{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6f4b888a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# you need the nok.xml file to run this\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7d351b80",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "import xml.etree.ElementTree as ET\n",
    "\n",
    "tree = ET.parse('nok.xml')\n",
    "root = tree.getroot()\n",
    "\n",
    "nok_eur_dict = {}\n",
    "\n",
    "node = root\n",
    "for child in node:\n",
    "    if \"DataSet\" in child.tag:\n",
    "        node = child\n",
    "        for child in node:\n",
    "            if \"Series\" in child.tag:\n",
    "                node = child\n",
    "                for child in node:\n",
    "                    if \"Obs\" in child.tag:\n",
    "                        node = child\n",
    "                        # print(node.attrib[\"TIME_PERIOD\"], node.attrib[\"OBS_VALUE\"], sep=\"\\t\")\n",
    "                        nok_eur_dict[datetime.strptime(node.attrib[\"TIME_PERIOD\"], \\\n",
    "                                                       \"%Y-%m-%d\")] = float(node.attrib[\"OBS_VALUE\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d4d906b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\t9.64\t2.265921108622454\n",
      "1\t9.667\t2.268718023482468\n",
      "4\t9.7675\t2.2790605479470885\n",
      "4\t9.7675\t2.2790605479470885\n",
      "4\t9.7675\t2.2790605479470885\n",
      "5\t9.7553\t2.277810727061961\n",
      "6\t9.9388\t2.2964462890346358\n",
      "7\t10.013\t2.303884248725666\n",
      "8\t10.0035\t2.3029350317583335\n",
      "11\t9.9188\t2.2944319462376583\n",
      "11\t9.9188\t2.2944319462376583\n",
      "11\t9.9188\t2.2944319462376583\n",
      "12\t9.8945\t2.2919790472068216\n",
      "13\t9.9483\t2.297401682301891\n",
      "14\t10.0615\t2.308716258924286\n",
      "15\t9.9836\t2.30094374672192\n",
      "18\t9.9718\t2.2997611093029438\n",
      "18\t9.9718\t2.2997611093029438\n",
      "18\t9.9718\t2.2997611093029438\n",
      "19\t9.9988\t2.3024650857934694\n",
      "20\t10.1125\t2.31377228238461\n",
      "21\t10.1203\t2.3145433076887114\n",
      "22\t10.1985\t2.322240650652487\n",
      "25\t10.2826\t2.3304531463365183\n",
      "25\t10.2826\t2.3304531463365183\n",
      "25\t10.2826\t2.3304531463365183\n",
      "26\t10.273\t2.3295190942341266\n",
      "27\t10.2858\t2.3307643032593535\n",
      "28\t10.235\t2.325813219113253\n",
      "29\t10.2335\t2.325666652437367\n",
      "32\t10.3585\t2.3378074387038064\n",
      "32\t10.3585\t2.3378074387038064\n",
      "32\t10.3585\t2.3378074387038064\n",
      "33\t10.3473\t2.336725616113777\n",
      "34\t10.4576\t2.3473289868034373\n",
      "35\t10.4518\t2.346774212381526\n",
      "36\t10.5838\t2.3593245301866466\n",
      "39\t10.5655\t2.357593976024845\n",
      "39\t10.5655\t2.357593976024845\n",
      "39\t10.5655\t2.357593976024845\n",
      "40\t10.4915\t2.350565405512613\n",
      "41\t10.4858\t2.350021960918667\n",
      "42\t10.4278\t2.34447531675423\n",
      "43\t10.4498\t2.346582839471691\n",
      "46\t10.3378\t2.335807080484949\n",
      "46\t10.3378\t2.335807080484949\n",
      "46\t10.3378\t2.335807080484949\n",
      "47\t10.4235\t2.3440628724403547\n",
      "48\t10.4145\t2.343199065879301\n",
      "49\t10.3525\t2.337228036437585\n",
      "50\t10.3323\t2.335274910816703\n",
      "53\t10.342\t2.33621327397403\n",
      "53\t10.342\t2.33621327397403\n",
      "53\t10.342\t2.33621327397403\n",
      "54\t10.3528\t2.3372570145253233\n",
