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dartdog/Airma_sample

Last active December 28, 2015 08:49
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Stats models Pandas Airma Sample with questions:
Raw
Airma_sample
{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"http://nbviewer.ipython.org/7473989"
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"1. Data load/clean"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.display import HTML\n",
"from numpy import nan\n",
"from numpy.random import randn\n",
"import numpy as np\n",
"np.set_printoptions(precision=2)\n",
"import pandas as pd\n",
"from pandas.core.common import adjoin\n",
"from pandas.io.data import DataReader\n",
"import pandas.util.testing as tm\n",
"tm.N = 10\n",
"import statsmodels.api as sm\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import cm\n",
"import matplotlib as mpl\n",
"import scipy.stats as stats\n",
"pd.options.display.max_columns=80\n",
"from StringIO import StringIO \n",
"import requests\n",
"from statsmodels.graphics.api import qqplot"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#get the statewide actual data from Sheet 1 parse dates and select just unit sales\n",
"act = requests.get('https://docs.google.com/spreadsheet/ccc?key=0Ak_wF7ZGeMmHdFZtQjI1a1hhUWR2UExCa2E4MFhiWWc&output=csv&gid=1')\n",
"dataact = act.content\n",
"actdf = pd.read_csv(StringIO(dataact),index_col=0,parse_dates=['date'], thousands=',') #converts to numbers\n",
"actdf.rename(columns={'Unit Sales': 'Units'}, inplace=True)\n",
"actdf=actdf[['Units']]\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I cribbed most of this code from Jseabold's tsa_arma notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I'd like this to be a useful basic intro to the use of stats models Arima functions:\n",
"The data is real, monthly redidential Real Estate Sales in Alabama, therefore highly seasonal\n",
"You can also now see the upward drift as the market recovers with higher highs and higher lows.."
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"2. data view"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"actdf.plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"<matplotlib.axes.AxesSubplot at 0x2cccfd0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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eTGIi9dNcuaKsTZ6gqYnq9i9fbvu/cuxz/DjVUwLIQpw71/acwUCWzcmTzo/h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\nDkfHcJHncDgcHfP/Gbg+V7BvyiUAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x2ccca90>"
]
}
],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"3. Data inspect acf pacf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Not real sure how to interpet these two graphs.. I think it says that as I suspect the auto correlaton at the 0 and 12 month levels are high, the PCF graph is still more of a mystery..Comments tell me that the ACF iddicates the longer cycle sould be diffed out,, and that the signifigance at ponts 0 and 12 indicate the seasonality is strongest there (as suspected). the PACF looks mostly random (except for 0 and 1).. Which is mostly what we should have? note that I did do a single order diff below Heading 5"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure(figsize=(12,8))\n",
"ax1 = fig.add_subplot(211)\n",
"fig = sm.graphics.tsa.plot_acf(actdf.values.squeeze(), lags=13, ax=ax1)\n",
"ax2 = fig.add_subplot(212)\n",
"fig = sm.graphics.tsa.plot_pacf(actdf, lags=13, ax=ax2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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RsSSoYFxSUsK0adPIzMwEYNmyZezYsSMgGD/11FN861vfIj09HYCJEycGc0iRkNB8lyIi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\n",
"text": [
"<matplotlib.figure.Figure at 0x48d66d0>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"4. Arima"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from statsmodels import tsa"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p=1\n",
"d=1\n",
"q=1\n",
"model1=tsa.arima_model.ARIMA(actdf, [p, d, q],freq='M').fit() #creates model"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"model1.summary() #summary Lgo Liklihood, AIC, BIC, HQIC, SD of innovations\n",
"#the std error of ar.L.1.D.Units and ma.L.1.D.Units seem quite low, (less than 1)?\n",
"#What is going on with Roots table? if it had data what would it be telling us?"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class=\"simpletable\">\n",
"<caption>ARIMA Model Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>D.Units</td> <th> No. Observations: </th> <td>160</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>ARIMA(1, 1, 1)</td> <th> Log Likelihood </th> <td>-1216.771</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>css-mle</td> <th> S.D. of innovations</th> <td>485.839</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 18 Nov 2013</td> <th> AIC </th> <td>2441.542</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>10:31:51</td> <th> BIC </th> <td>2453.842</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Sample:</th> <td>08-01-2013</td> <th> HQIC </th> <td>2446.537</td> \n",
