{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "dedicated-elements",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sklearn import linear_model\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"id": "medieval-cricket",
"metadata": {},
"source": [
"#### Ελάχιστες ημερήσιες θερμοκρασίες που παρατηρήθηκαν στη Μελβούρνη από 01-01-1981 μέχρι 31-12-1990"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "interior-candidate",
"metadata": {},
"outputs": [],
"source": [
"dataset = pd.read_csv('min_temp.csv', header=0, infer_datetime_format=True, parse_dates=[0], index_col=[0])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "optional-parameter",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" | 1981-01-01 | \n",
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" 13.6 | \n",
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" | 1990-12-29 | \n",
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" | 1990-12-30 | \n",
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" 13.0 | \n",
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"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dataset.plot(figsize=(15,8))\n",
"plt.ylim((-5,30))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "miniature-article",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
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" | 1981-01-01 | \n",
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" | 1981-01-05 | \n",
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" | 1981-01-06 | \n",
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" | 1981-01-07 | \n",
" 15.8 | \n",
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" 15.8 | \n",
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" | 1981-01-08 | \n",
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" 15.4 | \n",
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" | 1990-12-21 | \n",
" 13.1 | \n",
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" 14.7 | \n",
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" | 1990-12-22 | \n",
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" | 1990-12-23 | \n",
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" | 1990-12-30 | \n",
" 15.7 | \n",
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" | 1990-12-31 | \n",
" 13.0 | \n",
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" 15.8 | \n",
" 15.8 | \n",
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" | 1981-01-07 | \n",
" 15.8 | \n",
" 15.8 | \n",
" 15.8 | \n",
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" | 1981-01-08 | \n",
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" ... | \n",
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" 13.9 | \n",
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" | 1990-12-18 | \n",
" 17.2 | \n",
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" 14.7 | \n",
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" 15.4 | \n",
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" | \n",
" | \n",
" | \n",
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" | 1990-12-22 | \n",
" 13.2 | \n",
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" 15.4 | \n",
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" | 1990-12-23 | \n",
" 13.9 | \n",
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" 13.1 | \n",
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" | 1990-12-24 | \n",
" 10.0 | \n",
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" | 1990-12-26 | \n",
" 14.6 | \n",
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" 10.0 | \n",
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" | 1990-12-27 | \n",
" 14.0 | \n",
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" 12.9 | \n",
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" | 1990-12-28 | \n",
" 13.6 | \n",
" 14.0 | \n",
" 14.6 | \n",
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" | 1990-12-29 | \n",
