tutorials/Quickstart.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "66b7a99b",
   "metadata": {},
   "source": [
    "# Quickstart Tutorial\n",
    "\n",
    "## Installation\n",
    "\n",
    "Imagine your business currently operates in the US, but is launching its product in Europe. As a result it anticipates a sharp uptake in sales (which it has an estimate of). As your forecasting team, they come to you and ask you to account for this.\n",
    "\n",
    "Let's look at how we might do this using `judgyprophet` with some example data, where we know what happened. First let's plot this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "56579799",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from judgyprophet.tutorials.resources import get_trend_event\n",
    "\n",
    "example_data = get_trend_event()\n",
    "p = example_data.plot.line()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7568fae",
   "metadata": {},
   "source": [
    "We can see that product sales increased sharply from about September 2020. Suppose it was a launch in a new market, and that the business had an initial estimate of the impact in May 2020. The business expected the slope increase to be 6.\n",
    "\n",
    "Let's use `judgyprophet` to forecast this series from May 2020. We do this by encoding the initial business estimate as a _trend event_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aa42b565",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:judgyprophet.judgyprophet:Rescaling onto 0-mean, 1-sd.\n",
      "WARNING:judgyprophet.judgyprophet:Post-event data for trend event New market entry less than 0 points. Event deactivated in model. Event index: 2020-09-01, training data end index: 2019-06-01 00:00:00\n",
      "WARNING:judgyprophet.utils:No active trend or level events (i.e. no event indexes overlap with data). The model will just fit a base trend to the data.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial log joint probability = -3.07317\n",
      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
      "       7      -2.92768   9.35253e-05   0.000181658           1           1        8   \n",
      "Optimization terminated normally: \n",
      "  Convergence detected: relative gradient magnitude is below tolerance\n"
     ]
    }
   ],
   "source": [
    "from judgyprophet import JudgyProphet\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "# Create the expected trend events by consulting with the business\n",
    "trend_events = [\n",
    "    {'name': \"New market entry\", 'index': '2020-09-01', 'm0': 6}\n",
    "]\n",
    "\n",
    "\n",
    "# Cutoff the data to May 2020\n",
    "data_may2020 = example_data.loc[:\"2020-05-01\"]\n",
    "\n",
    "# Make the forecast with the business estimated level event\n",
    "# We have no level events, so just provide the empty list.\n",
    "jp = JudgyProphet()\n",
    "# Because the event is beyond the actuals, judgyprophet throws a warning.\n",
    "#    This is just because the Bayesian model at the event has no actuals to learn from.\n",
    "#    The event is still used in predictions.\n",
    "jp.fit(\n",
    "    data=data_may2020,\n",
    "    level_events=[],\n",
    "    trend_events=trend_events,\n",
    "    # Set random seed for reproducibility\n",
    "    seed=13\n",
    ")\n",
    "predictions = jp.predict(horizon=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88a1dac0",
   "metadata": {},
   "source": [
    "Because we are in May 2020, the forecasting algorithm has nothing to use for learning; so just uses the business estimate. Let's plot the result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "00354632",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
      "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
      "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Initial log joint probability = -17.0121\n",
      "Iteration  1. Log joint probability =    10.4753. Improved by 27.4875.\n",
      "Iteration  2. Log joint probability =    12.7533. Improved by 2.27796.\n",
      "Iteration  3. Log joint probability =    25.4696. Improved by 12.7163.\n",
      "Iteration  4. Log joint probability =     26.707. Improved by 1.2374.\n",
      "Iteration  5. Log joint probability =    26.7075. Improved by 0.000514342.\n",
      "Iteration  6. Log joint probability =    26.7104. Improved by 0.00296558.\n",
      "Iteration  7. Log joint probability =    26.7122. Improved by 0.00171322.\n",
      "Iteration  8. Log joint probability =    26.7157. Improved by 0.00351772.\n",
      "Iteration  9. Log joint probability =    26.7159. Improved by 0.000208268.\n",
      "Iteration 10. Log joint probability =    26.7159. Improved by 6.64977e-05.\n",
      "Iteration 11. Log joint probability =     26.716. Improved by 6.89899e-05.\n",
      "Iteration 12. Log joint probability =     26.716. Improved by 3.06578e-05.\n",
      "Iteration 13. Log joint probability =     26.716. Improved by 8.91492e-07.\n",