      "55\t10.3823\t2.340102433153993\n",
      "56\t10.402\t2.3419980953508808\n",
      "57\t10.4315\t2.3448300745877915\n",
      "60\t10.392\t2.341036279368298\n",
      "60\t10.392\t2.341036279368298\n",
      "60\t10.392\t2.341036279368298\n",
      "61\t10.391\t2.3409400468705095\n",
      "62\t10.3408\t2.336097235526494\n",
      "63\t10.342\t2.33621327397403\n",
      "64\t10.2695\t2.3291783372635666\n",
      "67\t10.3028\t2.3324157029527197\n",
      "67\t10.3028\t2.3324157029527197\n",
      "67\t10.3028\t2.3324157029527197\n",
      "68\t10.1835\t2.3207687634276746\n",
      "69\t10.2388\t2.32618442524437\n",
      "70\t10.3543\t2.337401892369408\n",
      "71\t10.2019\t2.322573977453087\n",
      "74\t10.2555\t2.3278141470398213\n",
      "74\t10.2555\t2.3278141470398213\n",
      "74\t10.2555\t2.3278141470398213\n",
      "75\t10.2795\t2.330151620711851\n",
      "76\t10.322\t2.334277539726535\n",
      "77\t10.3615\t2.338097013995405\n",
      "78\t10.2635\t2.3285939121750965\n",
      "81\t10.3143\t2.3335312818840594\n",
      "81\t10.3143\t2.3335312818840594\n",
      "81\t10.3143\t2.3335312818840594\n",
      "82\t10.357\t2.337662619606742\n",
      "83\t10.3675\t2.3386759131383994\n",
      "84\t10.498\t2.351184762830108\n",
      "85\t10.486\t2.3500410341503413\n",
      "88\t10.4898\t2.350403356449521\n",
      "88\t10.4898\t2.350403356449521\n",
      "88\t10.4898\t2.350403356449521\n",
      "89\t10.4445\t2.3460755240685742\n",
      "90\t10.3659\t2.338521572798351\n",
      "91\t10.3435\t2.336358303100967\n",
      "92\t10.2985\t2.3319982535623955\n",
      "95\t10.364\t2.3383382626998634\n",
      "95\t10.364\t2.3383382626998634\n",
      "95\t10.364\t2.3383382626998634\n",
      "96\t10.3313\t2.3351781222609067\n",
      "97\t10.2648\t2.3287205665986717\n",
      "98\t10.2495\t2.3272289239068056\n",
      "99\t10.2615\t2.3283990278870252\n",
      "102\t10.3366\t2.3356909948910407\n",
      "102\t10.3366\t2.3356909948910407\n",
      "102\t10.3366\t2.3356909948910407\n",
      "103\t10.4408\t2.345721207871181\n",
      "104\t10.5255\t2.353800884378316\n",
      "105\t10.488\t2.350231746461399\n",
      "106\t10.5345\t2.3546555852850477\n",
      "109\t10.5548\t2.356580732755058\n",
      "109\t10.5548\t2.356580732755058\n",
      "109\t10.5548\t2.356580732755058\n",
      "110\t10.4679\t2.3483134316990757\n",
      "111\t10.362\t2.3381452683925965\n",
      "112\t10.4013\t2.3419307983354782\n",
      "113\t10.4833\t2.3497835148230464\n",
      "116\t10.5025\t2.3516133240614\n",
      "116\t10.5025\t2.3516133240614\n",
      "116\t10.5025\t2.3516133240614\n",
      "117\t10.5098\t2.352308155212078\n",
      "118\t10.4309\t2.344772554839329\n",
      "119\t10.4123\t2.342987799625418\n",
      "120\t10.4448\t2.3461042469075415\n",
      "124\t10.4895\t2.3503747568298943\n",
      "124\t10.4895\t2.3503747568298943\n",
      "124\t10.4895\t2.3503747568298943\n",
      "124\t10.4895\t2.3503747568298943\n",
      "125\t10.4495\t2.346554130376169\n",
      "126\t10.55\t2.3561258599220753\n",
      "127\t10.5138\t2.3526886799602913\n",
      "130\t10.5135\t2.352660145626352\n",
      "130\t10.5135\t2.352660145626352\n",
      "130\t10.5135\t2.352660145626352\n",
      "131\t10.528\t2.3540383745829616\n",
      "132\t10.738\t2.3737888520003776\n",
      "133\t10.7248\t2.3725588166215563\n",
      "134\t10.807\t2.3801940723210286\n",
      "137\t10.6108\t2.361872350348916\n",
      "137\t10.6108\t2.361872350348916\n",
      "137\t10.6108\t2.361872350348916\n",
