},
"outputs": [],
"source": [
- "data = getData(ts_data=\"../pkg/data/pm10_mesures_H_loc.csv\", exo_data=\"../pkg/data/meteo_extra_noNAs.csv\",\n",
- " input_tz = \"Europe/Paris\", working_tz=\"Europe/Paris\", predict_at=7)"
+ "ts_data = read.csv(system.file(\"extdata\",\"pm10_mesures_H_loc.csv\",package=\"talweg\"))\n",
+ "exo_data = read.csv(system.file(\"extdata\",\"meteo_extra_noNAs.csv\",package=\"talweg\"))\n",
+ "data = getData(ts_data, exo_data, input_tz = \"Europe/Paris\", working_tz=\"Europe/Paris\", predict_at=7)"
]
},
{
" * same_season=FALSE : les indices pour la validation croisée ne tiennent pas compte des saisons\n",
" * mix_strategy=\"mult\" : on multiplie les poids (au lieu d'en éteindre)\n",
"\n",
+ "(valeurs par défaut).\n",
+ "\n",
"J'ai systématiquement comparé à deux autres approches : la persistence et la moyennes des lendemains des jours \"similaires\" dans tout le passé ; à chaque fois sans prédiction du saut (sauf pour Neighbors : prédiction basée sur les poids calculés).\n",
"\n",
"Ensuite j'affiche les erreurs, quelques courbes prévues/mesurées, quelques filaments puis les histogrammes de quelques poids. Concernant les graphes de filaments, la moitié gauche du graphe correspond aux jours similaires au jour courant, tandis que la moitié droite affiche les lendemains : ce sont donc les voisinages tels qu'utilisés dans l'algorithme.\n",
},
"outputs": [],
"source": [
- "indices = seq(as.Date(\"2015-01-18\"),as.Date(\"2015-01-24\"),\"days\")\n",
- "p_ch_nn = getForecast(data,indices,\"Neighbors\",\"Neighbors\",simtype=\"mix\",same_season=FALSE,mix_strategy=\"mult\")\n",
- "p_ch_pz = getForecast(data, indices, \"Persistence\", \"Zero\", same_day=TRUE)\n",
- "p_ch_az = getForecast(data, indices, \"Average\", \"Zero\") #, memory=183)\n",
- "#p_ch_zz = getForecast(data, indices, \"Zero\", \"Zero\")\n",
- "#p_ch_l = getForecast(data, indices, \"Level\", same_day=FALSE)"
+ "indices_ch = seq(as.Date(\"2015-01-18\"),as.Date(\"2015-01-24\"),\"days\")\n",
+ "p_ch_nn = computeForecast(data, indices_ch, \"Neighbors\", \"Neighbors\", simtype=\"mix\")\n",
+ "p_ch_pz = computeForecast(data, indices_ch, \"Persistence\", \"Zero\", same_day=TRUE)\n",
+ "p_ch_az = computeForecast(data, indices_ch, \"Average\", \"Zero\") #, memory=183)\n",
+ "#p_ch_zz = computeForecast(data, indices_ch, \"Zero\", \"Zero\")"
]
},
{
},
"outputs": [],
"source": [
- "e_ch_nn = getError(data, p_ch_nn)\n",
- "e_ch_pz = getError(data, p_ch_pz)\n",
- "e_ch_az = getError(data, p_ch_az)\n",
- "#e_ch_zz = getError(data, p_ch_zz)\n",
- "#e_ch_l = getError(data, p_ch_l)\n",
- "options(repr.plot.width=9, repr.plot.height=6)\n",
+ "e_ch_nn = computeError(data, p_ch_nn)\n",
+ "e_ch_pz = computeError(data, p_ch_pz)\n",
+ "e_ch_az = computeError(data, p_ch_az)\n",
+ "#e_ch_zz = computeError(data, p_ch_zz)\n",
+ "options(repr.plot.width=9, repr.plot.height=7)\n",
"plotError(list(e_ch_nn, e_ch_pz, e_ch_az), cols=c(1,2,colors()[258]))\n",
"\n",
"#Noir: neighbors, rouge: persistence, vert: moyenne"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "La méthode Neighbors fait assez nettement mieux que les autres dans ce cas."
- ]
- },
{
"cell_type": "code",
"execution_count": null,
"#Bleu: prévue, noir: réalisée"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Prédictions d'autant plus lisses que le jour à prévoir est atypique (pollué)."
