461.]],\n",
              "\n",
              "       [[390.]],\n",
              "\n",
              "       [[432.]]])\n",
              "Coordinates:\n",
              "  * Month      (Month) datetime64[ns] 1949-01-01 1949-02-01 ... 1960-12-01\n",
              "  * component  (component) object '#Passengers'\n",
              "Dimensions without coordinates: sample"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XG9C9ZmrhLmd",
        "outputId": "7de8542f-fdfe-4cd8-f058-50fae76b3cda"
      },
      "source": [
        "AirPassengersDataset().load().pd_series()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Month\n",
              "1949-01-01    112.0\n",
              "1949-02-01    118.0\n",
              "1949-03-01    132.0\n",
              "1949-04-01    129.0\n",
              "1949-05-01    121.0\n",
              "              ...  \n",
              "1960-08-01    606.0\n",
              "1960-09-01    508.0\n",
              "1960-10-01    461.0\n",
              "1960-11-01    390.0\n",
              "1960-12-01    432.0\n",
              "Freq: MS, Length: 144, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "nQJi0fHNeXZd",
        "outputId": "be41b5e2-5043-4f14-aba0-d02b89ae9b1c"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "series_air = AirPassengersDataset().load()\n",
        "series_milk = MonthlyMilkDataset().load()\n",
        "\n",
        "series_air.plot(label='Number of air passengers')\n",
        "series_milk.plot(label='Pounds of milk produced per cow')\n",
        "plt.legend();"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "UJM87PUre0sS",
        "outputId": "6696d868-3753-4214-8e84-7b83d6f15245"
      },
      "source": [
        "from darts.dataprocessing.transformers import Scaler\n",
        "scaler_air, scaler_milk = Scaler(), Scaler()\n",
        "series_air_scaled = scaler_air.fit_transform(series_air)\n",
        "series_milk_scaled = scaler_milk.fit_transform(series_milk)\n",
        "\n",
        "series_air_scaled.plot(label='air')\n",
        "series_milk_scaled.plot(label='milk')\n",
        "plt.legend();"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FbipyAOge81X"
      },
      "source": [
        "# Train And Validation Split\n",
        "train_air, val_air = series_air_scaled[:-36], series_air_scaled[-36:]\n",
        "train_milk, val_milk = series_milk_scaled[:-36], series_milk_scaled[-36:]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wqO_4ZPFfFNO"
      },
      "source": [
        "from darts import TimeSeries\n",
        "from darts.utils.timeseries_generation import gaussian_timeseries, linear_timeseries, sine_timeseries\n",
        "from darts.models import RNNModel, TCNModel, TransformerModel, NBEATSModel, BlockRNNModel\n",
        "from darts.metrics import mape, smape"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ddZUeqScffM9"
      },
      "source": [
        "model_air_milk = NBEATSModel(input_chunk_length=24, output_chunk_length=12, n_epochs=100, random_state=0)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WcRZdhngfipf",
        "outputId": "58e29105-2ed7-465e-c204-e1dc4a14aaaf"
      },
      "source": [
        "model_air_milk.fit([train_air, train_milk], verbose=True)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[2021-10-01 09:21:08,165] INFO | darts.models.forecasting.torch_forecasting_model | Train dataset contains 194 samples.\n",
            "[2021-10-01 09:21:08,165] INFO | darts.models.forecasting.torch_forecasting_model | Train dataset contains 194 samples.\n",
            "[2021-10-01 09:21:08,304] INFO | darts.models.forecasting.torch_forecasting_model | Time series values are 64-bits; casting model to float64. If training is too slow you can try casting your data to 32-bits.\n",
            "[2021-10-01 09:21:08,304] INFO | darts.models.forecasting.torch_forecasting_model | Time series values are 64-bits; casting model to float64. If training is too slow you can try casting your data to 32-bits.\n",
            "  1%|          | 1/100 [00:02<04:02,  2.45s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training loss: 0.3964\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 11%|█         | 11/100 [00:22<02:52,  1.94s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training loss: 0.0029\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 21%|██        | 21/100 [00:45<03:25,  2.60s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training loss: 0.0025\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 31%|███       | 31/100 [01:20<03:57,  3.44s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training loss: 0.0011\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 41%|████      | 41/100 [01:55<02:57,  3.01s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training loss: 0.0010\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 51%|█████     | 51/100 [02:30<02:30,  3.08s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training loss: 0.0041\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 61%|██████    | 61/100 [03:06<02:12,  3.40s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training loss: 0.0008\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 71%|███████   | 71/100 [03:41<01:37,  3.37s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training loss: 0.0007\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 81%|████████  | 81/100 [04:10<00:43,  2.30s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training loss: 0.0008\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 91%|█████████ | 91/100 [04:29<00:17,  1.89s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training loss: 0.0007\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 100/100 [04:46<00:00,  2.86s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "3OMRxLQifkjB",
        "outputId": "c36559ed-9d83-40d7-cc15-fc09cd51410f"
      },
      "source": [
        "pred = model_air_milk.predict(n=36, series=train_air)\n",
        "\n",
        "series_air_scaled.plot(label='actual')\n",
        "pred.plot(label='forecast')\n",
        "plt.legend();\n",
        "print('MAPE = {:.2f}%'.format(mape(series_air_scaled, pred)))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAPE = 5.51%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "m6yNqoPjiMji",
        "outputId": "7a4cadca-246d-4a6e-c187-aa552feb5360"
      },
      "source": [
        "pred = model_air_milk.predict(n=36, series=train_milk)\n",
        "\n",
        "series_milk_scaled.plot(label='actual')\n",
        "pred.plot(label='forecast')\n",
        "plt.legend();\n",
        "print('MAPE = {:.2f}%'.format(mape(series_milk_scaled, pred)))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAPE = 11.75%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2zc-J3WKCYXg"
      },
      "source": [
        "-----------------------------"
      ]
    }
  ]
}