FoodImportsNotebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "86e932a8",
   "metadata": {},
   "source": [
    "# Food Imports Project\n",
    "\n",
    "Suppose we are a company interested in the consumption patterns and business use of different food groups in th United States. Suppose we want to predict the total import value of different food groups for next year.\n",
    "\n",
    "We can use the food imports data provided by the USDA to build a model to make predictions.\n",
    "\n",
    "\n",
    "Note: This is a work in progress."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "39f6db5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c39f9011",
   "metadata": {},
   "source": [
    "We will be working on the USDA Food imports dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5c37ea2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_values = pd.read_csv('MyFoodImports - ValueFoodImports.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a3272ab7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_volumes = pd.read_csv('MyFoodImports - VolumeFoodImports.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ebeaa62c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c945d565",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['FoodImportsNotebook.ipynb',\n",
       " '.DS_Store',\n",
       " 'FoodImports.xlsx',\n",
       " 'MyFoodImports - VolumeFoodImports.csv',\n",
       " 'FoodImportsNotebook-Copy1.ipynb',\n",
       " '.ipynb_checkpoints',\n",
       " 'MyFoodImports - ValueFoodImports.csv']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ef7259d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>group</th>\n",
       "      <th>group_type</th>\n",
       "      <th>value</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>199,835.80</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>Live meat animals</td>\n",
       "      <td>Foods</td>\n",
       "      <td>2,509.60</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>Meats</td>\n",
       "      <td>Foods</td>\n",
       "      <td>14,055.40</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>Fish and shellfish 2/</td>\n",
       "      <td>Foods</td>\n",
       "      <td>30,525.50</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>Foods</td>\n",
       "      <td>2,721.40</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year                  group      group_type       value              unit\n",
       "0  2022           U.S. imports  Total foods 1/  199,835.80  dollars_millions\n",
       "1  2022      Live meat animals           Foods    2,509.60  dollars_millions\n",
       "2  2022                  Meats           Foods   14,055.40  dollars_millions\n",
       "3  2022  Fish and shellfish 2/           Foods   30,525.50  dollars_millions\n",
       "4  2022                  Dairy           Foods    2,721.40  dollars_millions"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_values.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "95caaad8",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = data_values['group'] == 'U.S. imports'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ad945799",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>group</th>\n",
       "      <th>group_type</th>\n",
       "      <th>value</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>199,835.80</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2021</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>174,144.80</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2020</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>147,631.00</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2019</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>142,987.40</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2018</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>140,916.90</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2017</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>132,511.30</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>2016</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>125,437.80</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>2015</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>123,350.70</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>2014</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>121,825.00</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>2013</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>111,777.70</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>2012</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>108,811.00</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>2011</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>104,482.80</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>2010</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>88,457.00</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>2009</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>78,472.30</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>2008</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>85,752.10</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>2007</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>77,937.80</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>2006</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>71,925.60</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>2005</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>65,311.70</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>2004</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>59,268.30</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>2003</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>53,102.20</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>2002</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>47,205.10</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>2001</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>44,396.40</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>2000</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>43,788.60</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>1999</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>41,825.00</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year         group      group_type       value              unit\n",
