ai-stock-forecast-using-lstms.ipynb

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    "<style>\n",
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   "source": [
    "# <div style=\"padding:15px;background-color:#f4ebdc;border-radius:20px;margin:0;color:#8b4726;border:2px dotted #aaaaaa;font-family:Charter;font-weight: bold;font-size:130%;text-align:center;overflow:hidden;font-weight:500\">AI Stock Forecast 📈 Using LSTMs</div>"
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   "source": [
    "Welcome to my notebook on AI Stock Forecast 📈 Using LSTMs! In this project, we will be using a dataset called C3.AI Stocks Dataset, which is a collection of stock market data sourced from Yahoo for the period from March 2022 to March 2023. This dataset contains several columns of data for each stock, including the date on which the stock market data was recorded, the opening price of the stock, the highest and lowest prices of the stock on a given day, the closing price of the stock, the adjusted closing price that accounts for any corporate actions that may have affected the stock's price, and the total number of shares traded in the stock on a given day.\n",
    "\n",
    "Our goal in this project is to use Long Short-Term Memory (LSTM) neural networks, a type of Recurrent Neural Networks (RNN), to predict the future stock prices of the AI company. LSTM networks are a powerful type of neural networks that can capture long-term dependencies in time-series data, making them ideal for stock price prediction tasks.\n",
    "\n",
    "We will start by performing exploratory data analysis on the C3.AI Stocks Dataset to gain insights into the data and understand the trends and patterns. Then, we will preprocess the data and split it into training, validation and testing sets. After that, we will build and train our LSTM model on the training set and use it to predict the future stock prices on other sets.\n",
    "\n",
    "By the end of this notebook, you will have a solid understanding of how to use LSTM networks to forecast stock prices and will be able to apply this knowledge to other forecasting tasks as well. Let's get started!"
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    "# <div style=\"color:white;display:fill;border-radius:20px;background-color:#f4ebdc;font-size:70%;font-family:Charter;letter-spacing:1px\"><h3 style='padding: 20px;color:#8b4726;text-align:center;'>Importing Main Dependencies</h3></div>"
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   "source": [
    "import pandas as pd # for data manipulation\n",
    "import numpy as np # for linear algebra\n",
    "import matplotlib.pyplot as plt # for plotting\n",
    "import plotly.express as px # for plotting\n",
    "import plotly.graph_objs as go # for plotting"
   ]
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   "source": [
    "# <div style=\"color:white;display:fill;border-radius:20px;background-color:#f4ebdc;font-size:70%;font-family:Charter;letter-spacing:1px\"><h3 style='padding: 20px;color:#8b4726;text-align:center;'>Loading Dataset</h3></div>"
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   "source": [
    "# Getting path of dataset\n",
    "dataset_path = \"/kaggle/input/c3ai-stocks-dataset/AI.csv\""
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2022-03-10</th>\n",
       "      <td>19.959999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-11</th>\n",
       "      <td>18.000000</td>\n",
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       "    <tr>\n",
       "      <th>2022-03-14</th>\n",
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       "      <th>2022-03-15</th>\n",
       "      <td>17.469999</td>\n",
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       "      <th>2022-03-16</th>\n",
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       "                Close\n",
       "Date                 \n",
       "2022-03-10  19.959999\n",
       "2022-03-11  18.000000\n",
       "2022-03-14  16.910000\n",
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   "source": [
    "# Loading dataset\n",
    "df = pd.read_csv(dataset_path, \n",
    "                 usecols=['Date','Close'], \n",
    "                 parse_dates=['Date'], \n",
    "                 index_col='Date')\n",
    "\n",
    "# Displaying our data\n",
    "df.head()"
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   "source": [
    "For simplicity we are gonna use only 'Close' column."
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    "# <div style=\"color:white;display:fill;border-radius:20px;background-color:#f4ebdc;font-size:70%;font-family:Charter;letter-spacing:1px\"><h3 style='padding: 20px;color:#8b4726;text-align:center;'>Exploratory Data Analysis</h3></div>"
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    {
     "data": {
      "text/plain": [
       "(251, 1)"
      ]
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     "execution_count": 4,
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   "source": [
    "# Printing shape of dataframe\n",
    "df.shape"
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   "source": [
    "Data from 1 year has only 251 records which make sense."
