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ibnummuhammad
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coal-price-forecast

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•
1 Parent(s): bc7c2f9

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Files changed (5) hide show
  1. autoregression.ipynb +52 -34
  2. box_cox.ipynb +2721 -0
  3. coal-price-forecast.ipynb +11 -4
  4. cross_validation.ipynb +109 -80
  5. multiple_timeseries_forecast.ipynb +0 -0
autoregression.ipynb CHANGED
@@ -1231,23 +1231,28 @@
1231
  "import pandas as pd\n",
1232
  "\n",
1233
  "# Read in the data\n",
1234
- "data = pd.read_csv('../coal-price-data/AirPassengers.csv')\n",
1235
- "data['Month'] = pd.to_datetime(data['Month'])\n",
1236
  "\n",
1237
  "\n",
1238
- "def plot_passenger_volumes(df: pd.DataFrame,\n",
1239
- " y: str) -> None:\n",
1240
  " \"\"\"General function to plot the passenger data.\"\"\"\n",
1241
  "\n",
1242
- " fig = px.line(df, x='Month', y=y, labels={'Month': 'Date'})\n",
1243
- " fig.update_layout(template=\"simple_white\", font=dict(size=18), title_text='Airline Passengers',\n",
1244
- " width=650, title_x=0.5, height=400)\n",
 
 
 
 
 
 
1245
  "\n",
1246
  " return fig.show()\n",
1247
  "\n",
1248
  "\n",
1249
  "# Plot the airline passenger data\n",
1250
- "plot_passenger_volumes(df=data, y='#Passengers')"
1251
  ]
1252
  },
1253
  {
@@ -2478,12 +2483,12 @@
2478
  "from scipy.stats import boxcox\n",
2479
  "\n",
2480
  "# Make the target stationary\n",
2481
- "data['Passengers_boxcox'], lam = boxcox(data['#Passengers'])\n",
2482
  "data[\"Passenger_stationary\"] = data[\"Passengers_boxcox\"].diff()\n",
2483
  "data.dropna(inplace=True)\n",
2484
  "\n",
2485
  "# Plot the stationary airline passenger data\n",
2486
- "plot_passenger_volumes(df=data, y='Passenger_stationary')"
2487
  ]
2488
  },
2489
  {
@@ -2509,15 +2514,16 @@
2509
  "# Import packages\n",
2510
  "from statsmodels.tsa.stattools import adfuller\n",
2511
  "\n",
 
2512
  "# ADF test for stationary\n",
2513
  "def adf_test(series):\n",
2514
  " \"\"\"Using an ADF test to determine if a series is stationary\"\"\"\n",
2515
  " test_results = adfuller(series)\n",
2516
- " print('ADF Statistic: ', test_results[0])\n",
2517
- " print('P-Value: ', test_results[1])\n",
2518
- " print('Critical Values:')\n",
2519
  " for threshold, adf_stat in test_results[4].items():\n",
2520
- " print('\\t%s: %.2f' % (threshold, adf_stat))\n",
2521
  "\n",
2522
  "\n",
2523
  "print(adf_test(data[\"Passenger_stationary\"]))"
@@ -2686,13 +2692,13 @@
2686
  "from statsmodels.graphics.tsaplots import plot_pacf\n",
2687
  "\n",
2688
  "# Plot partial autocorrelation\n",
2689
- "plt.rc(\"figure\", figsize=(11,5))\n",
2690
- "plot_pacf(data['Passenger_stationary'], method='ywm')\n",
2691
- "plt.xlabel('Lags', fontsize=18)\n",
2692
- "plt.ylabel('Correlation', fontsize=18)\n",
2693
  "plt.xticks(fontsize=18)\n",
2694
  "plt.yticks(fontsize=18)\n",
2695
- "plt.title('Partial Autocorrelation Plot', fontsize=20)\n",
2696
  "plt.tight_layout()\n",
2697
  "plt.show()"
2698
  ]
@@ -2726,12 +2732,12 @@
2726
  "from statsmodels.tsa.ar_model import AutoReg, ar_select_order\n",
2727
  "\n",
2728
  "# Split train and test\n",
2729
- "train = data.iloc[:-int(len(data) * 0.2)]\n",
2730
- "test = data.iloc[-int(len(data) * 0.2):]\n",
2731
  "\n",
2732
  "# Build AR model\n",
2733
- "selector = ar_select_order(train['Passenger_stationary'], 15)\n",
2734
- "model = AutoReg(train['Passenger_stationary'], lags=selector.ar_lags).fit()"
2735
  ]
2736
  },
2737
  {
@@ -4023,31 +4029,43 @@
4023
  "boxcox_forecasts = []\n",
4024
  "for idx in range(len(test)):\n",
4025
  " if idx == 0:\n",
4026
- " boxcox_forecast = transformed_forecasts[idx] + train['Passengers_boxcox'].iloc[-1]\n",
 