- ]
- },
{
"cell_type": "code",
"execution_count": null,
"plotPredReal(data, p_ch_az, 4)"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Les erreurs sont proches, mais les courbes prédites très différentes : avantage à \"Neighbors\""
- ]
- },
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"par(mfrow=c(1,2))\n",
- "f3 = computeFilaments(data, p_ch_nn$getIndexInData(3), plot=TRUE)\n",
- "f4 = computeFilaments(data, p_ch_nn$getIndexInData(4), plot=TRUE)"
+ "f3_ch = computeFilaments(data, p_ch_nn$getIndexInData(3), plot=TRUE)\n",
+ "f4_ch = computeFilaments(data, p_ch_nn$getIndexInData(4), plot=TRUE)"
]
},
{
},
"outputs": [],
"source": [
- "par(mfrow=c(2,2))\n",
- "options(repr.plot.width=9, repr.plot.height=6)\n",
- "plotFilamentsBox(data, f3$indices)\n",
- "plotFilamentsBox(data, f3$indices+1)\n",
- "plotFilamentsBox(data, f4$indices)\n",
- "plotFilamentsBox(data, f4$indices+1)"
+ "par(mfrow=c(1,2))\n",
+ "plotFilamentsBox(data, f3_ch)\n",
+ "plotFilamentsBox(data, f4_ch)\n",
+ "\n",
+ "#À gauche : jour 3 + lendemain (4) ; à droite : jour 4 + lendemain (5)"
]
},
{
- "cell_type": "markdown",
- "metadata": {},
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
- "Beaucoup de courbes similaires dans le cas peu pollué, très peu pour un jour pollué."
+ "par(mfrow=c(1,2))\n",
+ "plotRelVar(data, f3_ch)\n",
+ "plotRelVar(data, f4_ch)\n",
+ "\n",
+ "#Variabilité globale en rouge ; sur nos 60 voisins (+ lendemains) en noir"
]
},
{
"outputs": [],
"source": [
"par(mfrow=c(1,2))\n",
- "options(repr.plot.width=9, repr.plot.height=4)\n",
"plotSimils(p_ch_nn, 3)\n",
"plotSimils(p_ch_nn, 4)\n",
"\n",
- "#Non pollué à gauche, pollué au milieu, autre pollué à droite"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "La plupart des poids très proches de zéro ; pas pour le jour 5 : autre type de jour, cf. ci-dessous."
+ "#Non pollué à gauche, pollué à droite"
]
},
{
},
"outputs": [],
"source": [
- "par(mfrow=c(1,2))\n",
- "plotPredReal(data, p_ch_nn, 5)\n",
- "plotFilaments(data, p_ch_nn$getIndexInData(5))"
+ "#Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n",
+ "p_ch_nn$getParams(3)$window\n",
+ "p_ch_nn$getParams(4)$window"
]
},
{
},
"outputs": [],
"source": [
- "indices = seq(as.Date(\"2015-03-15\"),as.Date(\"2015-03-21\"),\"days\")\n",
- "p_ep_nn = getForecast(data,indices,\"Neighbors\",\"Neighbors\",simtype=\"mix\",same_season=FALSE,mix_strategy=\"mult\")\n",
- "p_ep_pz = getForecast(data, indices, \"Persistence\", \"Zero\", same_day=TRUE)\n",
- "p_ep_az = getForecast(data, indices, \"Average\", \"Zero\") #, memory=183)\n",
- "#p_ep_zz = getForecast(data, indices, \"Zero\", \"Zero\")\n",
- "#p_ep_l = getForecast(data, indices, \"Level\", same_day=TRUE)"
+ "indices_ep = seq(as.Date(\"2015-03-15\"),as.Date(\"2015-03-21\"),\"days\")\n",
+ "p_ep_nn = computeForecast(data,indices_ep, \"Neighbors\", \"Neighbors\", simtype=\"mix\")\n",
+ "p_ep_pz = computeForecast(data, indices_ep, \"Persistence\", \"Zero\", same_day=TRUE)\n",
+ "p_ep_az = computeForecast(data, indices_ep, \"Average\", \"Zero\") #, memory=183)\n",
+ "#p_ep_zz = computeForecast(data, indices_ep, \"Zero\", \"Zero\")"
]
},
{
},
"outputs": [],
"source": [
- "class(p_ep_nn)"
+ "e_ep_nn = computeError(data, p_ep_nn)\n",
+ "e_ep_pz = computeError(data, p_ep_pz)\n",
+ "e_ep_az = computeError(data, p_ep_az)\n",
+ "#e_ep_zz = computeError(data, p_ep_zz)\n",
+ "options(repr.plot.width=9, repr.plot.height=7)\n",
+ "plotError(list(e_ep_nn, e_ep_pz, e_ep_az), cols=c(1,2,colors()[258]))\n",
+ "\n",
+ "#Noir: neighbors, rouge: persistence, vert: moyenne"
]
},
{
},
"outputs": [],
"source": [
- "e_ep_nn = getError(data, p_ep_nn)\n",
- "e_ep_pz = getError(data, p_ep_pz)\n",
- "e_ep_az = getError(data, p_ep_az)\n",
- "#e_ep_zz = getError(data, p_ep_zz)\n",
- "#e_ep_l = getError(data, p_ep_l)\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]))\n",
+ "par(mfrow=c(1,2))\n",
+ "options(repr.plot.width=9, repr.plot.height=4)\n",
+ "plotPredReal(data, p_ep_nn, 4)\n",
+ "plotPredReal(data, p_ep_nn, 6)\n",
"\n",
- "#Noir: neighbors, rouge: persistence, vert: moyenne"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Cette fois les deux méthodes naïves font en moyenne moins d'erreurs que Neighbors. Prédiction trop difficile ?"