      "138\t10.6785\t2.3682323742299007\n",
      "139\t10.738\t2.3737888520003776\n",
      "140\t10.7228\t2.3723723155672847\n",
      "141\t10.6945\t2.369729590623376\n",
      "144\t10.6945\t2.369729590623376\n",
      "144\t10.6945\t2.369729590623376\n",
      "144\t10.6945\t2.369729590623376\n",
      "145\t10.7463\t2.374561509281548\n",
      "146\t10.6553\t2.366057420949615\n",
      "147\t10.746\t2.37453359230663\n",
      "148\t10.7135\t2.3715046284586436\n",
      "151\t10.6835\t2.368700495200906\n",
      "151\t10.6835\t2.368700495200906\n",
      "151\t10.6835\t2.368700495200906\n",
      "152\t10.7193\t2.372045855003153\n",
      "153\t10.7896\t2.378582707220511\n",
      "154\t10.762\t2.376021411067228\n",
      "155\t10.76\t2.3758355547336385\n",
      "158\t10.7925\t2.3788514484474956\n",
      "158\t10.7925\t2.3788514484474956\n",
      "158\t10.7925\t2.3788514484474956\n",
      "159\t10.9083\t2.3895239673554234\n",
      "160\t10.8569\t2.384800822559788\n",
      "161\t10.9535\t2.393659039893417\n",
      "162\t10.9783\t2.395920597136348\n",
      "165\t11.0428\t2.4017786318804313\n",
      "165\t11.0428\t2.4017786318804313\n",
      "165\t11.0428\t2.4017786318804313\n",
      "166\t11.1253\t2.409221793980855\n",
      "167\t11.026\t2.4002561201674917\n",
      "168\t10.9123\t2.3898905933918115\n",
      "169\t10.871\t2.3860986932218635\n",
      "172\t10.8213\t2.3815164140754645\n",
      "172\t10.8213\t2.3815164140754645\n",
      "172\t10.8213\t2.3815164140754645\n",
      "173\t10.8778\t2.386724015099078\n",
      "174\t10.9193\t2.3905318656979992\n",
      "175\t10.9513\t2.3934581706768623\n",
      "176\t11.015\t2.3992579802543066\n",
      "179\t10.9683\t2.395009294183539\n",
      "179\t10.9683\t2.395009294183539\n",
      "179\t10.9683\t2.395009294183539\n",
      "180\t10.9468\t2.3930471761099046\n",
      "181\t10.9665\t2.3948451714193433\n",
      "182\t10.9545\t2.393750330746137\n",
      "183\t10.937\t2.3921515363530887\n",
      "186\t10.9635\t2.394571573606419\n",
      "186\t10.9635\t2.394571573606419\n",
      "186\t10.9635\t2.394571573606419\n",
      "187\t10.9713\t2.395282772279648\n",
      "188\t11.0365\t2.4012079615991295\n",
      "189\t11.085\t2.4055928430682747\n",
      "190\t11.061\t2.403425407920117\n",
      "193\t11.1109\t2.40792660847137\n",
      "193\t11.1109\t2.40792660847137\n",
      "193\t11.1109\t2.40792660847137\n",
      "194\t11.1935\t2.4153332526857993\n",
      "195\t11.2205\t2.417742462381494\n",
      "196\t11.2715\t2.4222774159120504\n",
      "197\t11.29\t2.4239173781615704\n",
      "200\t11.3893\t2.432674318151718\n",
      "200\t11.3893\t2.432674318151718\n",
      "200\t11.3893\t2.432674318151718\n",
      "201\t11.335\t2.4278952839594923\n",
      "202\t11.34\t2.428336298299606\n",
      "203\t11.4487\t2.4378761864365184\n",
      "204\t11.4218\t2.435523810021079\n",
      "207\t11.421\t2.43545376606847\n",
      "207\t11.421\t2.43545376606847\n",
      "207\t11.421\t2.43545376606847\n",
      "208\t11.3125\t2.4259083090260445\n",
      "209\t11.332\t2.42763058196827\n",
      "210\t11.284\t2.42338579313975\n",
      "211\t11.3065\t2.4253777815809117\n",
      "214\t11.338\t2.428159915902112\n",
      "214\t11.338\t2.428159915902112\n",
      "214\t11.338\t2.428159915902112\n",
      "215\t11.324\t2.426924367249653\n",
      "216\t11.3005\t2.4248469725270008\n",
      "217\t11.357\t2.4298342939095092\n",
      "218\t11.394\t2.433086901058204\n",
      "221\t11.2515\t2.4205014530956634\n",
