labs/Module-3/6.Time_series.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "HPgCsOvYEkGZ"
   },
   "outputs": [],
   "source": [
    "# Begin by generating a time series\n",
    "from random import random\n",
    "time_series = [2*x+random() for x in range(1,100)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "198.2870997165355\n"
     ]
    }
   ],
   "source": [
    "print(time_series[98])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[178.7909818421423, 180.89433539778125, 182.37137770579312, 184.04647458530584, 186.58271389967365, 188.23490268057853, 190.194580200028, 192.3947869317429, 194.96947642465653, 196.70073194342214, 198.2870997165355]\n"
     ]
    }
   ],
   "source": [
    "print(time_series[88:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "mmv_8GYbEzvR",
    "outputId": "9f500521-2cdc-41d6-823f-c91f28a0663b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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e3/9fK/7crwNVkipGuyyJEaW5FHIkcArQ0MzWAQ8SuDqmCjApeATx4yWPJwGPmFkBUAzc6O65EapdJC7t2lfA0C+X8eb3q2hQQ8PyytEpzdUyAw/S/PohXjsaGB1qUSKJyN0ZN3cDj36yiJzd+Vx+bAvu7teRejU0LK8cOZ1mFykHMjfs5KHxC5mxMpduKXUYdnU6PVrUjXZZEsMU7iJRtHNfAU9/voR3pq2mbvXKPH5RVy5Nb0FFDR0gIVK4i0TJl5mbuf/j+eTsyufq49O444z21Kmu0RslPBTuImVse95+Hhi7kHFzN9CxaS1evTqdbil1o12WxBmFu0gZmrk6l1tHzCZndz53nNGem05pQ+UkTaAh4adwFykDxcXOsG+yeOrzJSTXrcbom36lo3WJKIW7SIRt3rmPP38wl2+WbeGcrk154uJu1K6qvnWJLIW7SAR9tmAj94yZT35BMY8N6MrAPi00dICUCYW7SATs2lfAI+Mz+WDmusBAX5dpEg0pWwp3kTCbnrWVOz+Yy4bte7n51DbcfkZ7KlXUSVMpWwp3kTDZsbeA5yYt5a3/riK1fnU+uPF4erfUsLwSHQp3kRAVFzujZ63jyc8Ws3XPfq7om8q9Zx9DDU2iIVGk3z6REGTv2sdtI+fw36yt9Eyty5vX9qFrSp1olyWicBc5WtOztnLryNns3FfAYwO6cvmxLaigMWGknFC4ixyhrJzdfDBzHcOmZpFavzpvXd+HY5rVjnZZIj+hcBcphaJi551pqxn1w1oyN+7EDM7r1pzHBnShlm5IknKoVOFuZm8QmC812927BNvqA6OANGAVcKm7b7PAHRpDgXOAPOBad58V/tJFysa6bXn86f25zFiZS/eUOvztvE6c27UZTetUjXZpIodU2iP34cCLwNsl2u4BJrv7E2Z2T3D5L8DZBOZObQf0BV4OPorEFHdn7JwN/G3sAtzhmd9056JeybrDVGJCqcLd3aeaWdoBzf0JzK0K8BbwFYFw7w+87e4OTDOzumbWzN03hqVikTKwPHs3D41byLfLt9C7ZT2ev6wHLepXj3ZZIqUWSp97kxKBvQloEnyeDKwt8bp1wbafhLuZDQIGAaSmpoZQhkj4FBQV89ykpbz6TRZVK1Xk4Qs6c0XfVJJ0h6nEmLCcUHV3NzM/wvcMA4YBpKenH9F7RSIhv7CIW0bMZlLmZi7pncI9Z3ekYc0q0S5L5KiEEu6bf+xuMbNmQHawfT3QosTrUoJtIuXW3v1FDPp3Bt8s28LDF3Tmml+lRbskkZCE8rfmOOCa4PNrgLEl2q+2gOOAHepvl/IsZ1c+17wxg++Wb+Efl3RTsEtcKO2lkCMJnDxtaGbrgAeBJ4D3zewGYDVwafDlEwlcBrmcwKWQ14W5ZpGwcHfez1jLo58sYl9BMUMv78n53ZtHuyyRsCjt1TIDD7Hq9IO81oGbQylKJJLcnf9mbWXol8uYvjKXPq3q8/hFXWmj8dYljugOVUkYe/ILGTljDSNmrCErZw91q1fiyYu78pveGhNG4o/CXRLCzNW53DFqLmty8+iVWpdnftOdc7s1o2qlitEuTSQiFO4S1wqKihn65TL++dVymtetxqhBx9G3dYNolyUScQp3iVszVubyt48XsGTzLi7pncKD53fSIF+SMBTuEne27s7nsYmLGT1rHcl1q/Hq1emc2anJ4d8oEkcU7hJXlm7exbVvzCBndz5/OKUNt5zWluqV9WsuiUe/9RI3vl++hcHvzKRapYqMuekETXcnCU3hLjGvuNgZMWMND49fSKuGNXjzuj4k160W7bJEokrhLjHth1W5PDI+k/nrd3Bi24a8dEUv6lTTSVMRhbvEpDlrt/PKVyv4bOEmmtauyvOX9eCC7s11M5JIkMJdYsqUJdm8PGUFM1blUqtqEred3o7BJ7fWSVORA+i/CIkJu/MLeWjcQj6cGbi88W/ndeKyY1tQs4p+hUUORv9lSLk3a802bn9vDuu25fHH09py6+ntqKSZkUR+kcJdyi1359VvsnjysyU0rV2VUYOP59i0+tEuSyQmKNylXNqRV8CdH8zly0WbObtLU568pBu1NXSASKkp3KVccXemLMnmgbEL2bRjHw+c14nrTkjDTFfBiByJow53M+sAjCrR1Bp4AKgL/B7ICbbf5+4Tj/Z7JHEs3byLIRMy+WbZFlo3rMGowcfTu2W9aJclEpOOOtzdfQnQA8DMKhKYBPsjAtPqPefuT4ejQIl/7s4b363isYmLqFG5Ig+c14krj2tJ5SSdNBU5WuHqljkdWOHuq/XnsxyJgqJiHhq3kHenr6Ff5yY8cVE36tWoHO2yRGJeuA6NLgdGlli+xczmmdkbZnbQv6vNbJCZZZhZRk5OzsFeInFu3bY8rh/+A+9OX8ONJ7fh5St6K9hFwsQC81mH8AFmlYENQGd332xmTYAtgANDgGbufv0vfUZ6erpnZGSEVIfEhn0FRYyfu4HRs9YxLSuXpArGowO6cNmxqdEuTSTmmNlMd08/2LpwdMucDcxy980APz4Gv/hVYEIYvkPiwJy12/nT+3PIytlDWoPq/OnM9gzomUyL+tWjXZpI3AlHuA+kRJeMmTVz943BxQHAgjB8h8SwgqJi/t9/lvPSlOU0rlWF4dcdy8ntG+nyRpEICinczawGcCYwuETzP8ysB4FumVUHrJMEszY3j1tGzmbu2u1c1CuZB8/vrCF5RcpASOHu7nuABge0XRVSRRI3Pp2/kbtHzwPgpd/24txuzaJckUji0B2qEnY79xXw+MTFjJyxhu4t6vLiwJ7qVxcpYwp3CavPFmzkgbEL2bI7n8Ent+bOMzvoZiSRKFC4S1js3FfAvaPn88n8jXRqVpvXrkmnW0rdaJclkrAU7hKyJZt2ceM7M1mTm8dd/Tow6KTWGm9dJMoU7hKSj2ev594x86lZNYmRvz+OPq003rpIeaBwl6OyPW8/fxu7kPFzN9AnrT4v/rYnjWtXjXZZIhKkcJcj9vXSHO7+cC5bd+/nzjPbc9MpbUhSN4xIuaJwl1LLytnN458uZlLmZto2rslrVx9L15Q60S5LRA5C4S6HtWV3Pi/+ZznvTFtNlaQK3NWvAzec2IqqlSpGuzQROQSFuxzSjr0FvDo1ize+W8m+giIu75PKHWe0p1GtKtEuTUQOQ+EuB/XFwk3c9eE8duwt4LxuzbjjzPa0aVQz2mWJSCkp3OUnioudoZOXMXTyMrql1OHxi7rSubn61UVijcJd/mfD9r08NG4hX2Ru5uJeKTw6oIv61UVilMI9wRUWFTMpczOjMtby9dIcKpjx4PmduPZXaRpvXSSGKdwT2Mote7h91Bzmrt1O09pVueXUtlya3kIjOIrEAYV7AnJ3RsxYw98nLKJyUgWev6wH53dvTsUKOlIXiRchh7uZrQJ2AUVAobunm1l9YBSQRmA2pkvdfVuo3yWh251fyJ/fn8tnCzfxf+0a8tQl3WlaR8MGiMSbcN0zfqq79ygxC/c9wGR3bwdMDi5LlGXl7ObCl75j0qLN3H/OMbx1XR8Fu0icilS3TH/glODzt4CvgL9E6LvkMAqLihk/bwMPjF1IUgXj39f34VdtG0a7LBGJoHCEuwNfmJkD/3L3YUATd98YXL8JaBKG75EjtK+giA9mruPVqVmsyc2ja3IdXr6yFyn1dMJUJN6FI9xPdPf1ZtYYmGRmi0uudHcPBv9PmNkgYBBAampqGMqQkqYuzeHeMfNZv30vPVPrcv+5x3DmMU2ooJOmIgkh5HB39/XBx2wz+wjoA2w2s2buvtHMmgHZB3nfMGAYQHp6+s/CX47OjrwChnySyYcz19GmUQ1G/K4vx7dpoGvWRRJMSOFuZjWACu6+K/j818AjwDjgGuCJ4OPYUAuVw1uwfge/fzuD7F353HxqG249rZ3uMBVJUKEeuTcBPgoeFSYBI9z9MzP7AXjfzG4AVgOXhvg9chjj527grg/n0qBGFT76w680ObVIggsp3N09C+h+kPatwOmhfLaUzr6CIp7/chmvfL2CY9Pq8fKVvWlYU0PyiiQ63aEao9ydTxds4rGJi1i3bS8D+7Tg4Qu6UDlJ092JiMI9Ji1Yv4MhEzKZvjKXjk1rMeJ3fXXduoj8hMI9hmTv2sczny/l/ZlrqVe9Mn+/sAsD+6RqTBgR+RmFewxwd0bPWs9D4xaSX1jE705sxS2ntaNOtUrRLk1EyimFezm3c18B93+0gPFzN9C3VX2euLgbrRrWiHZZIlLOKdzLsa+X5nD/R/PZuGMff/51e246pa26YESkVBTu5dDa3DyGTMjki8zNpDWozvuDj6d3y3rRLktEYojCvZx5Z9pqhkzIpIIZd5/VgRtObEWVJN1lKiJHRuFeThQUFfPQuIW8O30NJ7dvxBMXd6VZnWrRLktEYpTCvRzYujufm0fMYlpWLoNPbs3d/Tqqb11EQqJwjyJ35+M563lkfCZ79hfx3GXdGdAzJdpliUgcULhHydrcPO7/eAFTl+bQM7UuT17cjfZNakW7LBGJEwr3MlZQVMxr36xk6OSlVDTj4Qs6c+VxLdUNIyJhpXAvQ7PWbOO+MfNZvGkXv+7UhIcu6EzzujppKiLhp3AvA/sLi3lh8jL++dVymtSuyr+u6k2/zk2jXZaIxDGFe4Qt27yLO96fw4L1O7mkdwoPnt+JWlU1JoyIRJbCPULyC4t45assXpqynJpVk3jlyt6c1UVH6yJSNo463M2sBfA