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 251 entries, 2022-03-10 to 2023-03-09\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Close   251 non-null    float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 3.9 KB\n"
     ]
    }
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   "source": [
    "# Checking data types of features\n",
    "df.info()"
   ]
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   "source": [
    "Data types are ok."
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    {
     "data": {
      "text/plain": [
       "Close    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
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   ],
   "source": [
    "# Checking number of missing values\n",
    "df.isna().sum()"
   ]
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   "source": [
    "There are no missing values."
   ]
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Price Over The Months\"}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('35ceb4b9-5bd3-4b7a-9aac-bed5945ba956');\n",
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       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "}});\n",
       "\n",
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       "var notebookContainer = gd.closest('#notebook-container');\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting price of stock over the months\n",
    "px.line(df, x=df.index, y='Close', title='Stock Price Over The Months', labels={'x':'Date', 'y':'Price'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b1fbc28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-10T18:38:20.712841Z",
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     "iopub.status.idle": "2023-03-10T18:38:20.721038Z",
     "shell.execute_reply": "2023-03-10T18:38:20.719127Z",
     "shell.execute_reply.started": "2023-03-10T18:38:20.712799Z"
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     "exception": false,
     "start_time": "2023-03-12T10:24:42.131960",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "From plot we can se that in last months the price was going high pretty fast"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fdf07da",
   "metadata": {
    "papermill": {
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     "exception": false,
     "start_time": "2023-03-12T10:24:42.148634",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# <div style=\"color:white;display:fill;border-radius:20px;background-color:#f4ebdc;font-size:70%;font-family:Charter;letter-spacing:1px\"><h3 style='padding: 20px;color:#8b4726;text-align:center;'>Preprocessing</h3></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b366834",
   "metadata": {
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     "duration": 0.008746,
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     "start_time": "2023-03-12T10:24:42.165269",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Feature Extraction\n",
    "\n",
    "As preprocessing operation we age going to use feature extraction to create new lag features using shift method from pandas. <br>\n",
    "Feature extraction is a process of creating new features as it allows us to engineer new features that may better capture patterns and relationships in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "993f2dd5",
   "metadata": {
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     "exception": false,
     "start_time": "2023-03-12T10:24:42.182358",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>lag1</th>\n",
       "      <th>lag2</th>\n",
       "      <th>lag3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-03-10</th>\n",
       "      <td>19.959999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-11</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>19.959999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2022-03-14</th>\n",
       "      <td>16.910000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>19.959999</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2022-03-15</th>\n",
       "      <td>17.469999</td>\n",
       "      <td>16.910000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>19.959999</td>\n",
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       "    <tr>\n",
       "      <th>2022-03-16</th>\n",
       "      <td>19.299999</td>\n",
       "      <td>17.469999</td>\n",
       "      <td>16.910000</td>\n",
       "      <td>18.000000</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "                Close       lag1       lag2       lag3\n",
       "Date                                                  \n",
       "2022-03-10  19.959999        NaN        NaN        NaN\n",