      "221\t11.2515\t2.4205014530956634\n",
      "221\t11.2515\t2.4205014530956634\n",
      "222\t11.2345\t2.4189894008641484\n",
      "223\t11.399\t2.4335256322546415\n",
      "224\t11.3855\t2.4323406159865826\n",
      "229\t11.5435\t2.446122507990242\n",
      "229\t11.5435\t2.446122507990242\n",
      "229\t11.5435\t2.446122507990242\n",
      "229\t11.5435\t2.446122507990242\n",
      "229\t11.5435\t2.446122507990242\n",
      "230\t11.4745\t2.4401271820125294\n",
      "231\t11.457\t2.4386008969114887\n",
      "232\t11.402\t2.43378877860939\n",
      "235\t11.364\t2.4304504639919418\n",
      "235\t11.364\t2.4304504639919418\n",
      "235\t11.364\t2.4304504639919418\n",
      "236\t11.4675\t2.43951694748919\n",
      "237\t11.5335\t2.445255844158208\n",
      "238\t11.604\t2.4513498662591573\n",
      "239\t11.6505\t2.4553490975456747\n",
      "242\t11.647\t2.455048636120515\n",
      "242\t11.647\t2.455048636120515\n",
      "242\t11.647\t2.455048636120515\n",
      "243\t11.695\t2.4591614000361424\n",
      "244\t11.722\t2.4614674180542373\n",
      "245\t11.714\t2.4607847076675906\n",
      "246\t11.791\t2.467336528594536\n",
      "250\t11.762\t2.464873996037764\n",
      "250\t11.762\t2.464873996037764\n",
      "250\t11.762\t2.464873996037764\n",
      "250\t11.762\t2.464873996037764\n",
      "251\t11.886\t2.475361236944469\n",
      "252\t11.8282\t2.4704865108766967\n",
      "253\t11.6895\t2.4586910029695885\n",
      "256\t11.5505\t2.4467287260741055\n",
      "256\t11.5505\t2.4467287260741055\n",
      "256\t11.5505\t2.4467287260741055\n",
      "257\t11.575\t2.448847599972298\n",
      "258\t11.5485\t2.446555558404077\n",
      "259\t11.518\t2.4439110290844805\n",
      "260\t11.631\t2.4536739473569016\n",
      "263\t11.583\t2.449538505950209\n",
      "263\t11.583\t2.449538505950209\n",
      "263\t11.583\t2.449538505950209\n",
      "264\t11.613\t2.4521251602636207\n",
      "265\t11.7295\t2.4621070360109307\n",
      "266\t11.737\t2.462746245117987\n",
      "267\t11.701\t2.459674308236821\n",
      "270\t11.7765\t2.466106020324283\n",
      "270\t11.7765\t2.466106020324283\n",
      "270\t11.7765\t2.466106020324283\n",
      "271\t11.792\t2.4674213354469807\n",
      "272\t11.8175\t2.4695814836852543\n",
      "273\t11.7695\t2.465511439486245\n",
      "274\t11.8218\t2.46994528464784\n",
      "277\t11.9175\t2.478007908096844\n",
      "277\t11.9175\t2.478007908096844\n",
      "277\t11.9175\t2.478007908096844\n",
      "278\t11.8745\t2.4743932437710954\n",
      "279\t12.0045\t2.4852815794930736\n",
      "280\t11.967\t2.4821528615913793\n",
      "281\t11.845\t2.471905837610749\n",
      "284\t11.8275\t2.470427328523166\n",
      "284\t11.8275\t2.470427328523166\n",
      "284\t11.8275\t2.470427328523166\n",
      "285\t11.8775\t2.4746458540789393\n",
      "286\t11.7975\t2.4678876446184055\n",
      "287\t11.758\t2.464533859980063\n",
      "288\t11.612\t2.4520390461623833\n",
      "291\t11.634\t2.4539318454885697\n",
      "291\t11.634\t2.4539318454885697\n",
      "291\t11.634\t2.4539318454885697\n",
      "292\t11.6235\t2.4530289108899774\n",
      "293\t11.476\t2.4402578981191185\n",
      "294\t11.5025\t2.4425644030474865\n",
      "295\t11.517\t2.4438242046867704\n",
      "298\t11.6335\t2.453888867085211\n",
      "298\t11.6335\t2.453888867085211\n",
      "298\t11.6335\t2.453888867085211\n",
      "299\t11.7155\t2.46091275137335\n",
      "300\t11.747\t2.463597888841749\n",
      "301\t11.599\t2.450918887499739\n",