2gan2HBjm7kPN7CHg90BO8KX3ufvEUAuNJRmrcrlnzHyWZ+/m/O7NeeC8TjSqpdmRRKTshHLkXgjc6e6zzKwWMNPMJgXXPefuT4deXmwpLCpm6ORlvDhlOc3rVOPN647l1A6No12WiCSgow53d98IbAw+32Vmi4DkcBUWa9Zty+O29+Ywc/U2ftM7hYcu6EyNKur1EpHoCEv6mFka0BOYDpwA3GJmVwMZBI7utx3kPYOAQQCpqanhKCMq8vYX8vo3K/nX1CwAhl7eg/49Evb/cSJSTpi7h/YBZjWBr4FH3X2MmTUBthDohx8CNHP363/pM9LT0z0jIyOkOspaYVEx7/2wlqGTl5GzK58zOzXhb+d2IrVB9WiXJiIJwsxmunv6wdaFdORuZpWA0cC77j4GwN03l1j/KjAhlO8oj6ZnbeXBcQtZvGkXfdLq88qVvejdsn60yxIR+Z9QrpYx4HVgkbs/W6K9WbA/HmAAsCC0EsuPLbvzeWR8JuPmbiC5bjVeubIX/To3JfBPISJSfoRy5H4CcBUw38zmBNvuAwaaWQ8C3TKrgMEhfEe5MSlzM/eMnseu/EJuO70dN57chmqVNYmGiJRPoVwt8y1wsEPWuLqmPXfPfp78dDGjMtbSqVltRl7eQ6M3iki5p2v1DmHdtjxe+2Ylo35YS35hEX84pQ23n9GeykkVol2aiMhhKdwP4tWpWTzx2WIM6N8jmRtPbk07Ha2LSAxRuJfg7rwweTnPfbmUfp2b8MD5nUnWkLwiEoMU7kHuzlOfL+GfX63gol7JPHVJd02gISIxS+FO4BLHIRMyGTtnAwP7pPLohV2ooGAXkRiW0OFeXOyMmLGGf3y2mL0FRdxxRnv+eHpbXbcuIjEvYcM9K2c3d304j5mrt3F86wYMubALbRvXjHZZIiJhkXDhXlTsvPndSp76fAlVK1Xk2Uu7M6Bnso7WRSSuJFS4L9q4k/s/ms+sNds545jGPDagK41rV412WSIiYZcQ4b47v5DnJy3lze9XUadaJR2ti0jci/twn7Ikm/vHzGfDjn0M7NOCu/t1pF6NytEuS0QkouI23Lfn7eeR8ZmMmb2eto1rMvqm4zUsr4gkjLgLd3dn7JwN/P2TTLblFXDraW255bS2VEnSCI4ikjjiKtyXZ+/mgbEL+H7FVrqn1GH4dX3oklwn2mWJiJS5uAh3d2f496t4fOJiqlSqwJALu/DbPqkaPkBEElbMh/vW3fnc9eE8/rM4m9M7Nubxi7vSuJYubxSRxBaxcDezs4ChQEXgNXd/ItzfMX/dDq5/6wd27C3g4Qs6c/XxLXV5o4gIEQp3M6sIvAScCawDfjCzce6eGc7vSalXjY5Na3Hv2cfQqXntcH60iEhMi9SRex9gubtnAZjZe0B/IKzhXq9GZf59Q99wfqSISFyI1JxxycDaEsvrgm3/Y2aDzCzDzDJycnIiVIaISGKK2oSg7j7M3dPdPb1Ro0bRKkNEJC5FKtzXAy1KLKcE20REpAxEKtx/ANqZWSszqwxcDoyL0HeJiMgBInJC1d0LzewW4HMCl0K+4e4LI/FdIiLycxG7zt3dJwITI/X5IiJyaFE7oSoiIpGjcBcRiUPm7tGuATPLAVaH8BENgS1hKieWaLsTi7Y7sZRmu1u6+0GvJS8X4R4qM8tw9/Ro11HWtN2JRdudWELdbnXLiIjEIYW7iEgcipdwHxbtAqJE251YtN2JJaTtjos+dxER+al4OXIXEZESFO4iInEopsPdzM4ysyVmttzM7ol2PZFiZi3MbIqZZZrZQjO7Ldhe38wmmdmy4GO9aNcaCWZW0cxmm9mE4HIrM5se3O+jgoPTxRUzq2tmH5rZYjNbZGbHJ8L+NrM7gr/jC8xspJlVjdf9bWZvmFm2mS0o0XbQfWwBLwT/DeaZWa/DfX7MhnuJqfzOBjoBA82sU3SriphC4E537wQcB9wc3NZ7gMnu3g6YHFyOR7cBi0osPwk85+5tgW3ADVGpKrKGAp+5e0egO4Htj+v9bWbJwB+BdHfvQmDQwcuJ3/09HDjrgLZD7eOzgXbBn0HAy4f78JgNd0pM5efu+4Efp/KLO+6+0d1nBZ/vIvAfejKB7X0r+LK3gAujUmAEmVkKcC7wWnDZgNOAD4MvibvtNrM6wEnA6wDuvt/dt5MA+5vAYIbVzCwJqA5sJE73t7tPBXIPaD7UPu4PvO0B04C6Ztbslz4/lsP9sFP5xSMzSwN6AtOBJu6+MbhqE9AkWnVF0PPA3UBxcLkBsN3dC4PL8bjfWwE5wJvB7qjXzKwGcb6/3X098DSwhkCo7wBmEv/7u6RD7eMjzrtYDveEY2Y1gdHA7e6+s+Q6D1zTGlfXtZrZeUC2u8+Mdi1lLAnoBbzs7j2BPRzQBROn+7segSPUVkBzoAY/77ZIGKHu41gO94Says/MKhEI9nfdfUywefOPf5oFH7OjVV+EnABcYGarCHS7nUagL7pu8M92iM/9vg5Y5+7Tg8sfEgj7eN/fZwAr3T3H3QuAMQR+B+J9f5d0qH18xHkXy+GeMFP5BfuZXwcWufuzJVaNA64JPr8GGFvWtUWSu9/r7inunkZg//7H3a8ApgCXBF8Wj9u9CVhrZh2CTacDmcT5/ibQHXOcmVUP/s7/uN1xvb8PcKh9PA64OnjVzHHAjhLdNwfn7jH7A5wDLAVWAPdHu54IbueJBP48mwfMCf6cQ6D/eTKwDPgSqB/tWiP4b3AKMCH4vDUwA1gOfABUiXZ9EdjeHkBGcJ9/DNRLhP0NPAwsBhYA/waqxOv+BkYSOLdQQOCvtRsOtY8BI3B14ApgPoErin7x8zX8gIhIHIrlbhkRETkEhbuISBxSuIuIxCGFu4hIHFK4i4jEIYW7iEgcUriLiMSh/w+Wfu7cujjWmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot our data\n",
    "#%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(time_series)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "G6rOqRCiFAD-",
    "outputId": "c7aa3623-f6a3-4dc0-8449-03e354afcb7d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[200.26729809 202.24704364 204.22633647 206.20517669 208.1835644\n",
      " 210.1614997  212.1389827  214.1160135  216.09259221 218.06871892\n",
      " 220.04439375]\n"
     ]
    }
   ],
   "source": [
    "# Autoregression (AR):\n",
    "\n",
    "from statsmodels.tsa.ar_model import AutoReg\n",
    "\n",
    "model = AutoReg(time_series, lags=1)\n",
    "model_fit = model.fit()\n",
    "y= model_fit.predict(len(time_series), len(time_series)+10)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "Time series analysis.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}