       "0    2022  U.S. imports  Total foods 1/  199,835.80  dollars_millions\n",
       "17   2021  U.S. imports  Total foods 1/  174,144.80  dollars_millions\n",
       "34   2020  U.S. imports  Total foods 1/  147,631.00  dollars_millions\n",
       "51   2019  U.S. imports  Total foods 1/  142,987.40  dollars_millions\n",
       "68   2018  U.S. imports  Total foods 1/  140,916.90  dollars_millions\n",
       "85   2017  U.S. imports  Total foods 1/  132,511.30  dollars_millions\n",
       "102  2016  U.S. imports  Total foods 1/  125,437.80  dollars_millions\n",
       "119  2015  U.S. imports  Total foods 1/  123,350.70  dollars_millions\n",
       "136  2014  U.S. imports  Total foods 1/  121,825.00  dollars_millions\n",
       "153  2013  U.S. imports  Total foods 1/  111,777.70  dollars_millions\n",
       "170  2012  U.S. imports  Total foods 1/  108,811.00  dollars_millions\n",
       "187  2011  U.S. imports  Total foods 1/  104,482.80  dollars_millions\n",
       "204  2010  U.S. imports  Total foods 1/   88,457.00  dollars_millions\n",
       "221  2009  U.S. imports  Total foods 1/   78,472.30  dollars_millions\n",
       "238  2008  U.S. imports  Total foods 1/   85,752.10  dollars_millions\n",
       "255  2007  U.S. imports  Total foods 1/   77,937.80  dollars_millions\n",
       "272  2006  U.S. imports  Total foods 1/   71,925.60  dollars_millions\n",
       "289  2005  U.S. imports  Total foods 1/   65,311.70  dollars_millions\n",
       "306  2004  U.S. imports  Total foods 1/   59,268.30  dollars_millions\n",
       "323  2003  U.S. imports  Total foods 1/   53,102.20  dollars_millions\n",
       "340  2002  U.S. imports  Total foods 1/   47,205.10  dollars_millions\n",
       "357  2001  U.S. imports  Total foods 1/   44,396.40  dollars_millions\n",
       "374  2000  U.S. imports  Total foods 1/   43,788.60  dollars_millions\n",
       "391  1999  U.S. imports  Total foods 1/   41,825.00  dollars_millions"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_values[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b983587",
   "metadata": {},
   "source": [
    "### What does this dataset represent?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f13aab15",
   "metadata": {},
   "source": [
    "The prices are not adjusted for inflation.\n",
    "\n",
    "Volume units given, for live animals, fowl are excluded."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67d5aa21",
   "metadata": {},
   "source": [
    "### Data Source Flow Chart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21927013",
   "metadata": {},
   "source": [
    "Our dataset comes from the USDA. Each category of food is found in the USDA food availability data set. The USDA took the imprt data from each country and aggregated it into total import value for each food group. our dataset also has subtotals, aggregated by the USDA, by food type."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f65e0416",
   "metadata": {},
   "source": [
    "https://www.census.gov/foreign-trade/reference/guides/tradestatsinfo.html\n",
    "\n",
    "https://www.ers.usda.gov/data-products/u-s-food-imports/documentation/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "23ed56be",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_totals = data_values[mask].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "84ff5bb9",
   "metadata": {},
   "outputs": [
    {
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       "      <th>group</th>\n",
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       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>199,835.80</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2021</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>174,144.80</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2020</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>147,631.00</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2019</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>142,987.40</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2018</td>\n",
       "      <td>U.S. imports</td>\n",
       "      <td>Total foods 1/</td>\n",
       "      <td>140,916.90</td>\n",
       "      <td>dollars_millions</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year         group      group_type       value              unit\n",
       "0   2022  U.S. imports  Total foods 1/  199,835.80  dollars_millions\n",
       "17  2021  U.S. imports  Total foods 1/  174,144.80  dollars_millions\n",
       "34  2020  U.S. imports  Total foods 1/  147,631.00  dollars_millions\n",
       "51  2019  U.S. imports  Total foods 1/  142,987.40  dollars_millions\n",
       "68  2018  U.S. imports  Total foods 1/  140,916.90  dollars_millions"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_totals.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "30624230",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_totals = data_totals[['year','value']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4086b606",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_totals['year'] = pd.to_datetime(data_totals['year'],format = '%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2a73f0bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_totals.index = pd.DatetimeIndex(data_totals['year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6e4aab2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_totals.drop(['year'],inplace = True,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e2d18ce5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_totals['value'] = data_totals['value'].str.replace(',','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7eb7e3e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_totals['value'] = data_totals['value'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "60eb34c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 24 entries, 2022-01-01 to 1999-01-01\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   value   24 non-null     float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 384.0 bytes\n"