      "302\t11.8565\t2.472876240404027\n",
      "305\t11.708\t2.4602723688296395\n",
      "305\t11.708\t2.4602723688296395\n",
      "305\t11.708\t2.4602723688296395\n",
      "306\t11.7563\t2.464389267115044\n",
      "307\t11.8395\t2.4714413988344295\n",
      "308\t11.7645\t2.465086522338694\n",
      "309\t11.704\t2.4599306637178233\n",
      "312\t11.6945\t2.4591186458088115\n",
      "312\t11.6945\t2.4591186458088115\n",
      "312\t11.6945\t2.4591186458088115\n",
      "313\t11.5958\t2.450642963584008\n",
      "314\t11.6455\t2.4549198392973253\n",
      "315\t11.637\t2.4541896771259397\n",
      "316\t11.7085\t2.46031507375993\n",
      "319\t11.5635\t2.4478535855852894\n",
      "319\t11.5635\t2.4478535855852894\n",
      "319\t11.5635\t2.4478535855852894\n",
      "320\t11.452\t2.4381643872358687\n",
      "321\t11.331\t2.4275423323984295\n",
      "322\t11.248\t2.420190335068309\n",
      "323\t11.2105\t2.416850839122389\n",
      "326\t11.242\t2.419656764579883\n",
      "326\t11.242\t2.419656764579883\n",
      "326\t11.242\t2.419656764579883\n",
      "327\t11.3395\t2.4282922056167795\n",
      "328\t11.266\t2.421789340472522\n",
      "329\t11.1555\t2.4119326498293274\n",
      "330\t11.1775\t2.4139028291259987\n",
      "333\t11.1925\t2.4152439111329835\n",
      "333\t11.1925\t2.4152439111329835\n",
      "333\t11.1925\t2.4152439111329835\n",
      "334\t11.1605\t2.41238075881693\n",
      "335\t11.2\t2.4159137783010487\n",
      "336\t11.1275\t2.4094195219078722\n",
      "337\t11.168\t2.4130525460200167\n",
      "340\t11.1805\t2.4141711894460633\n",
      "340\t11.1805\t2.4141711894460633\n",
      "340\t11.1805\t2.4141711894460633\n",
      "341\t11.211\t2.4168954391714568\n",
      "342\t11.1615\t2.4124703565232886\n",
      "343\t11.2765\t2.4227209142340262\n",
      "344\t11.1858\t2.4146451167473337\n",
      "347\t11.1485\t2.4113049597082883\n",
      "347\t11.1485\t2.4113049597082883\n",
      "347\t11.1485\t2.4113049597082883\n",
      "348\t11.295\t2.4243601499199667\n",
      "349\t11.2063\t2.416476120157225\n",
      "350\t11.1785\t2.4139922905681765\n",
      "351\t11.4225\t2.4355850944552895\n",
      "354\t11.434\t2.4365913728417796\n",
      "354\t11.434\t2.4365913728417796\n",
      "354\t11.434\t2.4365913728417796\n",
      "355\t11.4705\t2.4397785221702524\n",
      "356\t11.453\t2.438251704415579\n",
      "357\t11.4985\t2.4422165920792396\n",
      "358\t11.58\t2.449279472144849\n",
      "361\t11.5205\t2.44412805710365\n",
      "361\t11.5205\t2.44412805710365\n",
      "361\t11.5205\t2.44412805710365\n",
      "362\t11.5195\t2.4440412515480396\n",
      "363\t11.573\t2.4486747988659405\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "num_entries = 256\n",
    "\n",
    "dates = sorted(nok_eur_dict.keys())[len(nok_eur_dict) - num_entries:]\n",
    "\n",
    "t_values = []\n",
    "d_values = []\n",
    "\n",
    "t_prev = -1\n",
    "for date in dates:\n",
    "    t_relative = (date - dates[0]).days\n",
    "    for i in range(t_relative - t_prev):\n",
    "        t_values.append(t_relative)\n",
    "        d_values.append(math.log(nok_eur_dict[date]))\n",
    "        print(t_relative, nok_eur_dict[date], math.log(nok_eur_dict[date]), sep = \"\\t\")\n",
    "    t_prev = t_relative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f90554f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit:\n",
      "                             OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.802\n",