" 13.5 | \n",
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" 14.0 | \n",
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" | 1990-12-30 | \n",
" 15.7 | \n",
" 13.5 | \n",
" 13.6 | \n",
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" | 1990-12-31 | \n",
" 13.0 | \n",
" 15.7 | \n",
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"
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"
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"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Y_t | \n",
" Y_(t-1) | \n",
" Y_(t-2) | \n",
"
\n",
" \n",
" \n",
" \n",
" | Y_t | \n",
" 1.000000 | \n",
" 0.774558 | \n",
" 0.630640 | \n",
"
\n",
" \n",
" | Y_(t-1) | \n",
" 0.774558 | \n",
" 1.000000 | \n",
" 0.774748 | \n",
"
\n",
" \n",
" | Y_(t-2) | \n",
" 0.630640 | \n",
" 0.774748 | \n",
" 1.000000 | \n",
"
\n",
" \n",
"
\n",
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"training_set.plot.scatter(x='Y_(t-2)', y='Y_(t-1)',s=1)"
]
},
{
"cell_type": "markdown",
"id": "surprised-tobago",
"metadata": {},
"source": [
"#### Αυτοπαλινδρομικό μοντέλο 2ης τάξης AR(2)"
]
},
{
"cell_type": "markdown",
"id": "apart-pizza",
"metadata": {},
"source": [
"$$Y_t = A + B^{(1)} Y_{t−1} + B^{(2)} Y_{t−2} + \\epsilon_t$$"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "promising-capacity",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LinearRegression()"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = linear_model.LinearRegression()\n",
"X = training_set[['Y_(t-2)','Y_(t-1)']]\n",
"y = training_set['Y_t']\n",
"model.fit(X,y)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "complicated-consolidation",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.07637309, 0.71502421])"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.coef_"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "phantom-bargain",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Y_t Y_(t-1) Y_(t-2) pr1\n",
"Date \n",
"1990-12-22 13.2 13.1 15.4 12.870713\n",
"1990-12-23 13.9 13.2 13.1 12.766557\n",
"1990-12-24 10.0 13.9 13.2 13.274712\n",
"1990-12-25 12.9 10.0 13.9 10.539578\n",
"1990-12-26 14.6 12.9 10.0 12.315294\n",
"1990-12-27 14.0 14.6 12.9 13.752317\n",
"1990-12-28 13.6 14.0 14.6 13.453136\n",
"1990-12-29 13.5 13.6 14.0 13.121303\n",
"1990-12-30 15.7 13.5 13.6 13.019251\n",
"1990-12-31 13.0 15.7 13.5 14.584667\n"
]
}
],
"source": [
"X = testing_set[['Y_(t-2)','Y_(t-1)']]\n",
"testing_set['pr1'] = model.predict(X)\n",
"print(testing_set)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "bearing-priority",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(5.0, 25.0)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(15,8))\n",
"training_set['Y_t'][3600:].plot(c='blue')\n",
"testing_set['Y_t'].plot(c='blue',alpha=0.5, style=['--'])\n",
"testing_set['pr1'].plot(c='red')\n",
"#testing_set['pr2'].plot(c='magenta')\n",
"plt.ylim((5,25))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "plastic-upset",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(5.0, 25.0)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(15,8))\n",
"training_set['Y_t'][3600:].plot(c='blue')\n",
"testing_set['Y_t'].plot(c='blue',alpha=0.5, style=['--'])\n",
"testing_set['pr1'].plot(c='red')\n",
"#testing_set['pr2'].plot(c='magenta')\n",
"plt.ylim((5,25))"
]
},
{
"cell_type": "markdown",
"id": "subjective-leeds",
"metadata": {},
"source": [
"##### Υπολογισμός του συντελεστή μερικής αυτοσυσχέτισης PACF(2)\n",
"\n",
"$$Y_t = A_1 + B_1 Y_{t−1} + (\\epsilon_1 )_t$$\n",
"$$\\hat y_t = a_1 + b_1 y_{t−1}$$"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "fitting-commercial",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.77420758])"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = training_set[['Y_(t-1)']] # black box\n",
"y = training_set['Y_t']\n",
"model.fit(X,y)\n",
"model.coef_"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "broken-honey",
"metadata": {},
