Univariate_Time_Series_Forecasting_Using_LSTM (1).ipynb

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    "kernelspec": {
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      "language": "python",
      "name": "python3"
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    "language_info": {
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    "colab": {
      "name": "Univariate_Time_Series_Forecasting_Using_LSTM.ipynb",
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  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "_CDxOi-aOBYz"
      },
      "source": [
        "import numpy as np\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten, LSTM"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "roeKZBGtOI5a"
      },
      "source": [
        "def prepare_data(timeseries_data, n_features):\n",
        "\tX, y =[],[]\n",
        "\tfor i in range(len(timeseries_data)):\n",
        "\t\tend_ix = i + n_features\n",
        "\t\tif end_ix > len(timeseries_data)-1:\n",
        "\t\t\tbreak\n",
        "\t\tseq_x, seq_y = timeseries_data[i:end_ix], timeseries_data[end_ix]\n",
        "\t\tX.append(seq_x)\n",
        "\t\ty.append(seq_y)\n",
        "\treturn np.array(X), np.array(y)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OxeUaDB0OS46"
      },
      "source": [
        "timeseries_data = [110, 135, 143, 156, 148, 172, 175, 168, 190]\n",
        "n_steps = 3\n",
        "X, y = prepare_data(timeseries_data, n_steps)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ek0Vr-6UOf2x",
        "outputId": "85b8f816-4d9a-4f30-92a9-467da8f55a22"
      },
      "source": [
        "X"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[110, 135, 143],\n",
              "       [135, 143, 156],\n",
              "       [143, 156, 148],\n",
              "       [156, 148, 172],\n",
              "       [148, 172, 175],\n",
              "       [172, 175, 168]])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VCUYmSiXOgVC",
        "outputId": "8e2d76e8-0691-4300-9018-b1a9c7d6e7ea"
      },
      "source": [
        "y"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([156, 148, 172, 175, 168, 190])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ic9C1AR2Ohrh",
        "outputId": "809907d5-1aa8-42f9-c312-933b4543538f"
      },
      "source": [
        "X.shape, y.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((6, 3), (6,))"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5MEQwGmAOlaa"
      },
      "source": [
        "n_features = 1\n",
        "X = X.reshape((X.shape[0], X.shape[1], n_features))"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ruOe2Oa6OqaC",
        "outputId": "b6d49ce0-52b9-49d8-c07b-8bb126151cdf"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(128, activation='relu', return_sequences=True, input_shape=(n_steps, n_features)))\n",
        "model.add(LSTM(256, activation='relu'))\n",
        "model.add(Dense(1))\n",
        "model.compile(optimizer='adam', loss='mse')\n",
        "model.fit(X, y, epochs= 1000)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "1/1 [==============================] - 0s 35ms/step - loss: 1.1902e-05\n",
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            "1/1 [==============================] - 0s 44ms/step - loss: 1.7664e-05\n",
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            "1/1 [==============================] - 0s 36ms/step - loss: 1.2804e-05\n",
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            "1/1 [==============================] - 0s 32ms/step - loss: 3.6029e-06\n",
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            "1/1 [==============================] - 0s 41ms/step - loss: 9.4878e-08\n",
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            "1/1 [==============================] - 0s 37ms/step - loss: 4.3908e-06\n",
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            "1/1 [==============================] - 0s 32ms/step - loss: 9.9017e-06\n",
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            "1/1 [==============================] - 0s 36ms/step - loss: 9.9654e-06\n",
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            "1/1 [==============================] - 0s 37ms/step - loss: 5.0662e-06\n",
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            "1/1 [==============================] - 0s 26ms/step - loss: 7.2829e-07\n",
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            "1/1 [==============================] - 0s 27ms/step - loss: 6.1673e-07\n",
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            "1/1 [==============================] - 0s 31ms/step - loss: 3.5001e-06\n",
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            "1/1 [==============================] - 0s 35ms/step - loss: 5.5268e-06\n",
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            "1/1 [==============================] - 0s 32ms/step - loss: 1.0357e-07\n",
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            "1/1 [==============================] - 0s 32ms/step - loss: 7.7568e-07\n",
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            "1/1 [==============================] - 0s 31ms/step - loss: 2.4479e-06\n",
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            "1/1 [==============================] - 0s 26ms/step - loss: 3.0287e-06\n",
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            "1/1 [==============================] - 0s 41ms/step - loss: 2.0033e-06\n",
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            "1/1 [==============================] - 0s 44ms/step - loss: 5.8503e-07\n",
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            "1/1 [==============================] - 0s 38ms/step - loss: 5.1650e-08\n",
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            "1/1 [==============================] - 0s 42ms/step - loss: 1.6460e-06\n",
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            "1/1 [==============================] - 0s 47ms/step - loss: 4.2860e-07\n",
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            "1/1 [==============================] - 0s 38ms/step - loss: 2.9026e-08\n",
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            "1/1 [==============================] - 0s 39ms/step - loss: 3.0427e-07\n",
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            "1/1 [==============================] - 0s 33ms/step - loss: 8.2907e-07\n",
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            "1/1 [==============================] - 0s 42ms/step - loss: 3.6698e-07\n",
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            "1/1 [==============================] - 0s 24ms/step - loss: 6.3485e-08\n",
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            "1/1 [==============================] - 0s 29ms/step - loss: 6.4416e-09\n",
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            "1/1 [==============================] - 0s 31ms/step - loss: 1.2720e-07\n",
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            "1/1 [==============================] - 0s 37ms/step - loss: 4.2414e-08\n",
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            "1/1 [==============================] - 0s 41ms/step - loss: 3.3760e-08\n",
            "Epoch 959/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 3.6011e-08\n",
            "Epoch 960/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 2.8832e-08\n",
            "Epoch 961/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 1.3815e-08\n",
            "Epoch 962/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 2.3671e-09\n",
            "Epoch 963/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 2.9492e-09\n",
            "Epoch 964/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 8.5759e-09\n",
            "Epoch 965/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 7.4118e-09\n",
            "Epoch 966/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 1.2030e-09\n",
            "Epoch 967/1000\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 2.1343e-09\n",
            "Epoch 968/1000\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 1.0788e-08\n",
            "Epoch 969/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 1.6221e-08\n",
            "Epoch 970/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 1.9403e-08\n",
            "Epoch 971/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 1.8820e-08\n",
            "Epoch 972/1000\n",
            "1/1 [==============================] - 0s 35ms/step - loss: 1.3892e-08\n",
            "Epoch 973/1000\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 4.5402e-09\n",
            "Epoch 974/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 1.0477e-09\n",
            "Epoch 975/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 1.2689e-08\n",
            "Epoch 976/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 2.4564e-08\n",
            "Epoch 977/1000\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 3.5313e-08\n",
            "Epoch 978/1000\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 3.8960e-08\n",
            "Epoch 979/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 3.8650e-08\n",
            "Epoch 980/1000\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 3.1704e-08\n",
            "Epoch 981/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 2.0955e-08\n",
            "Epoch 982/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 8.2267e-09\n",
            "Epoch 983/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 5.0447e-10\n",
            "Epoch 984/1000\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 3.6089e-09\n",
            "Epoch 985/1000\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 7.6446e-09\n",
            "Epoch 986/1000\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 4.3462e-09\n",
            "Epoch 987/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 1.5522e-09\n",
            "Epoch 988/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 2.4835e-09\n",
            "Epoch 989/1000\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 7.1789e-09\n",
            "Epoch 990/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 1.2068e-08\n",
            "Epoch 991/1000\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 1.4785e-08\n",
            "Epoch 992/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 1.7074e-08\n",
            "Epoch 993/1000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 1.3543e-08\n",
            "Epoch 994/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 6.9461e-09\n",
            "Epoch 995/1000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 1.7462e-09\n",
            "Epoch 996/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 1.6686e-09\n",
            "Epoch 997/1000\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 1.3853e-08\n",
            "Epoch 998/1000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 4.0047e-08\n",
            "Epoch 999/1000\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 7.7571e-08\n",
            "Epoch 1000/1000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 1.2220e-07\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f0503fccd10>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x1oavtQ9O_L7",
        "outputId": "669d08c4-4b82-4873-9762-aaf1ac9b0170"
      },
      "source": [
        "x_input = np.array([175, 168, 190])\n",
        "temp_input=list(x_input)\n",
        "lst_output=[]\n",
        "i=0\n",
        "while(i<10):\n",
        "    \n",
        "    if(len(temp_input)>3):\n",
        "        x_input = np.array(temp_input[1:])\n",
        "        print(\"{} day input {}\".format(i,x_input))\n",
        "        x_input = x_input.reshape((1, n_steps, n_features))\n",
        "        yhat = model.predict(x_input, verbose=0)\n",
        "        print(\"{} day output {}\".format(i,yhat))\n",
        "        temp_input.append(yhat[0][0])\n",
        "        temp_input=temp_input[1:]\n",
        "        lst_output.append(yhat[0][0])\n",
        "        i=i+1\n",
        "    else:\n",
        "        x_input = x_input.reshape((1, n_steps, n_features))\n",
        "        yhat = model.predict(x_input, verbose=0)\n",
        "        print(yhat[0])\n",
        "        temp_input.append(yhat[0][0])\n",
        "        lst_output.append(yhat[0][0])\n",
        "        i=i+1\n",
        "\n",
        "print(lst_output)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[197.56161]\n",
            "1 day input [168.         190.         197.56161499]\n",
            "1 day output [[194.0475]]\n",
            "2 day input [190.         197.56161499 194.04750061]\n",
            "2 day output [[212.31322]]\n",
            "3 day input [197.56161 194.0475  212.31322]\n",
            "3 day output [[223.49396]]\n",
            "4 day input [194.0475  212.31322 223.49396]\n",
            "4 day output [[224.70741]]\n",
            "5 day input [212.31322 223.49396 224.70741]\n",
            "5 day output [[239.94589]]\n",
            "6 day input [223.49396 224.70741 239.94589]\n",
            "6 day output [[253.61072]]\n",
            "7 day input [224.70741 239.94589 253.61072]\n",
            "7 day output [[259.68387]]\n",
            "8 day input [239.94589 253.61072 259.68387]\n",
            "8 day output [[273.83444]]\n",
            "9 day input [253.61072 259.68387 273.83444]\n",
            "9 day output [[289.13782]]\n",
            "[197.56161, 194.0475, 212.31322, 223.49396, 224.70741, 239.94589, 253.61072, 259.68387, 273.83444, 289.13782]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9d2Du__MPd_a"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "day_new = np.arange(1,10)\n",
        "day_pred = np.arange(10,20)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OsIA7vQkPsby",
        "outputId": "38b15da5-9a11-4a33-ad4d-e94ef2ed4123",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.plot(day_new,timeseries_data)\n",
        "plt.plot(day_pred,lst_output)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0503b96790>]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}