     ]
    }
   ],
   "source": [
    "data_totals.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26b42faa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c5b95f15",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.tick_params(axis='x', labelrotation=45)\n",
    "\n",
    "ax.plot(data_totals.index,data_totals['value'],label = 'U.S. Food Imports Total Value')\n",
    "ax.scatter(data_totals.index,data_totals['value'])\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "3bb15b14",
   "metadata": {},
   "source": [
    "we will fit three models. Linear, quadratic, and exponential. We'll see which one fits the best. We will also try using double exponetial smoothing to make predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2689914a",
   "metadata": {},
   "source": [
    "We will first implement single exponential smoothing and double exponential smoothing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "3cf10346",
   "metadata": {},
   "outputs": [],
   "source": [
    "def MySingleExpSmoother(X,alpha):\n",
    "\n",
    "    S = []\n",
    "    \n",
    "    S.append(X[0])\n",
    "    \n",
    "    for i in range(1,len(X)):\n",
    "        \n",
    "        S.append( X[i]*alpha + (1-alpha)*S[i-1]) #S_i = X_i*alpha + (1-alpha)*S_(i-1),\n",
    "                                                 # i runs through [1..len(A)-1].\n",
    "            \n",
    "    return S #Returns the smoothed out data.\n",
    "        \n",
    "        \n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef9101ec",
   "metadata": {},
   "source": [
    "$y_{t|t-1} = l_{t-1}$, where $l_{t-1}$ is our smoothed out data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ca01d435",
   "metadata": {},
   "outputs": [],
   "source": [
    "def BackPredict(X,S):\n",
    "    predictions = []\n",
    "    predictions.append(np.nan)\n",
    "    for i in range(1,len(X)):\n",
    "        predictions.append(S[i-1])  #S[i-1] is l_(i-1)\n",
    "    \n",
    "    return predictions\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c82bb68e",
   "metadata": {},
   "outputs": [],
   "source": [
    "S = MySingleExpSmoother([1,2,2,3],0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d19540e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[nan, 1, 1.8, 1.96]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BackPredict(X,S)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b57d8f7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def NextPredict(X,S):\n",
    "    return S[len(S) - 1]\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "008fa1f3",
   "metadata": {},
   "source": [
    "We want to write a class that bundles all of the functions, and a mse function and a mae function.\n",
    "\n",
    "and compares against baseline. and makes graphs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5537d60",
   "metadata": {},
   "source": [
    "Exponetial smoother app."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a800d43c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data_totals['value'].sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "9e833818",
   "metadata": {},
   "outputs": [],
   "source": [
    "S = MySingleExpSmoother(X,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "9f1614c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = BackPredict(X,S)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "09ed1e45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2022"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.index.max().year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "98768dfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "T = pd.date_range(X.index.min(), freq=\"Y\", periods= X.index.max().year - X.index.min().year + 2).year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "9e4aa87f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1999"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "6a6d8a80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.tick_params(axis='x', labelrotation=45)\n",
    "\n",
    "ax.plot(X.index.year,X.values,label = 'U.S. Food Imports Total Value')\n",
    "# ax.scatter(data_totals.index,data_totals['value'])\n",
    "\n",
    "\n",
    "# ax.scatter(X.index.year,S)\n",
    "\n",
    "ax.scatter(T.values[1:],Y,color = 'r')\n",
    "\n",
    "ax.vlines(2023,Y[-1]-10000,Y[-1]+10000)\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb5f876f",
   "metadata": {},
   "source": [
    "Write optimizer."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f5cf7dd",
   "metadata": {},
   "source": [
    "Now we want to create an optimizer which finds the alpha value that minimizes the MSE between $\\hat{y}_{t|t-1}$ and\n",
    "$y_t$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "721da1e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data_totals['value'].sort_index()\n",
    "S = MySingleExpSmoother(X,1)\n",
    "Y = BackPredict(X,S)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "11707caf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "0.9\n"
     ]
    }
   ],
   "source": [
    "for i in np.arange(0,1,0.9):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "dbbd80fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = list(enumerate([10,20,30],start = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "ee7edb7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def MyOptimizer(data,gran):\n",
    "    scores = []\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    for alpha in np.arange(0,1,gran):\n",
    "        S = MySingleExpSmoother(data,alpha)\n",
    "    \n",
    "        score = (1/len(data[1:]))* ((data[1:] - BackPredict(data,S)[1:])@(data[1:] - BackPredict(data,S)[1:]))**2\n",
    "        \n",
    "        scores.append(score)\n",
    "        \n",
    "    \n",
    "    \n",
    "    m = min(scores)\n",
    "    \n",
    "    A = [index for index in range(len(scores)) if scores[index] == m   ]\n",
    "    \n",