       "2022-03-11  18.000000  19.959999        NaN        NaN\n",
       "2022-03-14  16.910000  18.000000  19.959999        NaN\n",
       "2022-03-15  17.469999  16.910000  18.000000  19.959999\n",
       "2022-03-16  19.299999  17.469999  16.910000  18.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating Lag features\n",
    "df['lag1'] = df['Close'].shift(1)\n",
    "df['lag2'] = df['Close'].shift(2)\n",
    "df['lag3'] = df['Close'].shift(3)\n",
    "\n",
    "# Displaying our data\n",
    "df.head()"
   ]
  },
  {
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   "id": "924373a8",
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   "source": [
    "As we can see, the feature creation process was successful in generating new lag features.<br>\n",
    "However, some rows now contain NaN values due to a lack of past data.<br>\n",
    "To ensure the dataset is ready for analysis, we will need to drop these rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8743dbd7",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "# Dropping features that\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62a2ed41",
   "metadata": {
    "papermill": {
     "duration": 0.008853,
     "end_time": "2023-03-12T10:24:42.277602",
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     "start_time": "2023-03-12T10:24:42.268749",
     "status": "completed"
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    "tags": []
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   "source": [
    "Now the data doesn't contain inconsistencies."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d93f4002",
   "metadata": {
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     "duration": 0.008851,
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     "start_time": "2023-03-12T10:24:42.286622",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Splitting Data\n",
    "\n",
    "Splitting data into X and y and later into train, val and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "20ecfd79",
   "metadata": {
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       "      <td>20.750000</td>\n",
       "      <td>19.299999</td>\n",
       "      <td>17.469999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-21</th>\n",
       "      <td>21.959999</td>\n",
       "      <td>20.750000</td>\n",
       "      <td>19.299999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 lag1       lag2       lag3\n",
       "Date                                       \n",
       "2022-03-15  16.910000  18.000000  19.959999\n",
       "2022-03-16  17.469999  16.910000  18.000000\n",
       "2022-03-17  19.299999  17.469999  16.910000\n",
       "2022-03-18  20.750000  19.299999  17.469999\n",
       "2022-03-21  21.959999  20.750000  19.299999"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Chosing target feature\n",
    "target = \"Close\"\n",
    "\n",
    "# Splitting data into X and y\n",
    "X = df.drop(columns=[target])\n",
    "y = df[target]\n",
    "dates = df.index\n",
    "\n",
    "# Printing X\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "260fefdf",
   "metadata": {
    "papermill": {
     "duration": 0.008854,
     "end_time": "2023-03-12T10:24:42.348900",
     "exception": false,
     "start_time": "2023-03-12T10:24:42.340046",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Data has been split into X and y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ced6bf50",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-12T10:24:42.368696Z",
     "iopub.status.busy": "2023-03-12T10:24:42.367799Z",
     "iopub.status.idle": "2023-03-12T10:24:42.374990Z",
     "shell.execute_reply": "2023-03-12T10:24:42.373695Z"
    },
    "papermill": {
     "duration": 0.019443,
     "end_time": "2023-03-12T10:24:42.377196",
     "exception": false,
     "start_time": "2023-03-12T10:24:42.357753",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dates shape = (248,),\n",
      "X shape = (248, 3, 1),\n",
      "y shape = (248,)\n"
     ]
    }
   ],
   "source": [
    "# Converting data to numerical format\n",
    "X = X.to_numpy().astype(np.float32).reshape(X.shape[0], -1, 1)\n",
    "y = y.to_numpy().astype(np.float32)\n",
    "dates = dates.to_numpy()\n",
    "\n",
    "print(f\"dates shape = {dates.shape},\\nX shape = {X.shape},\\ny shape = {y.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02ed4952",
   "metadata": {
    "papermill": {
     "duration": 0.008806,
     "end_time": "2023-03-12T10:24:42.395137",
     "exception": false,
     "start_time": "2023-03-12T10:24:42.386331",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Data has correct format.\\\n",
