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coal-price-forecast

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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 2,
6
+ "metadata": {},
7
+ "outputs": [],
8
+ "source": [
9
+ "# Import packages\n",
10
+ "import plotly.graph_objects as go\n",
11
+ "import pandas as pd\n",
12
+ "import plotly.express as px\n",
13
+ "from statsmodels.tsa.holtwinters import ExponentialSmoothing"
14
+ ]
15
+ },
16
+ {
17
+ "cell_type": "code",
18
+ "execution_count": 3,
19
+ "metadata": {},
20
+ "outputs": [],
21
+ "source": [
22
+ "# Read in the data\n",
23
+ "data = pd.read_csv(\"../coal-price-data/AirPassengers.csv\")"
24
+ ]
25
+ },
26
+ {
27
+ "cell_type": "code",
28
+ "execution_count": 4,
29
+ "metadata": {},
30
+ "outputs": [],
31
+ "source": [
32
+ "data[\"Month\"] = pd.to_datetime(data[\"Month\"])"
33
+ ]
34
+ },
35
+ {
36
+ "cell_type": "code",
37
+ "execution_count": 5,
38
+ "metadata": {},
39
+ "outputs": [],
40
+ "source": [
41
+ "# Split train and test\n",
42
+ "train = data.iloc[: -int(len(data) * 0.2)]\n",
43
+ "test = data.iloc[-int(len(data) * 0.2) :]"
44
+ ]
45
+ },
46
+ {
47
+ "cell_type": "code",
48
+ "execution_count": 6,
49
+ "metadata": {},
50
+ "outputs": [],
51
+ "source": [
52
+ "def plot_func(forecast: list[float], title: str, save_path: str = None) -> None:\n",
53
+ " \"\"\"Function to plot the forecasts.\"\"\"\n",
54
+ " fig = go.Figure()\n",
55
+ " fig.add_trace(\n",
56
+ " go.Scatter(x=train[\"Month\"], y=train[\"#Passengers\"], name=\"Train\")\n",
57
+ " )\n",
58
+ " fig.add_trace(\n",
59
+ " go.Scatter(x=test[\"Month\"], y=test[\"#Passengers\"], name=\"Test\")\n",
60
+ " )\n",
61
+ " fig.add_trace(go.Scatter(x=test[\"Month\"], y=forecast, name=\"Forecast\"))\n",
62
+ " fig.update_layout(\n",
63
+ " template=\"simple_white\",\n",
64
+ " font=dict(size=18),\n",
65
+ " title_text=title,\n",
66
+ " width=700,\n",
67
+ " title_x=0.5,\n",
68
+ " height=400,\n",
69
+ " xaxis_title=\"Date\",\n",
70
+ " yaxis_title=\"Passenger Volume\",\n",
71
+ " )\n",
72
+ "\n",
73
+ " return fig.show()"
74
+ ]
75
+ },
76
+ {
77
+ "cell_type": "code",
78
+ "execution_count": 7,
79
+ "metadata": {},
80
+ "outputs": [
81
+ {
82
+ "name": "stderr",
83
+ "output_type": "stream",
84
+ "text": [
85
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning: overflow encountered in matmul\n",
86
+ " return err.T @ err\n"
87
+ ]
88
+ }
89
+ ],
90
+ "source": [
91
+ "# Fit Holt Winters model and get forecasts\n",
92
+ "model = ExponentialSmoothing(\n",
93
+ " train[\"#Passengers\"], trend=\"mul\", seasonal=\"mul\", seasonal_periods=12\n",
94
+ ").fit(optimized=True)"
95
+ ]
96
+ },
97
+ {
98
+ "cell_type": "code",
99
+ "execution_count": 13,
100
+ "metadata": {},
101
+ "outputs": [
102
+ {
103
+ "data": {
104
+ "text/plain": [
105
+ "<statsmodels.tsa.holtwinters.results.HoltWintersResultsWrapper at 0x7fc76fba6ed0>"
106
+ ]
107
+ },
108
+ "execution_count": 13,
109
+ "metadata": {},
110
+ "output_type": "execute_result"
111
+ }
112
+ ],
113
+ "source": [
114
+ "model"
115
+ ]
116
+ },
117
+ {
118
+ "cell_type": "code",
119
+ "execution_count": 8,
120
+ "metadata": {},
121
+ "outputs": [],
122
+ "source": [
123
+ "forecasts = model.forecast(len(test))"
124
+ ]
125
+ },
126
+ {
127
+ "cell_type": "code",
128
+ "execution_count": 12,
129
+ "metadata": {},
130
+ "outputs": [
131
+ {