    "    return A[0]*gran\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "da800259",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MyOptimizer(X,0.01) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "e63f6351",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = [10,2,10,3]\n",
    "M = max(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "c7914613",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 2]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[index for index in range(0,len(A)) if A[index] == M   ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0034d085",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "1b485730",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = np.array([np.sin(i) for i in np.arange(0,10,0.1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "449124c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fdbd0984850>"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(np.arange(len(d)),d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "0549bbeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fdb8005f580>]"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(len(d)),BackPredict(d,MySingleExpSmoother(d,0.34400)),color = 'r')\n",
    "plt.plot(np.arange(len(d)),d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "5be0aafc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.34400000000000003"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MyOptimizer(d,0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "c177775a",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = 0.1*np.arange(100) + np.random.normal(0,1,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b34dad76",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9db9fe64",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbda0010",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "8a72475c",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'D' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/92/ykbkbbfn2sj_bjgfj9s65x780000gn/T/ipykernel_32735/1957010296.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_totals\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mD\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'ewma 0.9'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_totals\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mD\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'D' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.tick_params(axis='x', labelrotation=45)\n",
    "\n",
    "ax.plot(data_totals.index,data_totals['value'],label = 'U.S. Food Imports Total Value')\n",
    "ax.scatter(data_totals.index,data_totals['value'])\n",
    "\n",
    "\n",
    "ax.plot(data_totals.index,D,label = 'ewma 0.9')\n",
    "ax.scatter(data_totals.index,D)\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b81ca215",
   "metadata": {},
   "outputs": [],
   "source": [
    "D = data_totals['value'].ewm(alpha = 0.9,adjust = False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0813143c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data_totals.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b55659b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84bb436d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_ewma(S,alpha):\n",
    "    A = []\n",
    "    \n",
    "    \n",
    "    \n",
    "    A.append(S[0])\n",
    "    \n",
    "    i = 1\n",
    "    while i < len(S):\n",
    "        \n",
    "        A.append(S[i]*alpha + A[i-1]*(1-alpha)  )\n",
    "        \n",
    "        i += 1\n",
    "        \n",
    "        \n",
    "    return A\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7152dd09",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_ewma([2,3,4,5],0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd11ca2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_mse_ewma(S,A):\n",
    "    A = np.array(A)\n",
    "    S = np.array(S)\n",
    "    \n",
    "    RA = np.roll(A,1)\n",
    "    \n",
    "    R = RA - S\n",
    "    \n",
    "    R = R[1:]\n",
    "    \n",
    "    SSE = R@R.T\n",
    "    \n",
    "    \n",
    "    return SSE\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3b6d02a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_best_alpha(S,gran):\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    space = np.arange(0,1,gran)\n",
    "\n",
    "    scores = []\n",
    "    \n",
    "    \n",
    "    for alpha in space:\n",
    "        A = my_ewma(list(S),alpha)\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        SSE = my_mse_ewma(list(S),A)\n",
    "        \n",
    "        scores.append(SSE)\n",
    "        \n",
    "        \n",
    "    \n",
    "    \n",
    "    m = scores.index(min(scores))\n",
    "    \n",
    "    print(len(space))\n",
    "    print(len(scores))\n",
    "    \n",
    "    \n",
    "    plt.scatter(space,scores)\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "    return space[m]\n",
    "        \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db7fdc21",
   "metadata": {},
   "outputs": [],
   "source": [
    "find_best_alpha([1,3,2,6,4,7,4],0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4a33df0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9c6da81",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.values.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9392371f",
   "metadata": {},
   "outputs": [],
   "source": [
    "find_best_alpha(df.values.flatten(),0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06ae733f",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(df.index,my_ewma(df.values.flatten(),0.9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d394663",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(np.arange(len(d)),u,color = 'r')\n",
    "\n",
    "\n",
    "plt.scatter(np.arange(len(d)),d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dffabba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "u = np.random.uniform(10,30,100)\n",
    "\n",
    "d = my_ewma(u,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afd730d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "find_best_alpha(u,0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "654c45be",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  }
 ],
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