"outputs": [],
"source": [
"training_set['pr_Y_t|Y_(t-1)'] = model.predict(X)\n",
"training_set['e_pr_Y_t|Y_(t-1)'] = training_set['Y_t'] - training_set['pr_Y_t|Y_(t-1)']"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "innovative-bhutan",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Y_t | \n",
" Y_(t-1) | \n",
" Y_(t-2) | \n",
" pr_Y_t|Y_(t-1) | \n",
" e_pr_Y_t|Y_(t-1) | \n",
"
\n",
" \n",
" | Date | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | 1981-01-04 | \n",
" 14.6 | \n",
" 18.8 | \n",
" 17.9 | \n",
" 17.074855 | \n",
" -2.474855 | \n",
"
\n",
" \n",
" | 1981-01-05 | \n",
" 15.8 | \n",
" 14.6 | \n",
" 18.8 | \n",
" 13.823183 | \n",
" 1.976817 | \n",
"
\n",
" \n",
" | 1981-01-06 | \n",
" 15.8 | \n",
" 15.8 | \n",
" 14.6 | \n",
" 14.752232 | \n",
" 1.047768 | \n",
"
\n",
" \n",
" | 1981-01-07 | \n",
" 15.8 | \n",
" 15.8 | \n",
" 15.8 | \n",
" 14.752232 | \n",
" 1.047768 | \n",
"
\n",
" \n",
" | 1981-01-08 | \n",
" 17.4 | \n",
" 15.8 | \n",
" 15.8 | \n",
" 14.752232 | \n",
" 2.647768 | \n",
"
\n",
" \n",
" | ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
"
\n",
" \n",
" | 1990-12-17 | \n",
" 13.9 | \n",
" 13.6 | \n",
" 13.4 | \n",
" 13.048976 | \n",
" 0.851024 | \n",
"
\n",
" \n",
" | 1990-12-18 | \n",
" 17.2 | \n",
" 13.9 | \n",
" 13.6 | \n",
" 13.281238 | \n",
" 3.918762 | \n",
"
\n",
" \n",
" | 1990-12-19 | \n",
" 14.7 | \n",
" 17.2 | \n",
" 13.9 | \n",
" 15.836123 | \n",
" -1.136123 | \n",
"
\n",
" \n",
" | 1990-12-20 | \n",
" 15.4 | \n",
" 14.7 | \n",
" 17.2 | \n",
" 13.900604 | \n",
" 1.499396 | \n",
"
\n",
" \n",
" | 1990-12-21 | \n",
" 13.1 | \n",
" 15.4 | \n",
" 14.7 | \n",
" 14.442549 | \n",
" -1.342549 | \n",
"
\n",
" \n",
"
\n",
"
3637 rows × 5 columns
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Y_t | \n",
" Y_(t-1) | \n",
" Y_(t-2) | \n",
" pr_Y_t|Y_(t-1) | \n",
" e_pr_Y_t|Y_(t-1) | \n",
" pr_Y_(t-2)|Y_(t-1) | \n",
" e_pr_Y_(t-2)|Y_(t-1) | \n",
"
\n",
" \n",
" | Date | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | 1981-01-04 | \n",
" 14.6 | \n",
" 18.8 | \n",
" 17.9 | \n",
" 17.074855 | \n",
" -2.474855 | \n",
" 17.082581 | \n",
" 0.817419 | \n",
"
\n",
" \n",
" | 1981-01-05 | \n",
" 15.8 | \n",
" 14.6 | \n",
" 18.8 | \n",
" 13.823183 | \n",
" 1.976817 | \n",
" 13.827899 | \n",
" 4.972101 | \n",
"
\n",
" \n",
" | 1981-01-06 | \n",
" 15.8 | \n",
" 15.8 | \n",
" 14.6 | \n",
" 14.752232 | \n",
" 1.047768 | \n",
" 14.757808 | \n",
" -0.157808 | \n",
"
\n",
" \n",
" | 1981-01-07 | \n",
" 15.8 | \n",
" 15.8 | \n",
" 15.8 | \n",
" 14.752232 | \n",
" 1.047768 | \n",
" 14.757808 | \n",
" 1.042192 | \n",
"
\n",
" \n",
" | 1981-01-08 | \n",
" 17.4 | \n",
" 15.8 | \n",
" 15.8 | \n",
" 14.752232 | \n",
" 2.647768 | \n",
" 14.757808 | \n",
" 1.042192 | \n",
"
\n",
" \n",
" | ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
"
\n",
" \n",
" | 1990-12-17 | \n",
" 13.9 | \n",
" 13.6 | \n",
" 13.4 | \n",
" 13.048976 | \n",
" 0.851024 | \n",
" 13.052974 | \n",
" 0.347026 | \n",
"
\n",
" \n",
" | 1990-12-18 | \n",
" 17.2 | \n",
" 13.9 | \n",
" 13.6 | \n",
" 13.281238 | \n",
" 3.918762 | \n",
" 13.285452 | \n",
" 0.314548 | \n",
"
\n",
" \n",
" | 1990-12-19 | \n",
" 14.7 | \n",
" 17.2 | \n",
" 13.9 | \n",
" 15.836123 | \n",
" -1.136123 | \n",
" 15.842702 | \n",
" -1.942702 | \n",
"
\n",
" \n",
" | 1990-12-20 | \n",
" 15.4 | \n",
" 14.7 | \n",
" 17.2 | \n",
" 13.900604 | \n",
" 1.499396 | \n",
" 13.905391 | \n",
" 3.294609 | \n",
"
\n",
" \n",
" | 1990-12-21 | \n",
" 13.1 | \n",
" 15.4 | \n",
" 14.7 | \n",
" 14.442549 | \n",
" -1.342549 | \n",
" 14.447838 | \n",
" 0.252162 | \n",
"
\n",
" \n",
"
\n",
"
3637 rows × 7 columns
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" e_pr_Y_t|Y_(t-1) | \n",
" e_pr_Y_(t-2)|Y_(t-1) | \n",
"
\n",
" \n",
" \n",
" \n",
" | e_pr_Y_t|Y_(t-1) | \n",
" 1.000000 | \n",
" 0.076397 | \n",
"
\n",
" \n",
" | e_pr_Y_(t-2)|Y_(t-1) | \n",
" 0.076397 | \n",
" 1.000000 | \n",
"
\n",
" \n",
"
\n",
"