SET50_stock_PyTorch_LSTM.ipynb

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  "metadata": {
    "colab": {
      "name": "SET50_stock PyTorch LSTM.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOunEheoMuiyn5jtYczb6oq",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/maytaweej/SET50-stock-prediction/blob/main/SET50_stock_PyTorch_LSTM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GfH9jC_CVo7d",
        "outputId": "3c95c0ad-1278-4876-8b6f-66769ea7a8d1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "main_path = \"/content/drive/MyDrive/Colab Notebooks/\""
      ],
      "metadata": {
        "id": "TtEAD_NpVxVh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "rjX7RLWHWUcy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(main_path+'SET 50 Historical Data.csv')"
      ],
      "metadata": {
        "id": "9NxoI52cVxYD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "AkzyggzaVxaz",
        "outputId": "8472cc7e-a7ce-4615-8386-f871b5dc8736"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date   Price    Open    High     Low     Vol. Change %\n",
              "0  Jan 04, 2012  726.99  727.22  731.54  725.16  684.66K    1.20%\n",
              "1  Jan 05, 2012  727.12  726.45  730.39  723.50  827.35K    0.02%\n",
              "2  Jan 06, 2012  725.69  725.00  728.96  723.79  798.07K   -0.20%\n",
              "3  Jan 09, 2012  732.21  723.76  732.21  723.13  718.55K    0.90%\n",
              "4  Jan 10, 2012  738.13  734.53  740.07  733.90  882.74K    0.81%"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Price</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Vol.</th>\n",
              "      <th>Change %</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Jan 04, 2012</td>\n",
              "      <td>726.99</td>\n",
              "      <td>727.22</td>\n",
              "      <td>731.54</td>\n",
              "      <td>725.16</td>\n",
              "      <td>684.66K</td>\n",
              "      <td>1.20%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Jan 05, 2012</td>\n",
              "      <td>727.12</td>\n",
              "      <td>726.45</td>\n",
              "      <td>730.39</td>\n",
              "      <td>723.50</td>\n",
              "      <td>827.35K</td>\n",
              "      <td>0.02%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jan 06, 2012</td>\n",
              "      <td>725.69</td>\n",
              "      <td>725.00</td>\n",
              "      <td>728.96</td>\n",
              "      <td>723.79</td>\n",
              "      <td>798.07K</td>\n",
              "      <td>-0.20%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Jan 09, 2012</td>\n",
              "      <td>732.21</td>\n",
              "      <td>723.76</td>\n",
              "      <td>732.21</td>\n",
              "      <td>723.13</td>\n",
              "      <td>718.55K</td>\n",
              "      <td>0.90%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Jan 10, 2012</td>\n",
              "      <td>738.13</td>\n",
              "      <td>734.53</td>\n",
              "      <td>740.07</td>\n",
              "      <td>733.90</td>\n",
              "      <td>882.74K</td>\n",
              "      <td>0.81%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set_style(\"darkgrid\")\n",
        "plt.figure(figsize = (15,5))\n",
        "plt.plot(data[['Price']])\n",
        "plt.xticks(range(0,data.shape[0],500),data['Date'].loc[::500],rotation=45)\n",
        "plt.title(\"SET50 Stock Price\",fontsize=18, fontweight='bold')\n",
        "plt.xlabel('Date',fontsize=18)\n",
        "plt.ylabel('Price (THB)',fontsize=18)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "CGlYKhXWVxeX",
        "outputId": "5059d6b2-2137-4aeb-c4a2-93761d928361"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Normalize data**"
      ],
      "metadata": {
        "id": "yndDkdzmWgAu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "price = data[['Price']]\n",
        "price.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3uxVPJhgWa_F",
        "outputId": "1864c830-d99f-459b-a995-dc17bf07731b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2439 entries, 0 to 2438\n",