    "Now we are going to split it into train, validation and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5c421d64",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-12T10:24:42.414866Z",
     "iopub.status.busy": "2023-03-12T10:24:42.414047Z",
     "iopub.status.idle": "2023-03-12T10:24:42.420367Z",
     "shell.execute_reply": "2023-03-12T10:24:42.419112Z"
    },
    "papermill": {
     "duration": 0.018235,
     "end_time": "2023-03-12T10:24:42.422369",
     "exception": false,
     "start_time": "2023-03-12T10:24:42.404134",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Train/Val/Test split\n",
    "q_80 = int(len(dates) * .70)\n",
    "q_90 = int(len(dates) * .90)\n",
    "\n",
    "dates_train, X_train, y_train = dates[:q_80], X[:q_80], y[:q_80]\n",
    "dates_val, X_val, y_val = dates[q_80:q_90], X[q_80:q_90], y[q_80:q_90]\n",
    "dates_test, X_test, y_test = dates[q_90:], X[q_90:], y[q_90:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "012ef471",
   "metadata": {
    "papermill": {
     "duration": 0.008315,
     "end_time": "2023-03-12T10:24:42.439734",
     "exception": false,
     "start_time": "2023-03-12T10:24:42.431419",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Plotting Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aa5e9dde",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-12T10:24:42.458971Z",
     "iopub.status.busy": "2023-03-12T10:24:42.458680Z",
     "iopub.status.idle": "2023-03-12T10:24:42.470495Z",
     "shell.execute_reply": "2023-03-12T10:24:42.469515Z"
    },
    "papermill": {
     "duration": 0.023992,
     "end_time": "2023-03-12T10:24:42.472574",
     "exception": false,
     "start_time": "2023-03-12T10:24:42.448582",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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                       {\"title\":{\"text\":\"Datasets Plot\"},\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"aut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       "                            \n",
       "var gd = document.getElementById('0df8a100-61d5-4d4c-9015-b8a67f8c089b');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
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       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(outputEl, {childList: true});\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating traces for each dataset\n",
    "trace_train = go.Scatter(x=dates_train, y=y_train, name='Train')\n",
    "trace_val = go.Scatter(x=dates_val, y=y_val, name='Validation')\n",
    "trace_test = go.Scatter(x=dates_test, y=y_test, name='Test')\n",
    "\n",
    "# Combining traces, setting layout and plotting figure\n",
    "data = [trace_train, trace_val, trace_test]\n",
    "layout = go.Layout(title='Datasets Plot')\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "529c9ff4",
   "metadata": {
    "papermill": {
     "duration": 0.009641,
     "end_time": "2023-03-12T10:24:42.491495",
     "exception": false,
     "start_time": "2023-03-12T10:24:42.481854",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As we can see data is correctly spitted."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d90280ac",
   "metadata": {
    "papermill": {
     "duration": 0.008991,
     "end_time": "2023-03-12T10:24:42.509728",
     "exception": false,
     "start_time": "2023-03-12T10:24:42.500737",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# <div style=\"color:white;display:fill;border-radius:20px;background-color:#f4ebdc;font-size:70%;font-family:Charter;letter-spacing:1px\"><h3 style='padding: 20px;color:#8b4726;text-align:center;'>Building LSTM Model</h3></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a512477",
   "metadata": {
    "papermill": {
     "duration": 0.008949,
     "end_time": "2023-03-12T10:24:42.528105",
     "exception": false,
     "start_time": "2023-03-12T10:24:42.519156",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Step in which we are going to build and train LSTM model using Keras library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1e50a8cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-12T10:24:42.548603Z",
     "iopub.status.busy": "2023-03-12T10:24:42.548223Z",
     "iopub.status.idle": "2023-03-12T10:24:57.418817Z",
     "shell.execute_reply": "2023-03-12T10:24:57.417719Z"
    },
    "papermill": {
     "duration": 14.883648,
     "end_time": "2023-03-12T10:24:57.421005",
     "exception": false,
     "start_time": "2023-03-12T10:24:42.537357",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/80\n",
      "6/6 [==============================] - 3s 112ms/step - loss: 301.4893 - mean_absolute_error: 17.0142 - val_loss: 143.7497 - val_mean_absolute_error: 11.7874\n",