      "Iteration 14. Log joint probability =     26.716. Improved by 8.71052e-09.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from judgyprophet.tutorials.resources import plot_forecast\n",
    "\n",
    "plot_forecast(\n",
    "    actuals=example_data,\n",
    "    predictions=predictions,\n",
    "    cutoff=\"2020-05-01\",\n",
    "    events=trend_events\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d953d51",
   "metadata": {},
   "source": [
    "We can see `judgyprophet` is accounting for the increased trend, but the business slightly overestimated the increase in sales due to the product launch.\n",
    "\n",
    "Let's fast forward to January 2021, the business want to reforecast based on their estimate, and what they've seen so far for the product launch. This is where `judgyprophet` comes into its own. \n",
    "\n",
    "Once actuals are observed after the event has taken place, `judgyprophet` updates its estimate of what the event impact is. Let's look at this in action:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "49688ce6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:judgyprophet.judgyprophet:Rescaling onto 0-mean, 1-sd.\n",
      "INFO:judgyprophet.judgyprophet:Adding trend event New market entry to model. Event index: 2020-09-01, training data start index: 2019-06-01 00:00:00, training data end index: 2021-01-01 00:00:00. Initial gradient: 6. Damping: None.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial log joint probability = -96.2602\n",
      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
      "       7      -1.64341   2.40778e-05   0.000287473           1           1       10   \n",
      "Optimization terminated normally: \n",
      "  Convergence detected: relative gradient magnitude is below tolerance\n"
     ]
    }
   ],
   "source": [
    "# Cutoff the data to January 2021\n",
    "data_jan2021 = example_data.loc[:\"2021-01-01\"]\n",
    "\n",
    "# Reforecast using the new actuals, not we are at Jan 2021\n",
    "jp = JudgyProphet()\n",
    "jp.fit(\n",
    "    data=data_jan2021,\n",
    "    level_events=[],\n",
    "    trend_events=trend_events,\n",
    "    # Set random seed for reproducibility\n",
    "    seed=13\n",
    ")\n",
    "predictions = jp.predict(horizon=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6d88b2c",
   "metadata": {},
   "source": [
    "Now let's plot the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "479de254",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
      "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
      "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Initial log joint probability = -24.5881\n",
      "Iteration  1. Log joint probability =   -1.06803. Improved by 23.5201.\n",
      "Iteration  2. Log joint probability =    11.6215. Improved by 12.6895.\n",
      "Iteration  3. Log joint probability =    36.5271. Improved by 24.9056.\n",
      "Iteration  4. Log joint probability =    37.3776. Improved by 0.850488.\n",
      "Iteration  5. Log joint probability =    37.6489. Improved by 0.271259.\n",
      "Iteration  6. Log joint probability =    37.6547. Improved by 0.00580657.\n",
      "Iteration  7. Log joint probability =    37.7831. Improved by 0.128419.\n",
      "Iteration  8. Log joint probability =    37.7884. Improved by 0.00527858.\n",
      "Iteration  9. Log joint probability =     37.789. Improved by 0.000612124.\n",
      "Iteration 10. Log joint probability =    37.7891. Improved by 9.93823e-05.\n",
      "Iteration 11. Log joint probability =    37.7902. Improved by 0.00112416.\n",
      "Iteration 12. Log joint probability =    37.7902. Improved by 3.17397e-06.\n",
      "Iteration 13. Log joint probability =    37.7902. Improved by 1.59404e-05.\n",
      "Iteration 14. Log joint probability =    37.7902. Improved by 5.06854e-07.\n",
      "Iteration 15. Log joint probability =    37.7902. Improved by 6.87792e-07.\n",
      "Iteration 16. Log joint probability =    37.7902. Improved by 4.82761e-08.\n",
      "Iteration 17. Log joint probability =    37.7902. Improved by 2.50385e-07.\n",
      "Iteration 18. Log joint probability =    37.7902. Improved by 6.60322e-09.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_forecast(actuals=example_data, predictions=predictions, cutoff=\"2021-01-01\", events=trend_events)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f3ac47b",
   "metadata": {},
   "source": [
    "In this case, once `judgyprophet` observes the data post-event, the Bayesian updating starts to realise the business estimate is a bit large, so it reduces it.\n",
    "\n",
    "This was a simple example to demonstrate `judgyprophet`. You can add many trend events into a single forecasting horizon, add damping. You can also add level events – changes in the forecasting level; and seasonality see our other tutorials for details about this."
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "a947054a5616cfeeb3573dd9a1f17861e3d43e8dd6d728616409daac5064e719"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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