            "Data columns (total 1 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Price   2439 non-null   float64\n",
            "dtypes: float64(1)\n",
            "memory usage: 19.2 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
        "price['Price'] = scaler.fit_transform(price['Price'].values.reshape(-1,1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5jA52EY-WbED",
        "outputId": "be5c5bc7-cfc5-4645-d409-2b3985326eb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def split_data(stock, lookback):\n",
        "    data_raw = stock.to_numpy() # convert to numpy array\n",
        "    data = []\n",
        "    \n",
        "    # create all possible sequences of length seq_len\n",
        "    for index in range(len(data_raw) - lookback): \n",
        "        data.append(data_raw[index: index + lookback])\n",
        "    \n",
        "    data = np.array(data);\n",
        "    test_set_size = int(np.round(0.2*data.shape[0]));\n",
        "    train_set_size = data.shape[0] - (test_set_size);\n",
        "    \n",
        "    x_train = data[:train_set_size,:-1,:]\n",
        "    y_train = data[:train_set_size,-1,:]\n",
        "\n",
        "    x_test = data[train_set_size:,:-1]\n",
        "    y_test = data[train_set_size:,-1,:]\n",
        "    \n",
        "    return [x_train, y_train, x_test, y_test]"
      ],
      "metadata": {
        "id": "Ng1p8szmWbGd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lookback = 20 # choose sequence length\n",
        "x_train, y_train, x_test, y_test = split_data(price, lookback)\n",
        "print('x_train.shape = ',x_train.shape)\n",
        "print('y_train.shape = ',y_train.shape)\n",
        "print('x_test.shape = ',x_test.shape)\n",
        "print('y_test.shape = ',y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L5kDhirYWbJ7",
        "outputId": "7779ce68-82e8-48c7-c5a7-7d3783418875"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train.shape =  (1935, 19, 1)\n",
            "y_train.shape =  (1935, 1)\n",
            "x_test.shape =  (484, 19, 1)\n",
            "y_test.shape =  (484, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Transform to Tensor"
      ],
      "metadata": {
        "id": "gq3sddeuW38w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "x_train = torch.from_numpy(x_train).type(torch.Tensor)\n",
        "x_test = torch.from_numpy(x_test).type(torch.Tensor)\n",
        "y_train_lstm = torch.from_numpy(y_train).type(torch.Tensor)\n",
        "y_test_lstm = torch.from_numpy(y_test).type(torch.Tensor)"
      ],
      "metadata": {
        "id": "gzUx1oO2WxRk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_dim = 1\n",
        "hidden_dim = 32\n",
        "num_layers = 2\n",
        "output_dim = 1\n",
        "num_epochs = 100"
      ],
      "metadata": {
        "id": "7LixJzbFWxUh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class LSTM(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, num_layers, output_dim):\n",
        "        super(LSTM, self).__init__()\n",
        "        self.hidden_dim = hidden_dim\n",
        "        self.num_layers = num_layers\n",
        "        \n",
        "        self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)\n",
        "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()\n",
        "        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()\n",
        "        out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))\n",
        "        out = self.fc(out[:, -1, :]) \n",
        "        return out"
      ],
      "metadata": {
        "id": "wd8j68hxWxYh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LSTM(input_dim=input_dim, hidden_dim=hidden_dim, output_dim=output_dim, num_layers=num_layers)\n",
        "criterion = torch.nn.MSELoss(reduction='mean')\n",
        "optimiser = torch.optim.Adam(model.parameters(), lr=0.01)"
      ],
      "metadata": {
        "id": "FzF9ZhjoWxb5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "hist = np.zeros(num_epochs)\n",
        "start_time = time.time()\n",
        "lstm = []\n",
        "\n",
        "for t in range(num_epochs):\n",
        "    y_train_pred = model(x_train)\n",
        "\n",
        "    loss = criterion(y_train_pred, y_train_lstm)\n",
