dataset_statistics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy import array\n",
    "from numpy.polynomial.polynomial import polyfit\n",
    "from tqdm import tqdm\n",
    "\n",
    "from xlstm_mixer.lit.data import Task, TSLibDataModule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 491/491 [00:00<00:00, 1246.46it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.50337593, 0.49924713])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def hurst(ts: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"\n",
    "    From: https://github.com/RyanWangZf/Hurst-exponent-R-S-analysis-/blob/master/Hurst.py\n",
    "    \"\"\"\n",
    "    N = ts.shape[0]\n",
    "    if N < 20:\n",
    "        raise ValueError(\n",
    "            \"Time series is too short! input series ought to have at least 20 samples!\"\n",
    "        )\n",
    "\n",
    "    max_k = N // 2\n",
    "    R_S_dict = []\n",
    "    for k in tqdm(range(10, max_k + 1)):\n",
    "        R, S = 0, 0\n",
    "        # split ts into subsets\n",
    "        subset_list = [ts[i : i + k, :] for i in range(0, N, k)]\n",
    "        if np.mod(N, k) > 0:\n",
    "            subset_list.pop()\n",
    "\n",
    "        # calc mean of every subset\n",
    "        for subset in subset_list:\n",
    "            cumsum_list = np.cumsum(subset - subset.mean(axis=0), axis=0)\n",
    "            R += np.max(cumsum_list, axis=0) - np.min(cumsum_list, axis=0)\n",
    "            S += np.std(subset, axis=0)\n",
    "        R_S_dict.append((R / len(subset_list), S / len(subset_list), k))\n",
    "\n",
    "    log_R_S = []\n",
    "    log_n = []\n",
    "\n",
    "    for R, S, n in R_S_dict:\n",
    "        log_n.append(np.log(n))\n",
    "        R_S = (R + np.spacing(1)) / (S + np.spacing(1))\n",
    "        log_R_S.append(np.log(R_S))\n",
    "\n",
    "    return polyfit(log_n, log_R_S, 1)[-1, :]\n",
    "\n",
    "\n",
    "# Try:\n",
    "ts = np.random.rand(1_000, 2)\n",
    "# ts[:, 0] = np.cos(np.arange(1_000) % 2.0)\n",
    "# ts[:, 0] = np.cos(np.arange(1_000) / 1_000.0)\n",
    "hurst(ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = [\n",
    "    \"Weather\",\n",
    "    \"ETTh1\",\n",
    "    \"ETTh2\",\n",
    "    \"ETTm1\",\n",
    "    \"ETTm2\",\n",
    "    \"Electricity\",\n",
    "    \"Exchange\",\n",
    "    \"Solar\",\n",
    "    \"Traffic\",\n",
    "    \"Ili\",\n",
    "]\n",
    "H_values = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating Hurst Exponent for Exchange dataset\n",
      "train 4256\n",
      "val 41\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3785/3785 [00:04<00:00, 799.87it/s] \n"
     ]
    }
   ],
   "source": [
    "for dataset in datasets:\n",
    "    if dataset in H_values:\n",
    "        print(f\"Skipping {dataset} dataset as it has already been calculated\")\n",
    "        continue\n",
    "\n",
    "    print(f\"Calculating Hurst Exponent for {dataset} dataset\")\n",
    "\n",
    "    data_module = TSLibDataModule(\n",
    "        task=Task.LONG_TERM_FORECAST, dataset_name=dataset, num_workers=0\n",
    "    )\n",
    "    data_module.embed = \"timeF\"\n",
    "    data_module.freq = \"W\"\n",
    "    data_module.seq_len = 336\n",
    "    data_module.label_len = 0\n",
    "    data_module.pred_len = 720\n",
    "    data_module.features = \"MS\"\n",
    "    data_module.target = \"OT\"\n",
    "    data_module.seasonal_patterns = None\n",
    "\n",
    "    if dataset == \"Ili\":\n",
    "        data_module.seq_len = 20\n",
    "        data_module.pred_len = 30\n",
    "\n",
    "    data_module.setup(\"fit\")\n",
    "    data = data_module.train_dataset._data_all  # includes train, val, and test\n",
    "\n",
    "    H_values_variates = hurst(data)\n",
    "    H_values[dataset] = H_values_variates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "H_values = {\n",
    "    \"Weather\": array(\n",
    "        [\n",
    "            0.80142176,\n",
    "            1.01528083,\n",
    "            1.00631775,\n",
    "            0.90463039,\n",
    "            0.94611491,\n",
    "            1.01963684,\n",
    "            0.90687494,\n",
    "            0.97728952,\n",
    "            0.90456967,\n",
    "            0.90463628,\n",
    "            0.95788105,\n",
    "            0.33302144,\n",
    "            0.81261352,\n",
    "            0.8552803,\n",
    "            0.63193458,\n",
    "            0.71179446,\n",
    "            0.99884365,\n",
    "            1.00576408,\n",
    "            0.99772455,\n",
    "            1.03830606,\n",
    "            0.50183524,\n",
    "        ]\n",
    "    ),\n",
    "    \"ETTh1\": array(\n",
    "        [\n",
    "            0.9834697,\n",
    "            1.04147944,\n",
    "            0.96823539,\n",
    "            1.07006409,\n",
    "            1.0044969,\n",
    "            0.94267486,\n",
    "            1.09817671,\n",
    "        ]\n",
    "    ),\n",
    "    \"ETTh2\": array(\n",
    "        [\n",
    "            0.90565816,\n",
    "            1.04367403,\n",
    "            0.99165408,\n",
    "            1.06774359,\n",
    "            0.9946176,\n",
    "            0.94996812,\n",
    "            1.13327527,\n",
    "        ]\n",
    "    ),\n",
    "    \"ETTm1\": array(\n",
    "        [\n",
    "            0.97795727,\n",
    "            1.03778551,\n",