 
4027
  " else:\n",
4028
- " boxcox_forecast = transformed_forecasts[idx] + boxcox_forecasts[idx-1]\n",
4029
  "\n",
4030
  " boxcox_forecasts.append(boxcox_forecast)\n",
4031
  "\n",
4032
  "forecasts = inv_boxcox(boxcox_forecasts, lam)\n",
4033
  "\n",
4034
  "\n",
4035
- "def plot_forecasts(forecasts: list[float],\n",
4036
- " title: str) -> None:\n",
4037
  " \"\"\"Function to plot the forecasts.\"\"\"\n",
4038
  " fig = go.Figure()\n",
4039
- " fig.add_trace(go.Scatter(x=train['Month'], y=train['#Passengers'], name='Train'))\n",
4040
- " fig.add_trace(go.Scatter(x=test['Month'], y=test['#Passengers'], name='Test'))\n",
4041
- " fig.add_trace(go.Scatter(x=test['Month'], y=forecasts, name='Forecast'))\n",
4042
- " fig.update_layout(template=\"simple_white\", font=dict(size=18), title_text=title,\n",
4043
- " width=650, title_x=0.5, height=400, xaxis_title='Date',\n",
4044
- " yaxis_title='Passenger Volume')\n",
 
 
 
 
 
 
 
 
 
 
 
4045
  "\n",
4046
  " return fig.show()\n",
4047
  "\n",
4048
  "\n",
4049
  "# Plot the forecasts\n",
4050
- "plot_forecasts(forecasts, 'Autoregression')"
4051
  ]
4052
  },
4053
  {
 
1231
  "import pandas as pd\n",
1232
  "\n",
1233
  "# Read in the data\n",
1234
+ "data = pd.read_csv(\"../coal-price-data/AirPassengers.csv\")\n",
1235
+ "data[\"Month\"] = pd.to_datetime(data[\"Month\"])\n",
1236
  "\n",
1237
  "\n",
1238
+ "def plot_passenger_volumes(df: pd.DataFrame, y: str) -> None:\n",
 
1239
  " \"\"\"General function to plot the passenger data.\"\"\"\n",
1240
  "\n",
1241
+ " fig = px.line(df, x=\"Month\", y=y, labels={\"Month\": \"Date\"})\n",
1242
+ " fig.update_layout(\n",
1243
+ " template=\"simple_white\",\n",
1244
+ " font=dict(size=18),\n",
1245
+ " title_text=\"Airline Passengers\",\n",
1246
+ " width=650,\n",
1247
+ " title_x=0.5,\n",
1248
+ " height=400,\n",
1249
+ " )\n",
1250
  "\n",
1251
  " return fig.show()\n",
1252
  "\n",
1253
  "\n",
1254
  "# Plot the airline passenger data\n",
1255
+ "plot_passenger_volumes(df=data, y=\"#Passengers\")"
1256
  ]
1257
  },
1258
  {
 
2483
  "from scipy.stats import boxcox\n",
2484
  "\n",
2485
  "# Make the target stationary\n",
2486
+ "data[\"Passengers_boxcox\"], lam = boxcox(data[\"#Passengers\"])\n",
2487
  "data[\"Passenger_stationary\"] = data[\"Passengers_boxcox\"].diff()\n",
2488
  "data.dropna(inplace=True)\n",
2489
  "\n",
2490
  "# Plot the stationary airline passenger data\n",
2491
+ "plot_passenger_volumes(df=data, y=\"Passenger_stationary\")"
2492
  ]
2493
  },
2494
  {
 