        "    print(\"Epoch \", t, \"MSE: \", loss.item())\n",
        "    hist[t] = loss.item()\n",
        "\n",
        "    optimiser.zero_grad()\n",
        "    loss.backward()\n",
        "    optimiser.step()\n",
        "    \n",
        "training_time = time.time()-start_time\n",
        "print(\"Training time: {}\".format(training_time))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vyjliOz4XFwM",
        "outputId": "e246b61d-b0f1-4d3b-c412-71daf32855a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  0 MSE:  0.23185858130455017\n",
            "Epoch  1 MSE:  0.16618716716766357\n",
            "Epoch  2 MSE:  0.1374802142381668\n",
            "Epoch  3 MSE:  0.15019990503787994\n",
            "Epoch  4 MSE:  0.12481153011322021\n",
            "Epoch  5 MSE:  0.1191677451133728\n",
            "Epoch  6 MSE:  0.11626175045967102\n",
            "Epoch  7 MSE:  0.10562233626842499\n",
            "Epoch  8 MSE:  0.08218991756439209\n",
            "Epoch  9 MSE:  0.05519900098443031\n",
            "Epoch  10 MSE:  0.07360882312059402\n",
            "Epoch  11 MSE:  0.030539965257048607\n",
            "Epoch  12 MSE:  0.029351703822612762\n",
            "Epoch  13 MSE:  0.03918970376253128\n",
            "Epoch  14 MSE:  0.03186066821217537\n",
            "Epoch  15 MSE:  0.018866758793592453\n",
            "Epoch  16 MSE:  0.022924967110157013\n",
            "Epoch  17 MSE:  0.02590782195329666\n",
            "Epoch  18 MSE:  0.015891408547759056\n",
            "Epoch  19 MSE:  0.009112000465393066\n",
            "Epoch  20 MSE:  0.010565984062850475\n",
            "Epoch  21 MSE:  0.013742292299866676\n",
            "Epoch  22 MSE:  0.014405363239347935\n",
            "Epoch  23 MSE:  0.012607156299054623\n",
            "Epoch  24 MSE:  0.010474557057023048\n",
            "Epoch  25 MSE:  0.010060785338282585\n",
            "Epoch  26 MSE:  0.011408702470362186\n",
            "Epoch  27 MSE:  0.012271980755031109\n",
            "Epoch  28 MSE:  0.010883590206503868\n",
            "Epoch  29 MSE:  0.008310564793646336\n",
            "Epoch  30 MSE:  0.006709662266075611\n",
            "Epoch  31 MSE:  0.006624426692724228\n",
            "Epoch  32 MSE:  0.006946263834834099\n",
            "Epoch  33 MSE:  0.006549242418259382\n",
            "Epoch  34 MSE:  0.005424921866506338\n",
            "Epoch  35 MSE:  0.004580640234053135\n",
            "Epoch  36 MSE:  0.0048317755572497845\n",
            "Epoch  37 MSE:  0.005558199714869261\n",
            "Epoch  38 MSE:  0.005484769120812416\n",
            "Epoch  39 MSE:  0.0047334181144833565\n",
            "Epoch  40 MSE:  0.004405459389090538\n",
            "Epoch  41 MSE:  0.004645598120987415\n",
            "Epoch  42 MSE:  0.004692381247878075\n",
            "Epoch  43 MSE:  0.004193445667624474\n",
            "Epoch  44 MSE:  0.003612266620621085\n",
            "Epoch  45 MSE:  0.003493325086310506\n",
            "Epoch  46 MSE:  0.003687896765768528\n",
            "Epoch  47 MSE:  0.003653578460216522\n",
            "Epoch  48 MSE:  0.0033567037899047136\n",
            "Epoch  49 MSE:  0.003224624553695321\n",
            "Epoch  50 MSE:  0.0033816311042755842\n",
            "Epoch  51 MSE:  0.0035288454964756966\n",
            "Epoch  52 MSE:  0.0034440860617905855\n",
            "Epoch  53 MSE:  0.0032490675803273916\n",
            "Epoch  54 MSE:  0.0031772840302437544\n",
            "Epoch  55 MSE:  0.003231341252103448\n",
            "Epoch  56 MSE:  0.003207368776202202\n",
            "Epoch  57 MSE:  0.0030436778906732798\n",
            "Epoch  58 MSE:  0.002914999844506383\n",
            "Epoch  59 MSE:  0.002930059563368559\n",
            "Epoch  60 MSE:  0.0029771102126687765\n",
            "Epoch  61 MSE:  0.002933213021606207\n",
            "Epoch  62 MSE:  0.0028507353272289038\n",
            "Epoch  63 MSE:  0.002843670779839158\n",
            "Epoch  64 MSE:  0.0028881242033094168\n",
            "Epoch  65 MSE:  0.0028761003632098436\n",
            "Epoch  66 MSE:  0.002810228383168578\n",
            "Epoch  67 MSE:  0.0027763773687183857\n",
            "Epoch  68 MSE:  0.0027806393336504698\n",
            "Epoch  69 MSE:  0.0027597961015999317\n",