      "Epoch 2/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 267.8904 - mean_absolute_error: 15.9847 - val_loss: 110.1572 - val_mean_absolute_error: 10.2563\n",
      "Epoch 3/80\n",
      "6/6 [==============================] - 0s 10ms/step - loss: 216.8987 - mean_absolute_error: 14.2697 - val_loss: 64.1041 - val_mean_absolute_error: 7.6762\n",
      "Epoch 4/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 144.5259 - mean_absolute_error: 11.4172 - val_loss: 17.6292 - val_mean_absolute_error: 3.4952\n",
      "Epoch 5/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 62.6059 - mean_absolute_error: 6.8143 - val_loss: 10.3875 - val_mean_absolute_error: 2.9476\n",
      "Epoch 6/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 17.6435 - mean_absolute_error: 3.5901 - val_loss: 60.5309 - val_mean_absolute_error: 7.5317\n",
      "Epoch 7/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 24.2366 - mean_absolute_error: 4.0194 - val_loss: 66.0478 - val_mean_absolute_error: 7.8811\n",
      "Epoch 8/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 18.3763 - mean_absolute_error: 3.4535 - val_loss: 33.5020 - val_mean_absolute_error: 5.5826\n",
      "Epoch 9/80\n",
      "6/6 [==============================] - 0s 8ms/step - loss: 12.0054 - mean_absolute_error: 2.8986 - val_loss: 17.3813 - val_mean_absolute_error: 3.9823\n",
      "Epoch 10/80\n",
      "6/6 [==============================] - 0s 8ms/step - loss: 12.3200 - mean_absolute_error: 3.0875 - val_loss: 14.9317 - val_mean_absolute_error: 3.6720\n",
      "Epoch 11/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 12.0212 - mean_absolute_error: 3.0448 - val_loss: 18.0720 - val_mean_absolute_error: 4.0718\n",
      "Epoch 12/80\n",
      "6/6 [==============================] - 0s 8ms/step - loss: 10.8596 - mean_absolute_error: 2.8431 - val_loss: 22.6365 - val_mean_absolute_error: 4.5757\n",
      "Epoch 13/80\n",
      "6/6 [==============================] - 0s 8ms/step - loss: 10.4380 - mean_absolute_error: 2.7029 - val_loss: 23.3493 - val_mean_absolute_error: 4.6512\n",
      "Epoch 14/80\n",
      "6/6 [==============================] - 0s 8ms/step - loss: 9.8311 - mean_absolute_error: 2.6228 - val_loss: 17.7855 - val_mean_absolute_error: 4.0591\n",
      "Epoch 15/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 8.7545 - mean_absolute_error: 2.5353 - val_loss: 10.1549 - val_mean_absolute_error: 3.0193\n",
      "Epoch 16/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 7.5192 - mean_absolute_error: 2.3672 - val_loss: 8.6288 - val_mean_absolute_error: 2.7813\n",
      "Epoch 17/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 5.7561 - mean_absolute_error: 2.0230 - val_loss: 5.9140 - val_mean_absolute_error: 2.2483\n",
      "Epoch 18/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 4.4720 - mean_absolute_error: 1.7869 - val_loss: 7.4683 - val_mean_absolute_error: 2.5871\n",
      "Epoch 19/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 4.0955 - mean_absolute_error: 1.7111 - val_loss: 4.8017 - val_mean_absolute_error: 1.9908\n",
      "Epoch 20/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 3.3601 - mean_absolute_error: 1.5425 - val_loss: 4.3877 - val_mean_absolute_error: 1.8944\n",
      "Epoch 21/80\n",
      "6/6 [==============================] - 0s 12ms/step - loss: 2.8190 - mean_absolute_error: 1.4009 - val_loss: 3.7906 - val_mean_absolute_error: 1.7350\n",
      "Epoch 22/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 2.5132 - mean_absolute_error: 1.3135 - val_loss: 2.7171 - val_mean_absolute_error: 1.3712\n",
      "Epoch 23/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 2.2206 - mean_absolute_error: 1.2110 - val_loss: 2.5234 - val_mean_absolute_error: 1.3241\n",
      "Epoch 24/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 2.0719 - mean_absolute_error: 1.1586 - val_loss: 1.9479 - val_mean_absolute_error: 1.0782\n",
      "Epoch 25/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.8859 - mean_absolute_error: 1.0862 - val_loss: 1.6191 - val_mean_absolute_error: 0.8985\n",
      "Epoch 26/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.8042 - mean_absolute_error: 1.0193 - val_loss: 1.5832 - val_mean_absolute_error: 0.9589\n",
      "Epoch 27/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.8292 - mean_absolute_error: 1.0577 - val_loss: 1.8069 - val_mean_absolute_error: 0.9277\n",
      "Epoch 28/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.9250 - mean_absolute_error: 1.0217 - val_loss: 1.3845 - val_mean_absolute_error: 0.8436\n",
      "Epoch 29/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.6827 - mean_absolute_error: 0.9727 - val_loss: 1.4485 - val_mean_absolute_error: 0.8467\n",