132
+ "data": {
133
+ "text/html": [
134
+ "<div>\n",
135
+ "<style scoped>\n",
136
+ " .dataframe tbody tr th:only-of-type {\n",
137
+ " vertical-align: middle;\n",
138
+ " }\n",
139
+ "\n",
140
+ " .dataframe tbody tr th {\n",
141
+ " vertical-align: top;\n",
142
+ " }\n",
143
+ "\n",
144
+ " .dataframe thead th {\n",
145
+ " text-align: right;\n",
146
+ " }\n",
147
+ "</style>\n",
148
+ "<table border=\"1\" class=\"dataframe\">\n",
149
+ " <thead>\n",
150
+ " <tr style=\"text-align: right;\">\n",
151
+ " <th></th>\n",
152
+ " <th>Month</th>\n",
153
+ " <th>#Passengers</th>\n",
154
+ " </tr>\n",
155
+ " </thead>\n",
156
+ " <tbody>\n",
157
+ " <tr>\n",
158
+ " <th>116</th>\n",
159
+ " <td>1958-09-01</td>\n",
160
+ " <td>404</td>\n",
161
+ " </tr>\n",
162
+ " <tr>\n",
163
+ " <th>117</th>\n",
164
+ " <td>1958-10-01</td>\n",
165
+ " <td>359</td>\n",
166
+ " </tr>\n",
167
+ " <tr>\n",
168
+ " <th>118</th>\n",
169
+ " <td>1958-11-01</td>\n",
170
+ " <td>310</td>\n",
171
+ " </tr>\n",
172
+ " <tr>\n",
173
+ " <th>119</th>\n",
174
+ " <td>1958-12-01</td>\n",
175
+ " <td>337</td>\n",
176
+ " </tr>\n",
177
+ " <tr>\n",
178
+ " <th>120</th>\n",
179
+ " <td>1959-01-01</td>\n",
180
+ " <td>360</td>\n",
181
+ " </tr>\n",
182
+ " <tr>\n",
183
+ " <th>121</th>\n",
184
+ " <td>1959-02-01</td>\n",
185
+ " <td>342</td>\n",
186
+ " </tr>\n",
187
+ " <tr>\n",
188
+ " <th>122</th>\n",
189
+ " <td>1959-03-01</td>\n",
190
+ " <td>406</td>\n",
191
+ " </tr>\n",
192
+ " <tr>\n",
193
+ " <th>123</th>\n",
194
+ " <td>1959-04-01</td>\n",
195
+ " <td>396</td>\n",
196
+ " </tr>\n",
197
+ " <tr>\n",
198
+ " <th>124</th>\n",
199
+ " <td>1959-05-01</td>\n",
200
+ " <td>420</td>\n",
201
+ " </tr>\n",
202
+ " <tr>\n",
203
+ " <th>125</th>\n",
204
+ " <td>1959-06-01</td>\n",
205
+ " <td>472</td>\n",
206
+ " </tr>\n",
207
+ " <tr>\n",
208
+ " <th>126</th>\n",
209
+ " <td>1959-07-01</td>\n",
210
+ " <td>548</td>\n",
211
+ " </tr>\n",
212
+ " <tr>\n",
213
+ " <th>127</th>\n",
214
+ " <td>1959-08-01</td>\n",
215
+ " <td>559</td>\n",
216
+ " </tr>\n",
217
+ " <tr>\n",
218
+ " <th>128</th>\n",
219
+ " <td>1959-09-01</td>\n",
220
+ " <td>463</td>\n",
221
+ " </tr>\n",
222
+ " <tr>\n",
223
+ " <th>129</th>\n",
224
+ " <td>1959-10-01</td>\n",
225
+ " <td>407</td>\n",
226
+ " </tr>\n",
227
+ " <tr>\n",
228
+ " <th>130</th>\n",
229
+ " <td>1959-11-01</td>\n",
230
+ " <td>362</td>\n",
231
+ " </tr>\n",
232
+ " <tr>\n",
233
+ " <th>131</th>\n",
234
+ " <td>1959-12-01</td>\n",
235
+ " <td>405</td>\n",
236
+ " </tr>\n",
237
+ " <tr>\n",
238
+ " <th>132</th>\n",
239
+ " <td>1960-01-01</td>\n",
240
+ " <td>417</td>\n",
241
+ " </tr>\n",
242
+ " <tr>\n",
243
+ " <th>133</th>\n",
244
+ " <td>1960-02-01</td>\n",
245
+ " <td>391</td>\n",
246
+ " </tr>\n",
247
+ " <tr>\n",
248
+ " <th>134</th>\n",
249
+ " <td>1960-03-01</td>\n",
250
+ " <td>419</td>\n",
251
+ " </tr>\n",
252
+ " <tr>\n",
253
+ " <th>135</th>\n",
254
+ " <td>1960-04-01</td>\n",
255
+ " <td>461</td>\n",
256
+ " </tr>\n",
257
+ " <tr>\n",
258
+ " <th>136</th>\n",
259
+ " <td>1960-05-01</td>\n",
260
+ " <td>472</td>\n",
261
+ " </tr>\n",
262
+ " <tr>\n",
263
+ " <th>137</th>\n",
264
+ " <td>1960-06-01</td>\n",
265
+ " <td>535</td>\n",
266
+ " </tr>\n",
267
+ " <tr>\n",
268
+ " <th>138</th>\n",
269
+ " <td>1960-07-01</td>\n",
270
+ " <td>622</td>\n",
271
+ " </tr>\n",
272
+ " <tr>\n",
273
+ " <th>139</th>\n",
274