            "Epoch  70 MSE:  0.002705527236685157\n",
            "Epoch  71 MSE:  0.002671922789886594\n",
            "Epoch  72 MSE:  0.0026743405032902956\n",
            "Epoch  73 MSE:  0.002667791675776243\n",
            "Epoch  74 MSE:  0.002635035663843155\n",
            "Epoch  75 MSE:  0.002612009644508362\n",
            "Epoch  76 MSE:  0.0026142331771552563\n",
            "Epoch  77 MSE:  0.002612271811813116\n",
            "Epoch  78 MSE:  0.002588457427918911\n",
            "Epoch  79 MSE:  0.0025636213831603527\n",
            "Epoch  80 MSE:  0.002554827369749546\n",
            "Epoch  81 MSE:  0.0025467027444392443\n",
            "Epoch  82 MSE:  0.0025248739402741194\n",
            "Epoch  83 MSE:  0.002502458868548274\n",
            "Epoch  84 MSE:  0.00249182409606874\n",
            "Epoch  85 MSE:  0.0024829180911183357\n",
            "Epoch  86 MSE:  0.002466355450451374\n",
            "Epoch  87 MSE:  0.002450453583151102\n",
            "Epoch  88 MSE:  0.002442004159092903\n",
            "Epoch  89 MSE:  0.002432800130918622\n",
            "Epoch  90 MSE:  0.002417437732219696\n",
            "Epoch  91 MSE:  0.0024033496156334877\n",
            "Epoch  92 MSE:  0.0023939423263072968\n",
            "Epoch  93 MSE:  0.0023823040537536144\n",
            "Epoch  94 MSE:  0.0023664983455091715\n",
            "Epoch  95 MSE:  0.0023529476020485163\n",
            "Epoch  96 MSE:  0.002342809224501252\n",
            "Epoch  97 MSE:  0.0023307246156036854\n",
            "Epoch  98 MSE:  0.002316670725122094\n",
            "Epoch  99 MSE:  0.0023050853051245213\n",
            "Training time: 17.389548778533936\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prediction"
      ],
      "metadata": {
        "id": "7_sfYe9AXTQp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predict = pd.DataFrame(scaler.inverse_transform(y_train_pred.detach().numpy()))\n",
        "original = pd.DataFrame(scaler.inverse_transform(y_train_lstm.detach().numpy()))"
      ],
      "metadata": {
        "id": "iAOZnnifXFy-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.set_style(\"darkgrid\")    \n",
        "\n",
        "fig = plt.figure()\n",
        "fig.subplots_adjust(hspace=0.2, wspace=0.2)\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "ax = sns.lineplot(x = original.index, y = original[0], label=\"Data\", color='royalblue')\n",
        "ax = sns.lineplot(x = predict.index, y = predict[0], label=\"Training Prediction (LSTM)\", color='tomato')\n",
        "ax.set_title('Stock price', size = 14, fontweight='bold')\n",
        "ax.set_xlabel(\"Days\", size = 14)\n",
        "ax.set_ylabel(\"Price (THB))\", size = 14)\n",
        "ax.set_xticklabels('', size=10)\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "ax = sns.lineplot(data=hist, color='royalblue')\n",
        "ax.set_xlabel(\"Epoch\", size = 14)\n",
        "ax.set_ylabel(\"Loss\", size = 14)\n",
        "ax.set_title(\"Training Loss\", size = 14, fontweight='bold')\n",
        "fig.set_figheight(6)\n",
        "fig.set_figwidth(16)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "id": "aG895_aWXF2e",
        "outputId": "a90b30a2-88b3-4a37-d692-1d44eb9050e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Loss"
      ],
      "metadata": {
        "id": "ff8K9dVeXqHd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import math, time\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "# make predictions\n",
        "y_test_pred = model(x_test)\n",
        "\n",
        "# invert predictions\n",
        "y_train_pred = scaler.inverse_transform(y_train_pred.detach().numpy())\n",
        "y_train = scaler.inverse_transform(y_train_lstm.detach().numpy())\n",
        "y_test_pred = scaler.inverse_transform(y_test_pred.detach().numpy())\n",
        "y_test = scaler.inverse_transform(y_test_lstm.detach().numpy())\n",
        "\n",
        "# calculate root mean squared error\n",
        "trainScore = math.sqrt(mean_squared_error(y_train[:,0], y_train_pred[:,0]))\n",
        "print('Train Score: %.2f RMSE' % (trainScore))\n",
        "testScore = math.sqrt(mean_squared_error(y_test[:,0], y_test_pred[:,0]))\n",
        "print('Test Score: %.2f RMSE' % (testScore))\n",
        "lstm.append(trainScore)\n",