      "Epoch 30/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.6854 - mean_absolute_error: 0.9696 - val_loss: 1.4271 - val_mean_absolute_error: 0.8439\n",
      "Epoch 31/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.6110 - mean_absolute_error: 0.9518 - val_loss: 1.4120 - val_mean_absolute_error: 0.8384\n",
      "Epoch 32/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.6081 - mean_absolute_error: 0.9482 - val_loss: 1.3580 - val_mean_absolute_error: 0.8418\n",
      "Epoch 33/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.6092 - mean_absolute_error: 0.9716 - val_loss: 1.6128 - val_mean_absolute_error: 0.8674\n",
      "Epoch 34/80\n",
      "6/6 [==============================] - 0s 12ms/step - loss: 1.7523 - mean_absolute_error: 1.0206 - val_loss: 1.4394 - val_mean_absolute_error: 0.8985\n",
      "Epoch 35/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.6413 - mean_absolute_error: 0.9920 - val_loss: 1.4875 - val_mean_absolute_error: 0.8367\n",
      "Epoch 36/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.5751 - mean_absolute_error: 0.9474 - val_loss: 1.3602 - val_mean_absolute_error: 0.8240\n",
      "Epoch 37/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.4679 - mean_absolute_error: 0.9138 - val_loss: 1.3543 - val_mean_absolute_error: 0.8181\n",
      "Epoch 38/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.5271 - mean_absolute_error: 0.9332 - val_loss: 1.3887 - val_mean_absolute_error: 0.8163\n",
      "Epoch 39/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.4281 - mean_absolute_error: 0.8876 - val_loss: 1.3450 - val_mean_absolute_error: 0.8104\n",
      "Epoch 40/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.4089 - mean_absolute_error: 0.8861 - val_loss: 1.3189 - val_mean_absolute_error: 0.8102\n",
      "Epoch 41/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.3991 - mean_absolute_error: 0.8909 - val_loss: 1.3373 - val_mean_absolute_error: 0.8113\n",
      "Epoch 42/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.3729 - mean_absolute_error: 0.8741 - val_loss: 1.3099 - val_mean_absolute_error: 0.8155\n",
      "Epoch 43/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.3807 - mean_absolute_error: 0.8783 - val_loss: 1.3293 - val_mean_absolute_error: 0.8038\n",
      "Epoch 44/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.3582 - mean_absolute_error: 0.8736 - val_loss: 1.3787 - val_mean_absolute_error: 0.8078\n",
      "Epoch 45/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.4325 - mean_absolute_error: 0.9136 - val_loss: 1.3382 - val_mean_absolute_error: 0.8551\n",
      "Epoch 46/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.4114 - mean_absolute_error: 0.9246 - val_loss: 1.5506 - val_mean_absolute_error: 0.8248\n",
      "Epoch 47/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.5656 - mean_absolute_error: 0.9276 - val_loss: 1.2716 - val_mean_absolute_error: 0.8004\n",
      "Epoch 48/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.4722 - mean_absolute_error: 0.9175 - val_loss: 1.2790 - val_mean_absolute_error: 0.8208\n",
      "Epoch 49/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.5539 - mean_absolute_error: 0.9610 - val_loss: 1.4966 - val_mean_absolute_error: 0.8109\n",
      "Epoch 50/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.6013 - mean_absolute_error: 0.9559 - val_loss: 1.4398 - val_mean_absolute_error: 0.9312\n",
      "Epoch 51/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.4912 - mean_absolute_error: 0.9424 - val_loss: 1.4365 - val_mean_absolute_error: 0.7988\n",
      "Epoch 52/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.5121 - mean_absolute_error: 0.9388 - val_loss: 1.3463 - val_mean_absolute_error: 0.8780\n",
      "Epoch 53/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.5496 - mean_absolute_error: 0.9522 - val_loss: 1.4744 - val_mean_absolute_error: 0.8091\n",
      "Epoch 54/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.5675 - mean_absolute_error: 0.9801 - val_loss: 1.1594 - val_mean_absolute_error: 0.7813\n",
      "Epoch 55/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.4343 - mean_absolute_error: 0.9225 - val_loss: 1.4130 - val_mean_absolute_error: 0.7944\n",
      "Epoch 56/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.3733 - mean_absolute_error: 0.8867 - val_loss: 1.1571 - val_mean_absolute_error: 0.7710\n",
      "Epoch 57/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.2977 - mean_absolute_error: 0.8547 - val_loss: 1.3680 - val_mean_absolute_error: 0.7830\n",
      "Epoch 58/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.3951 - mean_absolute_error: 0.8971 - val_loss: 1.1380 - val_mean_absolute_error: 0.7538\n",
