nbeatsx_example.ipynb

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   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from src.utils.data.datasets.epf import EPF, EPFInfo\n",
    "from src.utils.pytorch.ts_dataset import TimeSeriesDataset\n",
    "from src.utils.pytorch.ts_loader import TimeSeriesLoader\n",
    "from src.nbeats.nbeats import Nbeats"
   ]
  },
  {
   "source": [
    "This notebook presents an example on how to use the NBEATSx model, including the Dataset and Loader objects which comprise our full pipeline. We will train the model to forecast the last available week of the NP market described in the paper.\n",
    "\n",
    "Note: the hyperparameters of the model in this example do not correpond to the configuration used on the main results of the paper."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "# Load data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
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   "execution_count": 2,
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     "data": {
      "text/plain": [
       "  unique_id                  ds      y\n",
       "0        NP 2013-01-01 00:00:00  31.05\n",
       "1        NP 2013-01-01 01:00:00  30.47\n",
       "2        NP 2013-01-01 02:00:00  28.92\n",
       "3        NP 2013-01-01 03:00:00  27.88\n",
       "4        NP 2013-01-01 04:00:00  26.96"
      ],
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     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "Y_df, X_df, _ = EPF.load_groups(directory='./data', groups=['NP'])\n",
    "Y_df.head()"
   ]
  },
  {
   "source": [
    "# Dataset and Data Loader"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Processing dataframes ...\nCreating ts tensor ...\n"
     ]
    }
   ],
   "source": [
    "# train_mask: 1 to keep, 0 to mask\n",
    "train_mask = np.ones(len(Y_df))\n",
    "train_mask[-168:] = 0 # Last week of data (168 hours)\n",
    "\n",
    "# Dataset object. Pre-process the DataFrame into pytorch tensors and windows.\n",
    "ts_dataset = TimeSeriesDataset(Y_df=Y_df, X_df=X_df, ts_train_mask=train_mask)\n",
    "\n",
    "# Loaders object. Sample windows of dataset object.\n",
    "# For more information on each parameter, refer to comments on Loader object.\n",
    "train_loader = TimeSeriesLoader(model='nbeats',\n",
    "                                ts_dataset=ts_dataset,\n",
    "                                window_sampling_limit=365*4*24,\n",
    "                                offset=0,\n",
    "                                input_size=7*24,\n",
    "                                output_size=24,\n",
    "                                idx_to_sample_freq=24,\n",
    "                                batch_size=512,\n",
    "                                is_train_loader=True,\n",
    "                                shuffle=True)\n",
    "\n",
    "# Validation loader (note: in this example we are also validating on the period to forecast)\n",
    "val_loader = TimeSeriesLoader(model='nbeats',\n",
    "                              ts_dataset=ts_dataset,\n",
    "                              window_sampling_limit=365*4*24,\n",
    "                              offset=0,\n",
    "                              input_size=7*24,\n",
    "                              output_size=24,\n",
    "                              idx_to_sample_freq=24,\n",
    "                              batch_size=512,\n",
    "                              is_train_loader=False,\n",
    "                              shuffle=False)"
   ]
  },
  {
   "source": [
    "# Instantiate and train model"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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   "source": [
    "# Dictionary with lags to include for y and each exogenous variable.\n",
    "# Eg: -1 corresonds to future (available for exogenous), -2 correponds to last available day, and so on.\n",
    "include_var_dict = {'y': [-8,-4,-3,-2],\n",
    "                    'Exogenous1': [-8,-2,-1],\n",
    "                    'Exogenous2': [-8,-2,-1],\n",
    "                    'week_day': [-1]}\n",
    "\n",
    "model = Nbeats(input_size_multiplier=7,\n",
    "                output_size=24,\n",
    "                shared_weights=False,\n",
    "                initialization='glorot_normal',\n",
    "                activation='selu',\n",
    "                stack_types=['identity']+['exogenous_tcn'],\n",
    "                n_blocks=[1, 1],\n",
    "                n_layers=[2, 2],\n",
    "                n_hidden=[[512,512], [512,512]],\n",
    "                n_harmonics=0, # not used with exogenous_tcn\n",