        "lstm.append(testScore)\n",
        "lstm.append(training_time)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rpyMa8zTXoYG",
        "outputId": "dfbbd6d9-af64-46be-fd2e-6ea01c4af77d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Score: 12.63 RMSE\n",
            "Test Score: 19.69 RMSE\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_mse_score = mean_squared_error(y_train,y_train_pred)\n",
        "test_mse_score = mean_squared_error(y_test,y_test_pred)\n",
        "print(\"Train MSE: %.2f\"%(train_mse_score))\n",
        "print(\"Test MSE: %.2f\"%(test_mse_score))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YV3LpwcZnSiK",
        "outputId": "161f9198-898e-4a91-a4e4-17cda10d9bdd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train MSE: 159.59\n",
            "Test MSE: 387.79\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# shift train predictions for plotting\n",
        "trainPredictPlot = np.empty_like(price)\n",
        "trainPredictPlot[:, :] = np.nan\n",
        "trainPredictPlot[lookback:len(y_train_pred)+lookback, :] = y_train_pred\n",
        "\n",
        "# shift test predictions for plotting\n",
        "testPredictPlot = np.empty_like(price)\n",
        "testPredictPlot[:, :] = np.nan\n",
        "testPredictPlot[len(y_train_pred)+lookback-1:len(price)-1, :] = y_test_pred\n",
        "\n",
        "original = scaler.inverse_transform(price['Price'].values.reshape(-1,1))\n",
        "\n",
        "predictions = np.append(trainPredictPlot, testPredictPlot, axis=1)\n",
        "predictions = np.append(predictions, original, axis=1)\n",
        "result = pd.DataFrame(predictions)"
      ],
      "metadata": {
        "id": "6d7T6iNPXoa9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(go.Scatter(x=result.index, y=result[0],\n",
        "                    mode='lines',\n",
        "                    name='Train prediction')))\n",
        "fig.add_trace(go.Scatter(x=result.index, y=result[1],\n",
        "                    mode='lines',\n",
        "                    name='Test prediction'))\n",
        "fig.add_trace(go.Scatter(go.Scatter(x=result.index, y=result[2],\n",
        "                    mode='lines',\n",
        "                    name='Actual Value')))\n",
        "\n",
        "fig.update_layout(\n",
        "    xaxis=dict(\n",
        "        title_text='Date',\n",
        "        \n",
        "        showline=True,\n",
        "        showgrid=True,\n",
        "        showticklabels=False,\n",
        "        linecolor='white',\n",
        "        linewidth=2\n",
        "    ),\n",
        "    yaxis=dict(\n",
        "        title_text='Price (THB)',\n",
        "        titlefont=dict(\n",
        "            family='Rockwell',\n",
        "            size=12,\n",
        "            color='white',\n",
        "        ),\n",
        "         showline=True,\n",
        "        showgrid=True,\n",
        "        showticklabels=True,\n",
        "        linecolor='white',\n",
        "        linewidth=2,\n",
        "        ticks='outside',\n",
        "        tickfont=dict(\n",
        "            family='Rockwell',\n",
        "            size=12,\n",
        "            color='white',\n",
        "        ),\n",
        "    ),\n",
        "    showlegend=True,\n",
        "    template = 'plotly_dark'\n",
        "\n",
        ")\n",
        "\n",
        "annotations = []\n",
        "annotations.append(dict(xref='paper', yref='paper', x=0.0, y=1.05,\n",
        "                              xanchor='left', yanchor='bottom',\n",
        "                              text='Results (LSTM)',\n",
        "                              font=dict(family='Rockwell',\n",
        "                                        size=26,\n",
        "                                        color='white'),\n",
        "                              showarrow=False))\n",
        "fig.update_layout(annotations=annotations)\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "5GKSmUhQXoeK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "57b8db4e-e8d0-4ead-dc34-150a9b4cfedf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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