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[talweg.git] / reports / report_2017-03-01.ipynb
diff --git a/reports/report_2017-03-01.ipynb b/reports/report_2017-03-01.ipynb
new file mode 100644 (file)
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--- /dev/null
+++ b/reports/report_2017-03-01.ipynb
@@ -0,0 +1,281 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "library(talweg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "data = getData(ts_data=\"../data/pm10_mesures_H_loc.csv\", exo_data=\"../data/meteo_extra_noNAs.csv\",\n",
+    "               input_tz = \"Europe/Paris\", working_tz=\"Europe/Paris\", predict_at=7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pollution par chauffage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "p_ch_nn = getForecast(data, seq(as.Date(\"2015-01-18\"),as.Date(\"2015-01-24\"),\"days\"), Inf, 17,\n",
+    "                   \"Neighbors\", \"Neighbors\", simtype=\"mix\", same_season=FALSE, mix_strategy=\"mult\")\n",
+    "p_ch_pz = getForecast(data, seq(as.Date(\"2015-01-18\"),as.Date(\"2015-01-24\"),\"days\"), Inf, 17,\n",
+    "                   \"Persistence\", \"Zero\")\n",
+    "p_ch_az = getForecast(data, seq(as.Date(\"2015-01-18\"),as.Date(\"2015-01-24\"),\"days\"), Inf, 17,\n",
+    "                   \"Average\", \"Zero\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "e_ch_nn = getError(data, p_ch_nn, 17)\n",
+    "e_ch_pz = getError(data, p_ch_pz, 17)\n",
+    "e_ch_az = getError(data, p_ch_az, 17)\n",
+    "options(repr.plot.width=9, repr.plot.height=6)\n",
+    "plotError(list(e_ch_nn, e_ch_pz, e_ch_az), cols=c(1,2,colors()[258]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "par(mfrow=c(1,2))\n",
+    "options(repr.plot.width=9, repr.plot.height=4)\n",
+    "plotPredReal(data, p_ch_nn, 3)\n",
+    "plotPredReal(data, p_ch_nn, 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "par(mfrow=c(1,2))\n",
+    "plotFilaments(data, p_ch_nn$getIndexInData(3))\n",
+    "plotFilaments(data, p_ch_nn$getIndexInData(4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "par(mfrow=c(1,3))\n",
+    "plotSimils(p_ch_nn, 3)\n",
+    "plotSimils(p_ch_nn, 4)\n",
+    "plotSimils(p_ch_nn, 5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pollution par épandage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "p_ep_nn = getForecast(data, seq(as.Date(\"2015-03-15\"),as.Date(\"2015-03-21\"),\"days\"), Inf, 17,\n",
+    "                   \"Neighbors\", \"Neighbors\", simtype=\"mix\", same_season=FALSE, mix_strategy=\"mult\")\n",
+    "p_ep_pz = getForecast(data, seq(as.Date(\"2015-03-15\"),as.Date(\"2015-03-21\"),\"days\"), Inf, 17,\n",
+    "                   \"Persistence\", \"Zero\")\n",
+    "p_ep_az = getForecast(data, seq(as.Date(\"2015-03-15\"),as.Date(\"2015-03-21\"),\"days\"), Inf, 17,\n",
+    "                   \"Average\", \"Zero\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "e_ep_nn = getError(data, p_ep_nn, 17)\n",
+    "e_ep_pz = getError(data, p_ep_pz, 17)\n",
+    "e_ep_az = getError(data, p_ep_az, 17)\n",
+    "options(repr.plot.width=9, repr.plot.height=6)\n",
+    "plotError(list(e_ep_nn, e_ep_pz, e_ep_az), cols=c(1,2,colors()[258]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "par(mfrow=c(1,2))\n",
+    "options(repr.plot.width=9, repr.plot.height=4)\n",
+    "plotPredReal(data, p_ep_nn, 3)\n",
+    "plotPredReal(data, p_ep_nn, 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "par(mfrow=c(1,2))\n",
+    "plotFilaments(data, p_ep_nn$getIndexInData(3))\n",
+    "plotFilaments(data, p_ep_nn$getIndexInData(4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "par(mfrow=c(1,3))\n",
+    "plotSimils(p_ep_nn, 3)\n",
+    "plotSimils(p_ep_nn, 4)\n",
+    "plotSimils(p_ep_nn, 5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Semaine non polluée"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "p_np_nn = getForecast(data, seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\"), Inf, 17,\n",
+    "                   \"Neighbors\", \"Neighbors\", simtype=\"mix\", same_season=FALSE, mix_strategy=\"mult\")\n",
+    "p_np_pz = getForecast(data, seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\"), Inf, 17,\n",
+    "                   \"Persistence\", \"Zero\")\n",
+    "p_np_az = getForecast(data, seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\"), Inf, 17,\n",
+    "                   \"Average\", \"Zero\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "e_np_nn = getError(data, p_np_nn, 17)\n",
+    "e_np_pz = getError(data, p_np_pz, 17)\n",
+    "e_np_az = getError(data, p_np_az, 17)\n",
+    "options(repr.plot.width=9, repr.plot.height=6)\n",
+    "plotError(list(e_np_nn, e_np_pz, e_np_az), cols=c(1,2,colors()[258]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "par(mfrow=c(1,2))\n",
+    "options(repr.plot.width=9, repr.plot.height=4)\n",
+    "plotPredReal(data, p_np_nn, 3)\n",
+    "plotPredReal(data, p_np_nn, 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "par(mfrow=c(1,2))\n",
+    "plotFilaments(data, p_np_nn$getIndexInData(3))\n",
+    "plotFilaments(data, p_np_nn$getIndexInData(4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "par(mfrow=c(1,3))\n",
+    "plotSimils(p_np_nn, 3)\n",
+    "plotSimils(p_np_nn, 4)\n",
+    "plotSimils(p_np_nn, 5)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.3.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
Time-series sAmpLes forecasted With ExoGenous variables