      "Model:                            OLS   Adj. R-squared:                  0.801\n",
      "Method:                 Least Squares   F-statistic:                     1465.\n",
      "Date:                Wed, 23 Aug 2023   Prob (F-statistic):          2.65e-129\n",
      "Time:                        22:15:45   Log-Likelihood:                 844.66\n",
      "No. Observations:                 364   AIC:                            -1685.\n",
      "Df Residuals:                     362   BIC:                            -1678.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          2.3107      0.002    925.049      0.000       2.306       2.316\n",
      "x1             0.0005   1.19e-05     38.275      0.000       0.000       0.000\n",
      "==============================================================================\n",
      "Omnibus:                        8.880   Durbin-Watson:                   0.055\n",
      "Prob(Omnibus):                  0.012   Jarque-Bera (JB):                8.260\n",
      "Skew:                          -0.315   Prob(JB):                       0.0161\n",
      "Kurtosis:                       2.617   Cond. No.                         420.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "Fit params: [2.31068621e+00 4.54989460e-04]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "\n",
    "t_dat_stable = np.asarray(t_values)\n",
    "d_dat_stable = np.asarray(d_values)\n",
    "\n",
    "linear_fit_d = sm.OLS(np.asarray(d_dat_stable), sm.add_constant(np.asarray(t_dat_stable))).fit()\n",
    "\n",
    "print(\"Fit:\\n\", linear_fit_d.summary())\n",
    "print(\"\\nFit params:\", linear_fit_d.params, end=\"\\n\")\n",
    "\n",
    "residual = [d_dat_stable[i] - (linear_fit_d.params[0] + linear_fit_d.params[1]*t_dat_stable[i]) for i in range(t_dat_stable.size)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "61ff6ea8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='t, i.e., whole days from a selected starting point', ylabel='d, i.e., ln(EUR:NOK)'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sbn\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(13, 8)\n",
    "plt.xticks(fontsize=18, color=\"#322300\")\n",
    "plt.yticks(fontsize=18, color=\"#322300\")\n",
    "ax.set_xlabel(\"t, i.e., whole days from a selected starting point\", fontsize=24, color=\"#322300\")\n",
    "ax.set_ylabel(\"d, i.e., ln(EUR:NOK)\", fontsize=24, color=\"#322300\")\n",
    "\n",
    "sbn.regplot(x=t_dat_stable, y=d_dat_stable, color='#00a0d2', order=1, \\\n",
    "            scatter_kws={'s':1}, ci=None, truncate=False)\n",
    "\n",
    "# sbn.regplot(x=t_dat_stable, y=residual, color='#147864', order=1, \\\n",
    "#             scatter_kws={'s':1}, ci=None, truncate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a116353f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='t', ylabel='residual of d'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(13, 8)\n",
    "plt.xticks(fontsize=18, color=\"#322300\")\n",
    "plt.yticks(fontsize=18, color=\"#322300\")\n",
    "ax.set_xlabel(\"t\", fontsize=24, color=\"#322300\")\n",
    "ax.set_ylabel(\"residual of d\", fontsize=24, color=\"#322300\")\n",
    "\n",
    "# sbn.regplot(x=t_dat_stable, y=d_dat_stable, color='#00a0d2', order=1, \\\n",
    "#             scatter_kws={'s':1}, ci=None, truncate=False)\n",
    "\n",
    "sbn.regplot(x=t_dat_stable, y=residual, color='#147864', order=1, \\\n",
