timemixer_blog.ipynb

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     "text": [
      "env: PYTORCH_ENABLE_MPS_FALLBACK=1\n"
     ]
    }
   ],
   "source": [
    "%set_env PYTORCH_ENABLE_MPS_FALLBACK=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from datasetsforecast.m3 import M3\n",
    "from datasetsforecast.long_horizon import LongHorizon\n",
    "\n",
    "from neuralforecast.core import NeuralForecast\n",
    "from neuralforecast.losses.pytorch import MAE, MSE\n",
    "from neuralforecast.models import TimeMixer, PatchTST, iTransformer, NHITS, NBEATS\n",
    "\n",
    "from utilsforecast.losses import mae, mse, smape\n",
    "from utilsforecast.evaluation import evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dataset(name):\n",
    "    if name == 'M3-yearly':\n",
    "        Y_df, *_ = M3.load(\"./data\", \"Yearly\")\n",
    "        horizon = 6\n",
    "        freq = 'Y'\n",
    "    elif name == 'M3-quarterly':\n",
    "        Y_df, *_ = M3.load(\"./data\", \"Quarterly\")\n",
    "        horizon = 8\n",
    "        freq = 'Q'\n",
    "    elif name == 'M3-monthly':\n",
    "        Y_df, *_ = M3.load(\"./data\", \"Monthly\")\n",
    "        horizon = 18\n",
    "        freq = 'M'\n",
    "\n",
    "    return Y_df, horizon, freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forecasting on a short horizon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "\n",
    "DATASETS = ['M3-yearly', 'M3-quarterly', 'M3-monthly']\n",
    "\n",
    "for dataset in DATASETS:\n",
    "\n",
    "    Y_df, horizon, freq = get_dataset(dataset)\n",
    "\n",
    "    test_df = Y_df.groupby('unique_id').tail(horizon)\n",
    "    train_df = Y_df.drop(test_df.index).reset_index(drop=True)\n",
    "\n",
    "    timemixer_model = TimeMixer(input_size=2*horizon, \n",
    "                                h=horizon, \n",
    "                                n_series=1, \n",
    "                                scaler_type='identity', \n",
    "                                early_stop_patience_steps=3)\n",
    "    nbeats_model = NBEATS(input_size=2*horizon, \n",
    "                          h=horizon, \n",
    "                          scaler_type='identity', \n",
    "                          max_steps=1000, \n",
    "                          early_stop_patience_steps=3)\n",
    "    nhits_model = NHITS(input_size=2*horizon, \n",
    "                        h=horizon, \n",
    "                        scaler_type='identity', \n",
    "                        max_steps=1000, \n",
    "                        early_stop_patience_steps=3)\n",
    "\n",
    "    MODELS = [timemixer_model, nbeats_model, nhits_model]\n",
    "    MODEL_NAMES = ['TimeMixer', 'NBEATS', 'NHITS']\n",
    "\n",
    "    for i, model in enumerate(MODELS):\n",
    "        nf = NeuralForecast(models=[model], freq=freq)\n",
    "\n",
    "        start = time.time()\n",
    "\n",
    "        nf.fit(train_df, val_size=horizon)\n",
    "        preds = nf.predict()\n",
    "\n",
    "        end = time.time()\n",
    "        elapsed_time = round(end - start,0)\n",
    "\n",
    "        preds = preds.reset_index()\n",
    "        test_df = pd.merge(test_df, preds, 'left', ['ds', 'unique_id'])\n",
    "\n",
    "        evaluation = evaluate(\n",
    "            test_df,\n",
    "            metrics=[mae, smape],\n",
    "            models=[f\"{MODEL_NAMES[i]}\"],\n",
    "            target_col=\"y\",\n",
    "        )\n",
    "\n",
    "        evaluation = evaluation.drop(['unique_id'], axis=1).groupby('metric').mean().reset_index()\n",
    "\n",
    "        model_mae = evaluation[f\"{MODEL_NAMES[i]}\"][0]\n",
    "        model_smape = evaluation[f\"{MODEL_NAMES[i]}\"][1]\n",
    "\n",
    "        results.append([dataset, MODEL_NAMES[i], round(model_mae, 0), round(model_smape*100,2), elapsed_time])\n",
    "\n",
    "results_df = pd.DataFrame(data=results, columns=['dataset', 'model', 'mae', 'smape', 'time'])\n",
    "results_df.to_csv('./M3_benchmark.csv', header=True, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forecasting on a long horizon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(name):\n",
    "    if name == 'Ettm1':\n",
    "        Y_df, *_ = LongHorizon.load(directory='./', group='ETTm1')\n",
    "        Y_df['ds'] = pd.to_datetime(Y_df['ds'])\n",
    "        freq = '15T'\n",
    "        h = 96\n",
    "        val_size = 11520\n",
    "        test_size = 11520\n",
    "    elif name == 'Ettm2':\n",
    "        Y_df, *_ = LongHorizon.load(directory='./', group='ETTm2')\n",
    "        Y_df['ds'] = pd.to_datetime(Y_df['ds'])\n",
    "        freq = '15T'\n",
    "        h = 96\n",
    "        val_size = 11520\n",
    "        test_size = 11520\n",
    "\n",
    "    return Y_df, h, val_size, test_size, freq"
   ]
  },
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      "------------------------------------------------------------\n",
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      "  | Name         | Type              | Params\n",
      "---------------------------------------------------\n",
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      "---------------------------------------------------\n",
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      "  y_scale = torch.repeat_interleave(y_scale, repeats=y_hat.shape[-1], dim=-1).to(\n",
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      "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (mps), used: True\n",
      "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
      "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
      "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
      "INFO:pytorch_lightning.callbacks.model_summary:\n",
      "  | Name          | Type                   | Params\n",
      "---------------------------------------------------------\n",
      "0 | loss          | MAE                    | 0     \n",
      "1 | padder        | ConstantPad1d          | 0     \n",