"</tr>\n",
"<tr>\n",
" <th></th> <td>- 05-01-2000</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> -2.8504</td> <td> 39.739</td> <td> -0.072</td> <td> 0.943</td> <td> -80.738 75.037</td>\n",
"</tr>\n",
"<tr>\n",
" <th>ar.L1.D.Units</th> <td> 0.3665</td> <td> 0.526</td> <td> 0.697</td> <td> 0.487</td> <td> -0.665 1.398</td>\n",
"</tr>\n",
"<tr>\n",
" <th>ma.L1.D.Units</th> <td> -0.3444</td> <td> 0.526</td> <td> -0.655</td> <td> 0.513</td> <td> -1.375 0.686</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Roots</caption>\n",
"<tr>\n",
" <td></td> <th> Real</th> <th> Imaginary</th> <th> Modulus</th> <th> Frequency</th>\n",
"</tr>\n",
"<tr>\n",
" <th>AR.1</th> 2.7287 +0.0000j 2.7287 0.0000 \n",
"</tr>\n",
"<tr>\n",
" <th>MA.1</th> 2.9035 +0.0000j 2.9035 0.0000 \n",
"</tr>\n",
"</table>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" ARIMA Model Results \n",
"==============================================================================\n",
"Dep. Variable: D.Units No. Observations: 160\n",
"Model: ARIMA(1, 1, 1) Log Likelihood -1216.771\n",
"Method: css-mle S.D. of innovations 485.839\n",
"Date: Mon, 18 Nov 2013 AIC 2441.542\n",
"Time: 10:31:51 BIC 2453.842\n",
"Sample: 08-01-2013 HQIC 2446.537\n",
" - 05-01-2000 \n",
"=================================================================================\n",
" coef std err z P>|z| [95.0% Conf. Int.]\n",
"---------------------------------------------------------------------------------\n",
"const -2.8504 39.739 -0.072 0.943 -80.738 75.037\n",
"ar.L1.D.Units 0.3665 0.526 0.697 0.487 -0.665 1.398\n",
"ma.L1.D.Units -0.3444 0.526 -0.655 0.513 -1.375 0.686\n",
" Roots \n",
"=============================================================================\n",
" Real Imaginary Modulus Frequency\n",
"-----------------------------------------------------------------------------\n",
"AR.1 2.7287 +0.0000j 2.7287 0.0000\n",
"MA.1 2.9035 +0.0000j 2.9035 0.0000\n",
"-----------------------------------------------------------------------------\n",
"\"\"\""
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#how to use this one?\n",
"#tsa.arima_model.ARIMAResults(model1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Can't seem get a prediction either in or out of sample?\n",
"#print model1.predict('2013-1-01', '2013-12-01', dynamic=True, typ='levels') \n",
"predict1_units = model1.predict('2012-1-01','2013-12-01', dynamic=True)\n",
"print predict1_units"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "Wrong number of items passed 23, indices imply 303",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-9-a76ec67be707>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m#Can't seem get a prediction either in or out of sample?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m#print model1.predict('2013-1-01', '2013-12-01', dynamic=True, typ='levels')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mpredict1_units\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'2012-1-01'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'2013-12-01'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdynamic\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mpredict1_units\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/pymodules/python2.7/statsmodels/base/wrapper.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 88\u001b[0m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'_results'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 89\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 90\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrap_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 91\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 92\u001b[0m \u001b[0margspec\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minspect\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetargspec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/pymodules/python2.7/statsmodels/base/data.pyc\u001b[0m in \u001b[0;36mwrap_output\u001b[1;34m(self, obj, how)\u001b[0m\n\u001b[0;32m 262\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattach_cov\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 263\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mhow\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'dates'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 264\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattach_dates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 265\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mhow\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'columns_eq'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 266\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattach_columns_eq\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/pymodules/python2.7/statsmodels/base/data.pyc\u001b[0m in \u001b[0;36mattach_dates\u001b[1;34m(self, result)\u001b[0m\n\u001b[0;32m 