    "            scatter_kws={'s':1}, ci=None, truncate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7d6ea903",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Delta t', ylabel='norm. autocorr. of the residual'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels\n",
    "import statsmodels.tsa\n",
    "import statsmodels.tsa.stattools\n",
    "\n",
    "aut = statsmodels.tsa.stattools.acf(residual, nlags=50)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(13, 8)\n",
    "plt.xticks(fontsize=18, color=\"#322300\")\n",
    "plt.yticks(fontsize=18, color=\"#322300\")\n",
    "ax.set_xlabel(\"Delta t\", fontsize=24, color=\"#322300\")\n",
    "ax.set_ylabel(\"norm. autocorr. of the residual\", fontsize=24, color=\"#322300\")\n",
    "\n",
    "sbn.scatterplot(x=np.arange(0, aut.size, 1), y=aut, color=\"#322300\", s=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "43a31f9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Delta t', ylabel='ln rho(Delta t) for residual'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "t_log_out = np.arange(50)\n",
    "log_aut = np.asarray([math.log(max(abs(x), 1.0e-04)) for x in aut[:50]])\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(14, 6)\n",
    "plt.xticks(fontsize=18, color=\"#322300\")\n",
    "plt.yticks(fontsize=18, color=\"#322300\")\n",
    "ax.set_xlabel(\"Delta t\", fontsize=24, color=\"#322300\")\n",
    "ax.set_ylabel(\"ln rho(Delta t) for residual\", fontsize=24, color=\"#322300\")\n",
    "\n",
    "sbn.scatterplot(x=t_log_out, y=log_aut, color=\"#322300\", s=10)\n",
    "\n",
    "sbn.regplot(x=t_log_out[:25], y=log_aut[:25], color='#e6503c', order=2, \\\n",
    "            scatter_kws={'s':1}, ci=None, truncate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3ee4fb3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statsmodels regression output:\n",
      "                                  OLS Regression Results                                \n",
      "=======================================================================================\n",
      "Dep. Variable:                      y   R-squared (uncentered):                   1.000\n",
      "Model:                            OLS   Adj. R-squared (uncentered):              1.000\n",
      "Method:                 Least Squares   F-statistic:                          5.657e+04\n",
      "Date:                Wed, 23 Aug 2023   Prob (F-statistic):                    3.49e-43\n",
      "Time:                        22:15:46   Log-Likelihood:                          90.152\n",
      "No. Observations:                  25   AIC:                                     -176.3\n",
      "Df Residuals:                      23   BIC:                                     -173.9\n",
      "Df Model:                           2                                                  \n",
      "Covariance Type:            nonrobust                                                  \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "x1            -0.0284      0.000    -72.538      0.000      -0.029      -0.028\n",
      "x2            -0.0002   2.07e-05    -11.787      0.000      -0.000      -0.000\n",
      "==============================================================================\n",
      "Omnibus:                        4.052   Durbin-Watson:                   0.301\n",
      "Prob(Omnibus):                  0.132   Jarque-Bera (JB):                2.022\n",
      "Skew:                           0.408   Prob(JB):                        0.364\n",
      "Kurtosis:                       1.870   Cond. No.                         76.2\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