      "2 | scaler        | TemporalNorm           | 0     \n",
      "3 | enc_embedding | DataEmbedding_inverted | 12.4 K\n",
      "4 | encoder       | TransEncoder           | 199 K \n",
      "5 | projector     | Linear                 | 12.4 K\n",
      "---------------------------------------------------------\n",
      "224 K     Trainable params\n",
      "0         Non-trainable params\n",
      "224 K     Total params\n",
      "0.897     Total estimated model params size (MB)\n"
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       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
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     "text": [
      "INFO:pytorch_lightning.utilities.rank_zero:Trainer already configured with model summary callbacks: [<class 'pytorch_lightning.callbacks.model_summary.ModelSummary'>]. Skipping setting a default `ModelSummary` callback.\n",
      "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (mps), used: True\n",
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       "model_id": "de034a53f99e47eba11ea95b4ef8ff91",
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      "text/plain": [
       "Predicting: |          | 0/? [00:00<?, ?it/s]"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/marcopeix/dev/neuralforecast/neuralforecast/core.py:201: FutureWarning: In a future version the predictions will have the id as a column. You can set the `NIXTLA_ID_AS_COL` environment variable to adopt the new behavior and to suppress this warning.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "DATASETS = ['Ettm1', 'Ettm2']\n",
    "\n",
    "\n",
    "for dataset in DATASETS:\n",
    "\n",
    "    Y_df, horizon, val_size, test_size, freq = load_data(dataset)\n",
    "\n",
    "    timemixer_model = TimeMixer(input_size=horizon, \n",
    "                                h=horizon, \n",
    "                                n_series=7,\n",
    "                                e_layers=2,\n",
    "                                d_model=16,\n",
    "                                d_ff=32,\n",
    "                                down_sampling_layers=3,\n",
    "                                down_sampling_window=2,\n",
    "                                learning_rate=0.01,\n",
    "                                scaler_type='robust',\n",
    "                                batch_size=16, \n",
    "                                early_stop_patience_steps=5)\n",
    "    \n",
    "    patchtst_model = PatchTST(input_size=horizon, \n",
    "                              h=horizon, \n",
    "                              encoder_layers=3,\n",
    "                              n_heads=4,\n",
    "                              hidden_size=16,\n",
    "                              dropout=0.3,\n",
    "                              patch_len=16,\n",
    "                              stride=8,\n",
    "                              scaler_type='identity', \n",
    "                              max_steps=1000, \n",
    "                              early_stop_patience_steps=5)\n",
    "    \n",
    "    iTransformer_model = iTransformer(input_size=horizon, \n",
    "                                      h=horizon, \n",
    "                                      n_series=7,\n",
    "                                      e_layers=2,\n",
    "                                      hidden_size=128,\n",
    "                                      d_ff=128,\n",
    "                                      scaler_type='identity', \n",
    "                                      max_steps=1000, \n",
    "                                      early_stop_patience_steps=3)\n",
    "\n",
    "    models = [timemixer_model, patchtst_model, iTransformer_model]\n",
    "\n",
    "    nf = NeuralForecast(models=models, freq=freq)\n",
    "\n",
    "    nf_preds = nf.cross_validation(df=Y_df, val_size=val_size, test_size=test_size, n_windows=None)\n",
    "    nf_preds = nf_preds.reset_index()\n",
    "\n",
    "    evaluation = evaluate(df=nf_preds, metrics=[mae, mse], models=['TimeMixer', 'PatchTST', 'iTransformer'])\n",
    "    evaluation.to_csv(f'{dataset}_results.csv', index=False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>TimeMixer</th>\n",
       "      <th>PatchTST</th>\n",
       "      <th>iTransformer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mae</td>\n",
       "      <td>0.333756</td>\n",
       "      <td>0.258884</td>\n",
       "      <td>0.297765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mse</td>\n",
       "      <td>0.309988</td>\n",
       "      <td>0.180819</td>\n",
       "      <td>0.221797</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  metric  TimeMixer  PatchTST  iTransformer\n",
       "0    mae   0.333756  0.258884      0.297765\n",
       "1    mse   0.309988  0.180819      0.221797"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ettm_eval = pd.read_csv('Ettm2_results.csv')\n",
    "ettm_eval = ettm_eval.drop(['unique_id'], axis=1).groupby('metric').mean().reset_index()\n",
    "ettm_eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>TimeMixer</th>\n",
       "      <th>PatchTST</th>\n",
       "      <th>iTransformer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mae</td>\n",
       "      <td>0.462627</td>\n",
       "      <td>0.365640</td>\n",
       "      <td>0.513339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mse</td>\n",
       "      <td>0.553456</td>\n",
       "      <td>0.347774</td>\n",
       "      <td>0.625772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  metric  TimeMixer  PatchTST  iTransformer\n",
       "0    mae   0.462627  0.365640      0.513339\n",
       "1    mse   0.553456  0.347774      0.625772"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ettm_eval = pd.read_csv('Ettm1_results.csv')\n",
    "ettm_eval = ettm_eval.drop(['unique_id'], axis=1).groupby('metric').mean().reset_index()\n",
    "ettm_eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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