356\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 357\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mattach_dates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 358\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mTimeSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict_dates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 359\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 360\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_make_endog_names\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[0;32m 215\u001b[0m raise_cast_failure=True)\n\u001b[0;32m 216\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 217\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSingleBlockManager\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 218\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 219\u001b[0m \u001b[0mgeneric\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNDFrame\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, block, axis, do_integrity_check, fastpath)\u001b[0m\n\u001b[0;32m 3297\u001b[0m \u001b[0mblock\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mblock\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3298\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBlock\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3299\u001b[1;33m \u001b[0mblock\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmake_block\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3300\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3301\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mmake_block\u001b[1;34m(values, items, ref_items, klass, ndim, dtype, fastpath, placement)\u001b[0m\n\u001b[0;32m 1805\u001b[0m \u001b[0mklass\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mObjectBlock\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1806\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1807\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mklass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mitems\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mref_items\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfastpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplacement\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mplacement\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1808\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1809\u001b[0m \u001b[1;31m# TODO: flexible with index=None and/or items=None\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, values, items, ref_items, ndim, fastpath, placement)\u001b[0m\n\u001b[0;32m 60\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 61\u001b[0m raise ValueError('Wrong number of items passed %d, indices imply %d'\n\u001b[1;32m---> 62\u001b[1;33m % (len(items), len(values)))\n\u001b[0m\u001b[0;32m 63\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 64\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_ref_locs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplacement\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: Wrong number of items passed 23, indices imply 303"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ax = model1.plot(ax=ax, style='r--', label='Dynamic Prediction');\n",
"ax.legend();\n",
"#ax.axis((-20.0, 38.0, -4.0, 200.0));\n",
"ax.plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'ARIMAResults' object has no attribute 'plot'",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-10-d5511c4ee826>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'r--'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Dynamic Prediction'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;31m#ax.axis((-20.0, 38.0, -4.0, 200.0));\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/pymodules/python2.7/statsmodels/base/wrapper.pyc\u001b[0m in \u001b[0;36m__getattribute__\u001b[1;34m(self, attr)\u001b[0m\n\u001b[0;32m 33\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 34\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 35\u001b[1;33m \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 36\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 37\u001b[0m \u001b[0mhow\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_wrap_attrs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mattr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mAttributeError\u001b[0m: 'ARIMAResults' object has no attribute 'plot'"
]
}
],
"prompt_number": 10
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"5. Diff and plot data"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#a single order diff yields bank acf and pacf is this right? note the next lot shows that the diffed data \n",
"#has no remaing cyclical trend tha I can see\n",
"actdf2=actdf.diff()\n",
"fig = plt.figure(figsize=(12,8))\n",
"ax1 = fig.add_subplot(211)\n",
"fig = sm.graphics.tsa.plot_acf(actdf2.values.squeeze(), lags=13, ax=ax1)\n",
"ax2 = fig.add_subplot(212)\n",