      "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "Decorrelation time: 35.17685093706318\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "lin_sqr_array = np.asarray([[t, t*t] for t in t_log_out[:25]])\n",
    "\n",
    "compute_decorrelation_time = sm.OLS(log_aut[:25], lin_sqr_array).fit()\n",
    "\n",
    "print(\"Statsmodels regression output:\\n\", compute_decorrelation_time.summary())\n",
    "\n",
    "print(\"\\nDecorrelation time:\", -1/compute_decorrelation_time.params[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a4b7d7d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating 3 blocks consisting of 106 data points each.\n"
     ]
    }
   ],
   "source": [
    "block_size = max(1, math.ceil(-3/compute_decorrelation_time.params[0]))\n",
    "N_blocks = math.floor(t_dat_stable.size / block_size)\n",
    "\n",
    "print(\"Evaluating\", N_blocks, \"blocks consisting of\", block_size, \"data points each.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9351e702",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "318\n",
      "364\n"
     ]
    }
   ],
   "source": [
    "sum_of_squares = 0.0\n",
    "step_approximation = []\n",
    "\n",
    "for i in range(N_blocks):\n",
    "    this_block_sum = 0.0\n",
    "    for j in range(block_size*i, block_size*(i+1)):\n",
    "        this_block_sum += residual[j]\n",
    "        \n",
    "    this_block_average = this_block_sum / block_size\n",
    "    sum_of_squares += this_block_average*this_block_average\n",
    "    \n",
    "    for k in range(block_size):\n",
    "        step_approximation.append(this_block_average)\n",
    "\n",
    "sigma_block_avg = math.sqrt(sum_of_squares / (N_blocks - 1))\n",
    "print(np.asarray(step_approximation).size)\n",
    "print(t_dat_stable.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2c59ee7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='t', ylabel='residual of d'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(13, 8)\n",
    "plt.xticks(fontsize=18, color=\"#322300\")\n",
    "plt.yticks(fontsize=18, color=\"#322300\")\n",
    "ax.set_xlabel(\"t\", fontsize=24, color=\"#322300\")\n",
    "ax.set_ylabel(\"residual of d\", fontsize=24, color=\"#322300\")\n",
    "\n",
    "step_array = np.asarray(step_approximation)\n",
    "t_array = t_dat_stable[:step_array.size]\n",
    "\n",
    "sbn.regplot(x=t_dat_stable, y=residual, color='#a0a0a0', order=1, \\\n",
    "            scatter_kws={'s':1}, ci=None, truncate=False)\n",
    "\n",
    "sbn.scatterplot(x=t_array, y=step_array, color=\"#e6503c\", s=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3d8c8658",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sigma of individual block-average data points: 0.016436311328486528\n",
      "sigma for whole series: 0.009489508769986194\n",
      "\n",
      "*** 2*sigma uncertainty metric: ±0.018979017539972387 ***\n"
     ]
    }
   ],
   "source": [
    "print(\"sigma of individual block-average data points:\", sigma_block_avg)\n",
    "\n",
    "# apply central limit theorem and divide by square root of number of blocks\n",
    "sigma_whole = sigma_block_avg / math.sqrt(N_blocks)\n",
    "print(\"sigma for whole series:\", sigma_whole)\n",
    "\n",
    "# uncertainty metric based on 2*sigma\n",
    "print(\"\\n*** 2*sigma uncertainty metric: ±\", 2*sigma_whole, \" ***\", sep=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53a2aef2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}