      "Epoch 59/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.2434 - mean_absolute_error: 0.8376 - val_loss: 1.2215 - val_mean_absolute_error: 0.7541\n",
      "Epoch 60/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.2381 - mean_absolute_error: 0.8332 - val_loss: 1.2002 - val_mean_absolute_error: 0.7488\n",
      "Epoch 61/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.2343 - mean_absolute_error: 0.8377 - val_loss: 1.1511 - val_mean_absolute_error: 0.7600\n",
      "Epoch 62/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.2382 - mean_absolute_error: 0.8326 - val_loss: 1.2625 - val_mean_absolute_error: 0.7544\n",
      "Epoch 63/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.2487 - mean_absolute_error: 0.8399 - val_loss: 1.1112 - val_mean_absolute_error: 0.7507\n",
      "Epoch 64/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.2456 - mean_absolute_error: 0.8473 - val_loss: 1.2758 - val_mean_absolute_error: 0.7549\n",
      "Epoch 65/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.2420 - mean_absolute_error: 0.8283 - val_loss: 1.1122 - val_mean_absolute_error: 0.7430\n",
      "Epoch 66/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.1638 - mean_absolute_error: 0.8246 - val_loss: 1.1655 - val_mean_absolute_error: 0.7318\n",
      "Epoch 67/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.1401 - mean_absolute_error: 0.8081 - val_loss: 1.0794 - val_mean_absolute_error: 0.7205\n",
      "Epoch 68/80\n",
      "6/6 [==============================] - 0s 12ms/step - loss: 1.1440 - mean_absolute_error: 0.7967 - val_loss: 1.1234 - val_mean_absolute_error: 0.7190\n",
      "Epoch 69/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.1237 - mean_absolute_error: 0.7963 - val_loss: 1.1282 - val_mean_absolute_error: 0.7181\n",
      "Epoch 70/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.1209 - mean_absolute_error: 0.7954 - val_loss: 1.0959 - val_mean_absolute_error: 0.7112\n",
      "Epoch 71/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.1256 - mean_absolute_error: 0.7986 - val_loss: 1.1523 - val_mean_absolute_error: 0.7209\n",
      "Epoch 72/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.1130 - mean_absolute_error: 0.7939 - val_loss: 1.0768 - val_mean_absolute_error: 0.7305\n",
      "Epoch 73/80\n",
      "6/6 [==============================] - 0s 8ms/step - loss: 1.2132 - mean_absolute_error: 0.8175 - val_loss: 1.1286 - val_mean_absolute_error: 0.7124\n",
      "Epoch 74/80\n",
      "6/6 [==============================] - 0s 8ms/step - loss: 1.1104 - mean_absolute_error: 0.7873 - val_loss: 1.0298 - val_mean_absolute_error: 0.6902\n",
      "Epoch 75/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.1636 - mean_absolute_error: 0.7893 - val_loss: 1.0313 - val_mean_absolute_error: 0.6965\n",
      "Epoch 76/80\n",
      "6/6 [==============================] - 0s 8ms/step - loss: 1.1078 - mean_absolute_error: 0.7811 - val_loss: 1.1565 - val_mean_absolute_error: 0.7136\n",
      "Epoch 77/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.2523 - mean_absolute_error: 0.8427 - val_loss: 1.1179 - val_mean_absolute_error: 0.7842\n",
      "Epoch 78/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.2270 - mean_absolute_error: 0.8335 - val_loss: 1.0892 - val_mean_absolute_error: 0.6977\n",
      "Epoch 79/80\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 1.2966 - mean_absolute_error: 0.8736 - val_loss: 1.1161 - val_mean_absolute_error: 0.7018\n",
      "Epoch 80/80\n",
      "6/6 [==============================] - 0s 8ms/step - loss: 1.2121 - mean_absolute_error: 0.8243 - val_loss: 1.0052 - val_mean_absolute_error: 0.7104\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7eff5062fd10>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "model = Sequential([layers.Input((3, 1)),\n",
    "                    layers.LSTM(32),\n",
    "                    layers.Dense(16, activation='relu'),\n",
    "                    layers.Dense(16, activation='relu'),\n",
    "                    layers.Dense(1)])\n",
    "\n",
    "model.compile(loss='mse', \n",
    "              optimizer=Adam(learning_rate=0.003),\n",
    "              metrics=['mean_absolute_error'])\n",
    "\n",
    "model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=80)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c52ae62a",
   "metadata": {
    "papermill": {
     "duration": 0.021742,
     "end_time": "2023-03-12T10:24:57.465382",
     "exception": false,
     "start_time": "2023-03-12T10:24:57.443640",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# <div style=\"color:white;display:fill;border-radius:20px;background-color:#f4ebdc;font-size:70%;font-family:Charter;letter-spacing:1px\"><h3 style='padding: 20px;color:#8b4726;text-align:center;'>Model Testing</h3></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7db56ebe",