+ " <td>1960-08-01</td>\n",
275
+ " <td>606</td>\n",
276
+ " </tr>\n",
277
+ " <tr>\n",
278
+ " <th>140</th>\n",
279
+ " <td>1960-09-01</td>\n",
280
+ " <td>508</td>\n",
281
+ " </tr>\n",
282
+ " <tr>\n",
283
+ " <th>141</th>\n",
284
+ " <td>1960-10-01</td>\n",
285
+ " <td>461</td>\n",
286
+ " </tr>\n",
287
+ " <tr>\n",
288
+ " <th>142</th>\n",
289
+ " <td>1960-11-01</td>\n",
290
+ " <td>390</td>\n",
291
+ " </tr>\n",
292
+ " <tr>\n",
293
+ " <th>143</th>\n",
294
+ " <td>1960-12-01</td>\n",
295
+ " <td>432</td>\n",
296
+ " </tr>\n",
297
+ " </tbody>\n",
298
+ "</table>\n",
299
+ "</div>"
300
+ ],
301
+ "text/plain": [
302
+ " Month #Passengers\n",
303
+ "116 1958-09-01 404\n",
304
+ "117 1958-10-01 359\n",
305
+ "118 1958-11-01 310\n",
306
+ "119 1958-12-01 337\n",
307
+ "120 1959-01-01 360\n",
308
+ "121 1959-02-01 342\n",
309
+ "122 1959-03-01 406\n",
310
+ "123 1959-04-01 396\n",
311
+ "124 1959-05-01 420\n",
312
+ "125 1959-06-01 472\n",
313
+ "126 1959-07-01 548\n",
314
+ "127 1959-08-01 559\n",
315
+ "128 1959-09-01 463\n",
316
+ "129 1959-10-01 407\n",
317
+ "130 1959-11-01 362\n",
318
+ "131 1959-12-01 405\n",
319
+ "132 1960-01-01 417\n",
320
+ "133 1960-02-01 391\n",
321
+ "134 1960-03-01 419\n",
322
+ "135 1960-04-01 461\n",
323
+ "136 1960-05-01 472\n",
324
+ "137 1960-06-01 535\n",
325
+ "138 1960-07-01 622\n",
326
+ "139 1960-08-01 606\n",
327
+ "140 1960-09-01 508\n",
328
+ "141 1960-10-01 461\n",
329
+ "142 1960-11-01 390\n",
330
+ "143 1960-12-01 432"
331
+ ]
332
+ },
333
+ "execution_count": 12,
334
+ "metadata": {},
335
+ "output_type": "execute_result"
336
+ }
337
+ ],
338
+ "source": [
339
+ "test"
340
+ ]
341
+ },
342
+ {
343
+ "cell_type": "code",
344
+ "execution_count": 10,
345
+ "metadata": {},
346
+ "outputs": [
347
+ {
348
+ "data": {
349
+ "text/plain": [
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+ "title": {
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+ "x": 0.05
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+ },
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+ "xaxis": {
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+ "automargin": true,
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+ "gridcolor": "rgb(232,232,232)",
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+ "linecolor": "rgb(36,36,36)",
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+ "showgrid": false,
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+ "showline": true,
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+ "ticks": "outside",
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+ "title": {
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+ "standoff": 15
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+ },
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+ "zerolinecolor": "rgb(36,36,36)"
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+ },
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+ "yaxis": {
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+ "gridcolor": "rgb(232,232,232)",
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+ "linecolor": "rgb(36,36,36)",
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+ "showgrid": false,
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+ "showline": true,
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+ "ticks": "outside",
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+ "title": {