    "            0.96186627,\n",
    "            1.06656588,\n",
    "            0.99844366,\n",
    "            0.94484742,\n",
    "            1.09465053,\n",
    "        ]\n",
    "    ),\n",
    "    \"ETTm2\": array(\n",
    "        [\n",
    "            0.90802315,\n",
    "            1.04222523,\n",
    "            0.9918761,\n",
    "            1.06492962,\n",
    "            0.99685033,\n",
    "            0.95316695,\n",
    "            1.12737572,\n",
    "        ]\n",
    "    ),\n",
    "    \"Electricity\": array(\n",
    "        [\n",
    "            1.07827311,\n",
    "            1.05093915,\n",
    "            1.0192512,\n",
    "            1.19688509,\n",
    "            1.13573192,\n",
    "            1.20999045,\n",
    "            0.96774448,\n",
    "            0.99494064,\n",
    "            1.07735322,\n",
    "            1.02977754,\n",
    "            1.1772559,\n",
    "            0.6639689,\n",
    "            1.05976412,\n",
    "            1.00832099,\n",
    "            1.04718738,\n",
    "            0.93513461,\n",
    "            1.00087996,\n",
    "            1.05888557,\n",
    "            1.09027116,\n",
    "            0.90033111,\n",
    "            0.88597373,\n",
    "            1.10136505,\n",
    "            1.17064897,\n",
    "            1.20212543,\n",
    "            1.15600716,\n",
    "            0.77216082,\n",
    "            1.14630534,\n",
    "            0.89518123,\n",
    "            1.04208161,\n",
    "            0.77132208,\n",
    "            0.97676416,\n",
    "            0.99139871,\n",
    "            1.2407155,\n",
    "            1.03368901,\n",
    "            1.17595976,\n",
    "            1.08778418,\n",
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    "        ]\n",
    "    ),\n",
    "    \"Solar\": array(\n",
    "        [\n",
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    "            0.67253111,\n",
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    "            0.8244742,\n",
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    "            0.86856136,\n",
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    "            0.70514394,\n",
    "            1.17649142,\n",
    "            0.94464866,\n",
    "            0.58067035,\n",
    "            0.57754956,\n",
    "            0.61154594,\n",
    "            0.72579702,\n",
    "            0.94286907,\n",
    "            0.80156022,\n",
    "            0.9699543,\n",
    "            0.88586923,\n",
    "            0.8174382,\n",
    "            0.90114596,\n",
    "            0.77572541,\n",
    "            0.99581627,\n",
    "            0.84852145,\n",
    "            1.18517165,\n",
    "            0.83324639,\n",
    "            0.78769043,\n",
    "            0.86041902,\n",
    "            0.86289791,\n",
    "            0.76923162,\n",
    "            0.61399493,\n",
    "            0.54209793,\n",
    "            0.58197807,\n",
    "            0.58553049,\n",
    "            0.88341214,\n",
    "            0.75499833,\n",
    "            1.08516962,\n",
    "            0.89433255,\n",
    "            0.60128078,\n",
    "            1.15651714,\n",
    "            0.49045712,\n",
    "            0.82827364,\n",
    "            0.93499578,\n",
    "            1.161046,\n",
    "            1.0060731,\n",
    "            0.78048954,\n",
    "        ]\n",
    "    ),\n",
    "    \"Ili\": array(\n",
    "        [\n",
    "            0.49920406,\n",
    "            0.54239663,\n",
    "            0.70616712,\n",
    "            0.67185809,\n",
    "            0.68459424,\n",
    "            0.79198604,\n",
    "            0.90693042,\n",
    "        ]\n",
    "    ),\n",
    "    \"Exchange\": array(\n",
    "        [\n",
    "            0.99598133,\n",
    "            0.98883904,\n",
    "            0.99674804,\n",
    "            0.99275004,\n",
    "            1.05859793,\n",
    "            0.97667778,\n",
    "            1.01972458,\n",
    "            0.9681607,\n",
    "        ]\n",
    "    ),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weather: 0.864 ± 0.177\n",
      "ETTh1: 0.985 ± 0.021\n",
      "ETTh2: 0.977 ± 0.034\n",
      "ETTm1: 0.983 ± 0.021\n",
      "ETTm2: 0.979 ± 0.033\n",
      "Electricity: 0.975 ± 0.065\n",
      "Solar: 0.739 ± 0.043\n",
      "Traffic: 0.745 ± 0.141\n",
      "Ili: 0.686 ± 0.129\n",
      "Exchange: 0.990 ± 0.011\n",
      "\n",
      "Weather: 0.333 - 1.000\n",
      "ETTh1: 0.943 - 1.000\n",
      "ETTh2: 0.906 - 1.000\n",
      "ETTm1: 0.945 - 1.000\n",
      "ETTm2: 0.908 - 1.000\n",
      "Electricity: 0.555 - 1.000\n",
      "Solar: 0.631 - 0.822\n",
      "Traffic: 0.162 - 1.000\n",
      "Ili: 0.499 - 0.907\n",
      "Exchange: 0.968 - 1.000\n"
     ]
    }
   ],
   "source": [
    "clipped_H_values = {\n",
    "    name: np.clip(values, 0.0, 1.0) for name, values in H_values.items()\n",
    "}\n",
    "\n",
    "for name, all_values in clipped_H_values.items():\n",
    "    print(f\"{name}: {np.mean(all_values):.3f} ± {np.std(all_values):.3f}\")\n",
    "\n",
    "print()\n",
    "\n",
    "for name, all_values in clipped_H_values.items():\n",
    "    print(f\"{name}: {np.min(all_values):.3f} - {np.max(all_values):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
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