+ "#Bleu: prévue, noir: réalisée"
]
},
{
"outputs": [],
"source": [
"par(mfrow=c(1,2))\n",
- "options(repr.plot.width=9, repr.plot.height=4)\n",
- "plotPredReal(data, p_ep_nn, 5)\n",
- "plotPredReal(data, p_ep_nn, 2)\n",
- "\n",
- "#Bleu: prévue, noir: réalisée"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "À gauche un jour \"bien\" prévu, à droite le pic d'erreur (jour 6)."
+ "plotPredReal(data, p_ep_az, 4)\n",
+ "plotPredReal(data, p_ep_az, 6)"
]
},
{
"outputs": [],
"source": [
"par(mfrow=c(1,2))\n",
- "plotPredReal(data, p_ep_pz, 4)\n",
- "plotPredReal(data, p_ep_pz, 6)"
+ "f4_ep = computeFilaments(data, p_ep_nn$getIndexInData(4), plot=TRUE)\n",
+ "f6_ep = computeFilaments(data, p_ep_nn$getIndexInData(6), plot=TRUE)"
]
},
{
- "cell_type": "markdown",
- "metadata": {},
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
- "Bonnes performances de la persistence (par chance...)"
+ "par(mfrow=c(2,2))\n",
+ "plotFilamentsBox(data, f4_ep)\n",
+ "plotFilamentsBox(data, f6_ep)\n",
+ "\n",
+ "#À gauche : jour 4 + lendemain (5) ; à droite : jour 6 + lendemain (7)"
]
},
{
"outputs": [],
"source": [
"par(mfrow=c(1,2))\n",
- "plotFilaments(data, p_ep_nn$getIndexInData(5))\n",
- "plotFilaments(data, p_ep_nn$getIndexInData(2))"
+ "plotRelativeVariability(data, f4_ep)\n",
+ "plotRelativeVariability(data, f6_ep)\n",
+ "\n",
+ "#Variabilité globale en rouge ; sur nos 60 voisins (+ lendemains) en noir"
]
},
{
"outputs": [],
"source": [
"par(mfrow=c(1,2))\n",
- "plotSimils(p_ep_nn, 5)\n",
- "plotSimils(p_ep_nn, 2)"
+ "plotSimils(p_ep_nn, 4)\n",
+ "plotSimils(p_ep_nn, 6)"
]
},
{
- "cell_type": "markdown",
- "metadata": {},
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
- "Même observation concernant les poids : concentrés près de zéro pour les prédictions avec peu de voisins."