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+ "standoff": 15
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+ "zerolinecolor": "rgb(36,36,36)"
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+ }
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+ }
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+ },
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+ "title": {
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+ "text": "Holt Winters Forecast",
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+ "x": 0.5
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+ },
1639
+ "width": 700,
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+ "xaxis": {
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+ "title": {
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+ "text": "Date"
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+ }
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+ },
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+ "yaxis": {
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+ "title": {
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+ "text": "Passenger Volume"
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+ }
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+ }
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+ }
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+ }
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ }
1656
+ ],
1657
+ "source": [
1658
+ "# Plot the forecasts\n",
1659
+ "plot_func(forecasts, \"Holt Winters Forecast\")"
1660
+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 3,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ " Month #Passengers fittedvalues residuals\n",
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+ "0 1949-01-01 112 111.997011 0.002989\n",
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+ "1 1949-02-01 118 119.397772 -1.397772\n",
1674
+ "2 1949-03-01 132 132.485694 -0.485694\n",
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+ "3 1949-04-01 129 126.790028 2.209972\n",
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+ "4 1949-05-01 121 118.553190 2.446810\n",
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+ ".. ... ... ... ...\n",
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+ "111 1958-04-01 348 370.894734 -22.894734\n",
1679
+ "112 1958-05-01 363 370.984509 -7.984509\n",
1680
+ "113 1958-06-01 435 435.713085 -0.713085\n",
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+ "114 1958-07-01 491 479.724073 11.275927\n",
1682
+ "115 1958-08-01 505 479.483081 25.516919\n",
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+ "\n",
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+ "[116 rows x 4 columns]\n"
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+ ]
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+ },
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
1691
+ "/tmp/ipykernel_68203/2996281503.py:2: SettingWithCopyWarning:\n",
1692
+ "\n",
1693
+ "\n",
1694
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
1695
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
1696
+ "\n",
1697
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
1698
+ "\n",
1699
+ "/tmp/ipykernel_68203/2996281503.py:3: SettingWithCopyWarning:\n",
1700
+ "\n",
1701
+ "\n",
1702
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