    "                n_polynomials=0, # not used with exogenous_tcn\n",
    "                x_s_n_hidden = 0,\n",
    "                exogenous_n_channels=9,\n",
    "                include_var_dict=include_var_dict,\n",
    "                t_cols=ts_dataset.t_cols,\n",
    "                batch_normalization = True,\n",
    "                dropout_prob_theta=0.1,\n",
    "                dropout_prob_exogenous=0,\n",
    "                learning_rate=0.001,\n",
    "                lr_decay=0.5,\n",
    "                n_lr_decay_steps=3,\n",
    "                early_stopping=10,\n",
    "                weight_decay=0,\n",
    "                l1_theta=0,\n",
    "                n_iterations=2_000,\n",
    "                loss='MAE',\n",
    "                loss_hypar=0.5,\n",
    "                val_loss='MAE',\n",
    "                seasonality=24, # not used: only used with MASE loss\n",
    "                random_seed=1)"
   ]
  },
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     "text": [
      "\n",
      "\n",
      "============================== Start fitting ==============================\n",
      "Step: 50, Time: 16.409, Insample MAE: 5.83938, Outsample MAE: 4.39927\n",
      "Step: 100, Time: 33.134, Insample MAE: 2.44916, Outsample MAE: 3.42556\n",
      "Step: 150, Time: 49.876, Insample MAE: 1.95872, Outsample MAE: 3.27034\n",
      "Step: 200, Time: 66.734, Insample MAE: 1.66577, Outsample MAE: 2.54300\n",
      "Step: 250, Time: 83.622, Insample MAE: 1.66716, Outsample MAE: 2.27116\n",
      "Step: 300, Time: 100.542, Insample MAE: 1.64837, Outsample MAE: 1.88122\n",
      "Step: 350, Time: 117.531, Insample MAE: 1.78279, Outsample MAE: 1.98933\n",
      "Step: 400, Time: 134.531, Insample MAE: 1.76788, Outsample MAE: 2.08533\n",
      "Step: 450, Time: 151.632, Insample MAE: 1.63130, Outsample MAE: 1.70282\n",
      "Step: 500, Time: 168.747, Insample MAE: 1.64432, Outsample MAE: 6.03191\n",
      "Step: 550, Time: 185.964, Insample MAE: 1.47742, Outsample MAE: 1.68312\n",
      "Step: 600, Time: 203.181, Insample MAE: 1.49786, Outsample MAE: 4.21936\n",
      "Step: 650, Time: 220.507, Insample MAE: 1.42323, Outsample MAE: 1.93227\n",
      "Step: 700, Time: 237.585, Insample MAE: 1.44609, Outsample MAE: 1.66294\n",
      "Step: 750, Time: 254.749, Insample MAE: 1.49243, Outsample MAE: 2.82989\n",
      "Step: 800, Time: 272.323, Insample MAE: 1.51305, Outsample MAE: 5.09496\n",
      "Step: 850, Time: 291.720, Insample MAE: 1.61729, Outsample MAE: 1.71840\n",
      "Step: 900, Time: 312.187, Insample MAE: 1.42796, Outsample MAE: 2.15482\n",
      "Step: 950, Time: 333.070, Insample MAE: 1.36555, Outsample MAE: 1.38699\n",
      "Step: 1000, Time: 353.921, Insample MAE: 1.43740, Outsample MAE: 2.73787\n",
      "Step: 1050, Time: 372.782, Insample MAE: 1.41395, Outsample MAE: 2.85002\n",
      "Step: 1100, Time: 390.022, Insample MAE: 1.39468, Outsample MAE: 1.74032\n",
      "Step: 1150, Time: 407.229, Insample MAE: 1.35997, Outsample MAE: 3.02875\n",
      "Step: 1200, Time: 424.468, Insample MAE: 1.29870, Outsample MAE: 6.58525\n",
      "Step: 1250, Time: 441.800, Insample MAE: 1.45839, Outsample MAE: 4.73858\n",
      "Step: 1300, Time: 459.627, Insample MAE: 1.59119, Outsample MAE: 2.38898\n",
      "Step: 1350, Time: 481.101, Insample MAE: 1.37958, Outsample MAE: 4.00619\n",
      "Step: 1400, Time: 504.903, Insample MAE: 1.34632, Outsample MAE: 2.20403\n",
      "Step: 1450, Time: 528.766, Insample MAE: 1.35892, Outsample MAE: 1.56017\n",
      "\n",
      "\n",
      "-------------------  Stopped training by early stopping -------------------\n",
      "Step: 1450, Time: 528.783, Insample MAE: 1.36555, Outsample MAE: 1.38699\n",
      "==============================  End fitting  ==============================\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model.fit(train_ts_loader=train_loader, val_ts_loader=val_loader, eval_steps=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true, y_hat, *_ = model.predict(ts_loader=val_loader, return_decomposition=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price')"
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     },
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     "execution_count": 12
    },
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(range(336), Y_df['y'].values[-336:], label='Price')\n",
    "plt.plot(range(168,336), y_hat.flatten(), linestyle='dashed', label='Forecast')\n",
    "plt.axvline(168, color='black')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.xlabel('Hour')\n",
    "plt.ylabel('Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}