+ "#Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n",
+ "p_ep_nn$getParams(4)$window\n",
+ "p_ep_nn$getParams(6)$window"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Semaine non polluée"
+ "<h2 style=\"color:blue;font-size:2em\">Semaine non polluée</h2>"
]
},
{
},
"outputs": [],
"source": [
- "indices = seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\")\n",
- "p_np_nn = getForecast(data,indices,\"Neighbors\",\"Neighbors\",simtype=\"mix\",same_season=FALSE,mix_strategy=\"mult\")\n",
- "p_np_pz = getForecast(data, indices, \"Persistence\", \"Zero\", same_day=FALSE)\n",
- "p_np_az = getForecast(data, indices, \"Average\", \"Zero\") #, memory=183)\n",
- "#p_np_zz = getForecast(data, indices, \"Zero\", \"Zero\")\n",
- "#p_np_l = getForecast(data, indices, \"Level\", same_day=FALSE)"
+ "indices_np = seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\")\n",
+ "p_np_nn = computeForecast(data,indices_np, \"Neighbors\", \"Neighbors\", simtype=\"mix\")\n",
+ "p_np_pz = computeForecast(data, indices_np, \"Persistence\", \"Zero\", same_day=FALSE)\n",
+ "p_np_az = computeForecast(data, indices_np, \"Average\", \"Zero\") #, memory=183)\n",
+ "#p_np_zz = computeForecast(data, indices_np, \"Zero\", \"Zero\")"
]
},
{
},
"outputs": [],
"source": [
- "e_np_nn = getError(data, p_np_nn)\n",
- "e_np_pz = getError(data, p_np_pz)\n",
- "e_np_az = getError(data, p_np_az)\n",
- "#e_np_zz = getError(data, p_np_zz)\n",
- "#e_np_l = getError(data, p_np_l)\n",
- "options(repr.plot.width=9, repr.plot.height=6)\n",
+ "e_np_nn = computeError(data, p_np_nn)\n",
+ "e_np_pz = computeError(data, p_np_pz)\n",
+ "e_np_az = computeError(data, p_np_az)\n",
+ "#e_np_zz = computeError(data, p_np_zz)\n",
+ "options(repr.plot.width=9, repr.plot.height=7)\n",
"plotError(list(e_np_nn, e_np_pz, e_np_az), cols=c(1,2,colors()[258]))\n",
"\n",
"#Noir: neighbors, rouge: persistence, vert: moyenne"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Performances des méthodes \"Average\" et \"Neighbors\" comparables ; mauvais résultats pour la persistence."
- ]
- },
{
"cell_type": "code",
"execution_count": null,
"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, 5)\n",
"plotPredReal(data, p_np_nn, 6)\n",
"\n",
"#Bleu: prévue, noir: réalisée"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Les \"bonnes\" prédictions (à gauche) sont tout de même trop lissées."
- ]
- },
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"par(mfrow=c(1,2))\n",
- "plotPredReal(data, p_np_az, 3)\n",
+ "plotPredReal(data, p_np_az, 5)\n",
"plotPredReal(data, p_np_az, 6)"
]
},
{
- "cell_type": "markdown",
- "metadata": {},
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
- "Légèrement meilleur ajustement par la méthode \"Average\" ; très net à droite."
+ "par(mfrow=c(1,2))\n",
+ "f5_np = computeFilaments(data, p_np_nn$getIndexInData(5), plot=TRUE)\n",
+ "f6_np = computeFilaments(data, p_np_nn$getIndexInData(6), plot=TRUE)"
]
},
{
},
"outputs": [],
"source": [
- "par(mfrow=c(1,2))\n",
- "plotFilaments(data, p_np_nn$getIndexInData(3))\n",
- "plotFilaments(data, p_np_nn$getIndexInData(6))"
+ "par(mfrow=c(2,2))\n",
+ "plotFilamentsBox(data, f5_np)\n",
+ "plotFilamentsBox(data, f6_np)\n",
+ "\n",
+ "#À gauche : jour 5 + lendemain (6) ; à droite : jour 6 + lendemain (7)"
]
},
{
- "cell_type": "markdown",
- "metadata": {},
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
- "Jours \"typiques\", donc beaucoup de voisins."
+ "par(mfrow=c(1,2))\n",
+ "plotRelVar(data, f5_np)\n",
+ "plotRelVar(data, f6_np)\n",
+ "\n",
+ "#Variabilité globale en rouge ; sur nos 60 voisins (+ lendemains) en noir"
]
},
{
},
"outputs": [],
"source": [
- "par(mfrow=c(1,3))\n",
- "plotSimils(p_np_nn, 3)\n",
- "plotSimils(p_np_nn, 4)\n",
+ "par(mfrow=c(1,2))\n",
+ "plotSimils(p_np_nn, 5)\n",
"plotSimils(p_np_nn, 6)"
]
},
{
- "cell_type": "markdown",
- "metadata": {},
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
- "Répartition idéale des poids : quelques uns au-delà de 0.3-0.4, le reste très proche de zéro."
+ "#Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n",
+ "p_np_nn$getParams(5)$window\n",
+ "p_np_nn$getParams(6)$window"
]
},
{
projects / talweg.git / blobdiff