"fig = sm.graphics.tsa.plot_pacf(actdf2, lags=13, ax=ax2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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tXSkqKuLcc8+lW7du3H///f/Un4cknYrBWNIX1j9mUsvLy3nppZcYNGgQx44d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\n",
"text": [
"<matplotlib.figure.Figure at 0x519ad10>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"actdf2.plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"<matplotlib.axes.AxesSubplot at 0x530a850>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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CdrqDeMPhL5GReqilluXnFEnATk2Aj9is0gQGD/ZdKF20BPS4g0RLSxyl87qc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\nAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x52f5e10>"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#at some point I'd like to get this working also\n",
"#dynammodel=sm.tsa.vector_ar.dynamic.DynamicVAR(actdf, lag_order=1, window=None, window_type='expanding', trend='c', min_periods=None)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"6. ARMA "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What does this tell me? (below)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arma_mod12 = sm.tsa.ARMA(actdf,(12,0)).fit()\n",
"print arma_mod12.params"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"const 3342.634789\n",
"ar.L1.Units 0.688099\n",
"ar.L2.Units 0.099637\n",
"ar.L3.Units 0.160202\n",
"ar.L4.Units -0.292844\n",
"ar.L5.Units 0.046065\n",
"ar.L6.Units -0.216280\n",
"ar.L7.Units 0.143673\n",
"ar.L8.Units -0.014395\n",
"ar.L9.Units 0.335274\n",
"ar.L10.Units -0.176165\n",
"ar.L11.Units 0.013755\n",
"ar.L12.Units 0.178772\n",
"dtype: float64\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I don't know how to interpet this below? The residuals?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"resid = arma_mod12.resid\n",
"stats.normaltest(resid)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"(12.796574667872109, 0.0016644054041814781)"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This looks like a great chart below but no idea of what it means.."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure(figsize=(12,8))\n",
"ax = fig.add_subplot(111)\n",
"fig = qqplot(resid, line='q', ax=ax, fit=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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czz777BLfX3DBBZx00kk1VpAkSTWlaDZza2AsMaRwHoczlo4sJpY/8AYLmAGl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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x4eb6d90>"
]
}
],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I cannot seem to select the dates for in sample prediction, much less tell the system to project out of sample\n",
"I would have thought I could use the Pandas DF notation like ('2012-1-1':'2013-9-1') Assuming I just wanted the in \n",
"sample prediction for that period."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"predict_units = arma_mod12.predict('2012-1-1',dynamic=True)\n",
"print predict_units"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2012-01-01 2630.343093\n",
"2011-12-01 2560.204981\n",
"2011-11-01 2716.596701\n",
"2011-10-01 2930.735217\n",
"2011-09-01 3154.498396\n",
"2011-08-01 3598.095215\n",
"2011-07-01 3649.452966\n",
"2011-06-01 3785.937579\n",
"2011-05-01 3630.817452\n",
"2011-04-01 3478.927091\n",
"2011-03-01 3302.637450\n",
"2011-02-01 2970.171888\n",
"2011-01-01 2847.359800\n",
"2010-12-01 2737.204984\n",
"2010-11-01 2833.168512\n",
"...\n",
"2001-07-01 3281.110218\n",
"2001-06-01 3291.310834\n",
"2001-05-01 3302.873167\n",
"2001-04-01 3312.975171\n",
"2001-03-01 3319.301975\n",
"2001-02-01 3320.582686\n",
"2001-01-01 3316.860082\n",
"2000-12-01 3309.402505\n",
"2000-11-01 3300.334602\n",
"2000-10-01 3292.067383\n",
"2000-09-01 3286.688035\n",
"2000-08-01 3285.465379\n",
"2000-07-01 3288.575575\n",
"2000-06-01 3295.119256\n",
"2000-05-01 3303.394592\n",
"Length: 141\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"7. ARMA prediction?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Again I just want the 2013 projection and maybe into 2014? Looks like in spite of all that the red dotted line is beginning to track fairly well. at least in the most recent time periods."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ax = actdf.plot(figsize=(12,8))\n",
"ax = predict_units.plot(ax=ax, style='r--', label='Dynamic Prediction');\n",
"ax.legend();\n",
"#ax.axis((-20.0, 38.0, -4.0, 200.0));\n",
"ax.plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"[]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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h1EHWIn8MGBOxIKLwwQ4yOTFXZR96jWVFBbB+vWys8M47utyF7tTuscOhz+1H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INzaR6cS/PgXJoD57FhYIBMRERERGSFNchERERE1OCwBpmIiIiIyE0MkKkK66rMg3NpHpxL8+Bc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\nrDBAJiIiIiKywgCZiIiIiMjK/wfOztNDa8uqtQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x519a950>"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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