   "metadata": {
    "papermill": {
     "duration": 0.021681,
     "end_time": "2023-03-12T10:24:57.508982",
     "exception": false,
     "start_time": "2023-03-12T10:24:57.487301",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5d2db8dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-12T10:24:57.555120Z",
     "iopub.status.busy": "2023-03-12T10:24:57.554750Z",
     "iopub.status.idle": "2023-03-12T10:24:58.175021Z",
     "shell.execute_reply": "2023-03-12T10:24:58.173966Z"
    },
    "papermill": {
     "duration": 0.646016,
     "end_time": "2023-03-12T10:24:58.177364",
     "exception": false,
     "start_time": "2023-03-12T10:24:57.531348",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 [==============================] - 0s 2ms/step\n",
      "2/2 [==============================] - 0s 3ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n"
     ]
    }
   ],
   "source": [
    "# Using model to predict sets\n",
    "train_predictions = model.predict(X_train).flatten()\n",
    "val_predictions = model.predict(X_val).flatten()\n",
    "test_predictions = model.predict(X_test).flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf9b00f4",
   "metadata": {
    "papermill": {
     "duration": 0.023195,
     "end_time": "2023-03-12T10:24:58.223589",
     "exception": false,
     "start_time": "2023-03-12T10:24:58.200394",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Plotting Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b88c21bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-12T10:24:58.274629Z",
     "iopub.status.busy": "2023-03-12T10:24:58.273692Z",
     "iopub.status.idle": "2023-03-12T10:24:58.290875Z",
     "shell.execute_reply": "2023-03-12T10:24:58.289734Z"
    },
    "papermill": {
     "duration": 0.045108,
     "end_time": "2023-03-12T10:24:58.293508",
     "exception": false,
     "start_time": "2023-03-12T10:24:58.248400",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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   ],
   "source": [
    "# Creating traces for all results\n",
    "trace_y = go.Scatter(x=dates, y=y, name='Observations', opacity=0.5, line=dict(color='grey'))\n",
    "trace_train_pred = go.Scatter(x=dates_train, y=train_predictions, name='Training Predictions')\n",
    "trace_val_pred = go.Scatter(x=dates_val, y=val_predictions, name='Validation Predictions')\n",
    "trace_test_pred = go.Scatter(x=dates_test, y=test_predictions, name='Testing Predictions')\n",
    "\n",
    "# Combining traces, setting layout and plotting figure\n",
    "data = [trace_y, trace_train_pred, trace_val_pred, trace_test_pred]\n",
    "layout = go.Layout(title='Predictions Plot')\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "fig.show()"
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  },
  {
   "cell_type": "markdown",
   "id": "15a9c532",
   "metadata": {
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     "end_time": "2023-03-12T10:24:58.342339",
     "exception": false,
     "start_time": "2023-03-12T10:24:58.318670",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "# <div style=\"color:white;display:fill;border-radius:20px;background-color:#f4ebdc;font-size:70%;font-family:Charter;letter-spacing:1px\"><h3 style='padding: 20px;color:#8b4726;text-align:center;'>Summary</h3></div>"
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     "status": "completed"
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   "source": [
    "So, based on what we've seen, it seems like we're on the right track with using LSTM neural networks to predict future stock prices. Our exploratory data analysis helped us to gain insights into the C3.AI Stocks Dataset and understand the trends and patterns in the data. \n",
    "In preprocessing creating 3 lag features, splitting the data into training, validation, and testing sets was also a crucial step before building and training the LSTM model on the training set.\n",
    "\n",
    "I'm quite impressed by the ability of the LSTM model to capture long-term dependencies in time-series data and make accurate predictions on future stock prices. This gives me confidence that we can use this knowledge to apply LSTM networks to other forecasting tasks as well.\n",
    "\n",
    "Overall, I believe that this notebook provides a useful basic guide for applying LSTM networks to stock price prediction tasks and provides a foundation for further exploration and development in this area."
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  }
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