1703
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
1704
+ "\n",
1705
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
1706
+ "\n"
1707
+ ]
1708
+ }
1709
+ ],
1710
+ "source": [
1711
+ "# Appending residuals and fitted values to the train dataframe\n",
1712
+ "train[\"fittedvalues\"] = model.fittedvalues\n",
1713
+ "train[\"residuals\"] = model.resid\n",
1714
+ "print(train)"
1715
+ ]
1716
+ },
1717
+ {
1718
+ "cell_type": "code",
1719
+ "execution_count": 4,
1720
+ "metadata": {},
1721
+ "outputs": [
1722
+ {
1723
+ "data": {
1724
+ "image/png": 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",
1725
+ "text/plain": [
1726
+ "<Figure size 800x300 with 2 Axes>"
1727
+ ]
1728
+ },
1729
+ "metadata": {},
1730
+ "output_type": "display_data"
1731
+ }
1732
+ ],
1733
+ "source": [
1734
+ "# Import packages\n",
1735
+ "from statsmodels.tsa.holtwinters import ExponentialSmoothing\n",
1736
+ "from statsmodels.graphics.tsaplots import plot_pacf, plot_acf\n",
1737
+ "import matplotlib.pyplot as plt\n",
1738
+ "\n",
1739
+ "# Plot ACF and PACF\n",
1740
+ "fig, ax = plt.subplots(1, 2, figsize=(8, 3))\n",
1741
+ "plot_acf(train[\"residuals\"], lags=30, ax=ax[0])\n",
1742
+ "ax[0].set_xlabel(\"Lags\")\n",
1743
+ "plot_pacf(train[\"residuals\"], lags=30, ax=ax[1])\n",
1744
+ "ax[1].set_xlabel(\"Lags\")\n",
1745
+ "plt.tight_layout()\n",
1746
+ "plt.show()"
1747
+ ]
1748
+ },
1749
+ {
1750
+ "cell_type": "code",
1751
+ "execution_count": 5,
1752
+ "metadata": {},
1753
+ "outputs": [
1754
+ {
1755
+ "name": "stdout",
1756
+ "output_type": "stream",
1757
+ "text": [
1758
+ " lb_stat lb_pvalue\n",
1759
+ "1 13.917146 0.000191\n",
1760
+ "2 16.931975 0.000211\n",
1761
+ "3 18.861072 0.000292\n",
1762
+ "4 22.061208 0.000195\n",
1763
+ "5 23.398389 0.000283\n",
1764
+ "6 24.627916 0.000400\n",
1765
+ "7 27.059270 0.000325\n",
1766
+ "8 29.031125 0.000313\n",
1767
+ "9 29.344194 0.000567\n",
1768
+ "10 30.414797 0.000733\n"
1769
+ ]
1770
+ }
1771
+ ],
1772
+ "source": [
1773
+ "# Import packages\n",
1774
+ "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
1775
+ "\n",
1776
+ "# Carry out Ljung-Box test\n",
1777
+ "print(acorr_ljungbox(train[\"residuals\"], return_df=True))"
1778
+ ]
1779
+ },
1780
+ {
1781
+ "cell_type": "code",
1782
+ "execution_count": 6,
1783
+ "metadata": {},
1784
+ "outputs": [
1785
+ {
1786
+ "data": {
1787
+ "application/vnd.plotly.v1+json": {
1788
+ "config": {
1789
+ "plotlyServerURL": "https://plot.ly"
1790
+ },
1791
+ "data": [
1792
+ {
1793
+ "alignmentgroup": "True",
1794
+ "bingroup": "x",
1795
+ "hovertemplate": "residuals=%{x}<br>count=%{y}<extra></extra>",
1796
+ "legendgroup": "",
1797
+ "marker": {
1798
+ "color": "#636efa",
1799
+ "pattern": {
1800
+ "shape": ""
1801
+ }
1802
+ },
1803
+ "name": "",
1804
+ "offsetgroup": "",
1805
+ "orientation": "v",
1806
+ "showlegend": false,
1807
+ "type": "histogram",
1808
+ "x": [
1809
+ 0.0029885509041491787,
1810
+ -1.3977715272270643,
1811
+ -0.48569421554674364,
1812
+ 2.2099717953831686,
1813
+ 2.446809977007419,
1814
+ 2.3144994254786297,
1815
+ 0.28556472864667626,
1816
+ 0.40306903295868324,
1817
+ 0.18403920366532134,
1818
+ -0.04208236753810013,
1819
+ -2.5501023923200705,
1820
+ -6.29759906988788,
1821
+ -10.229186708520444,
1822
+ -2.368196190757004,
1823
+ -1.774909044075656,
1824
+ -2.944427246017426,
1825
+ -2.3164716464191315,
1826
+ 8.894391720365405,
1827
+ 12.773425732045155,
1828
+ 8.021542496626495,
1829
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+ "showgrid": false,
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+ },
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+ "zerolinecolor": "rgb(36,36,36)"
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+ }
2799
+ }
2800
+ },
2801
+ "title": {
2802
+ "text": "Distribution of Residuals",
2803
+ "x": 0.5
2804
+ },
2805
+ "width": 700,
2806
+ "xaxis": {
2807
+ "anchor": "y",
2808
+ "domain": [
2809
+ 0,
2810
+ 1
2811
+ ],
2812
+ "title": {
2813
+ "text": "Residuals"
2814
+ }
2815
+ },
2816
+ "yaxis": {
2817
+ "anchor": "x",
2818
+ "domain": [
2819
+ 0,
2820
+ 1
2821
+ ],
2822
+ "title": {
2823
+ "text": "Count"
2824
+ }
2825
+ }
2826
+ }
2827
+ }
2828
+ },
2829
+ "metadata": {},
2830
+ "output_type": "display_data"
2831
+ },
2832
+ {
2833
+ "name": "stdout",
2834
+ "output_type": "stream",
2835
+ "text": [
2836
+ "-0.023048689329401923\n"
2837
+ ]
2838
+ }
2839
+ ],
2840
+ "source": [
2841
+ "# Import plotly\n",
2842
+ "import plotly.graph_objects as go\n",
2843
+ "\n",
2844
+ "# Plot histogram of the residuals\n",
2845
+ "fig = px.histogram(train, x=\"residuals\")\n",
2846
+ "fig.update_layout(\n",
2847
+ " template=\"simple_white\",\n",
2848
+ " font=dict(size=18),\n",
2849
+ " title_text=\"Distribution of Residuals\",\n",
2850
+ " width=700,\n",
2851
+ " title_x=0.5,\n",
2852
+ " height=400,\n",
2853
+ " xaxis_title=\"Residuals\",\n",
2854
+ " yaxis_title=\"Count\",\n",
2855
+ ")\n",
2856
+ "fig.show()\n",
2857
+ "\n",
2858
+ "# Mean of residuals\n",
2859
+ "print(train[\"residuals\"].mean())"
2860
+ ]
2861
+ },
2862
+ {
2863
+ "cell_type": "code",
2864
+ "execution_count": null,
2865
+ "metadata": {},
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+ "outputs": [],
2867
+ "source": []
2868
+ },
2869
+ {
2870
+ "cell_type": "code",
2871
+ "execution_count": null,
2872
+ "metadata": {},
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+ "outputs": [],
2874
+ "source": []
2875
+ }
2876
+ ],
2877
+ "metadata": {
2878
+ "kernelspec": {
2879
+ "display_name": "py311-kfp240-airflow251",
2880
+ "language": "python",
2881
+ "name": "python3"
2882
+ },
2883
+ "language_info": {
2884
+ "codemirror_mode": {
2885
+ "name": "ipython",
2886
+ "version": 3
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+ },
2888
+ "file_extension": ".py",
2889
+ "mimetype": "text/x-python",
2890
+ "name": "python",
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+ "nbconvert_exporter": "python",
2892
+ "pygments_lexer": "ipython3",
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+ "version": "3.11.5"
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+ }
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+ },
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+ "nbformat": 4,
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+ "nbformat_minor": 2
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+ }
stationary.ipynb CHANGED
@@ -9467,7 +9467,7 @@
9467
  "name": "python",
9468
  "nbconvert_exporter": "python",
9469
  "pygments_lexer": "ipython3",
9470
- "version": "3.12.1"
9471
  }
9472
  },
9473
  "nbformat": 4,
 
9467
  "name": "python",
9468
  "nbconvert_exporter": "python",
9469
  "pygments_lexer": "ipython3",
9470
+ "version": "3.11.5"
9471
  }
9472
  },
9473
  "nbformat": 4,