2514
  "# Import packages\n",
2515
  "from statsmodels.tsa.stattools import adfuller\n",
2516
  "\n",
2517
+ "\n",
2518
  "# ADF test for stationary\n",
2519
  "def adf_test(series):\n",
2520
  " \"\"\"Using an ADF test to determine if a series is stationary\"\"\"\n",
2521
  " test_results = adfuller(series)\n",
2522
+ " print(\"ADF Statistic: \", test_results[0])\n",
2523
+ " print(\"P-Value: \", test_results[1])\n",
2524
+ " print(\"Critical Values:\")\n",
2525
  " for threshold, adf_stat in test_results[4].items():\n",
2526
+ " print(\"\\t%s: %.2f\" % (threshold, adf_stat))\n",
2527
  "\n",
2528
  "\n",
2529
  "print(adf_test(data[\"Passenger_stationary\"]))"
 
2692
  "from statsmodels.graphics.tsaplots import plot_pacf\n",
2693
  "\n",
2694
  "# Plot partial autocorrelation\n",
2695
+ "plt.rc(\"figure\", figsize=(11, 5))\n",
2696
+ "plot_pacf(data[\"Passenger_stationary\"], method=\"ywm\")\n",
2697
+ "plt.xlabel(\"Lags\", fontsize=18)\n",
2698
+ "plt.ylabel(\"Correlation\", fontsize=18)\n",
2699
  "plt.xticks(fontsize=18)\n",
2700
  "plt.yticks(fontsize=18)\n",
2701
+ "plt.title(\"Partial Autocorrelation Plot\", fontsize=20)\n",
2702
  "plt.tight_layout()\n",
2703
  "plt.show()"
2704
  ]
 
2732
  "from statsmodels.tsa.ar_model import AutoReg, ar_select_order\n",
2733
  "\n",
2734
  "# Split train and test\n",
2735
+ "train = data.iloc[: -int(len(data) * 0.2)]\n",
2736
+ "test = data.iloc[-int(len(data) * 0.2) :]\n",
2737
  "\n",
2738
  "# Build AR model\n",
2739
+ "selector = ar_select_order(train[\"Passenger_stationary\"], 15)\n",
2740
+ "model = AutoReg(train[\"Passenger_stationary\"], lags=selector.ar_lags).fit()"
2741
  ]
2742
  },
2743
  {
 
4029
  "boxcox_forecasts = []\n",
4030
  "for idx in range(len(test)):\n",
4031
  " if idx == 0:\n",
4032
+ " boxcox_forecast = (\n",
4033
+ " transformed_forecasts[idx] + train[\"Passengers_boxcox\"].iloc[-1]\n",
4034
+ " )\n",
4035
  " else:\n",
4036
+ " boxcox_forecast = transformed_forecasts[idx] + boxcox_forecasts[idx - 1]\n",
4037
  "\n",
4038
  " boxcox_forecasts.append(boxcox_forecast)\n",
4039
  "\n",
4040
  "forecasts = inv_boxcox(boxcox_forecasts, lam)\n",
4041
  "\n",
4042
  "\n",
4043
+ "def plot_forecasts(forecasts: list[float], title: str) -> None:\n",
 
4044
  " \"\"\"Function to plot the forecasts.\"\"\"\n",
4045
  " fig = go.Figure()\n",
4046
+ " fig.add_trace(\n",
4047
+ " go.Scatter(x=train[\"Month\"], y=train[\"#Passengers\"], name=\"Train\")\n",
4048
+ " )\n",
4049
+ " fig.add_trace(\n",
4050
+ " go.Scatter(x=test[\"Month\"], y=test[\"#Passengers\"], name=\"Test\")\n",
4051
+ " )\n",
4052
+ " fig.add_trace(go.Scatter(x=test[\"Month\"], y=forecasts, name=\"Forecast\"))\n",
4053
+ " fig.update_layout(\n",
4054
+ " template=\"simple_white\",\n",
4055
+ " font=dict(size=18),\n",
4056
+ " title_text=title,\n",
4057
+ " width=650,\n",
4058
+ " title_x=0.5,\n",
4059
+ " height=400,\n",
4060
+ " xaxis_title=\"Date\",\n",
4061
+ " yaxis_title=\"Passenger Volume\",\n",
4062
+ " )\n",
4063
  "\n",
4064
  " return fig.show()\n",
4065
  "\n",
4066
  "\n",
4067
  "# Plot the forecasts\n",
4068
+ "plot_forecasts(forecasts, \"Autoregression\")"
4069
  ]
4070
  },
4071
  {
box_cox.ipynb ADDED
@@ -0,0 +1,2721 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 8,
6
+ "metadata": {},
7
+ "outputs": [],
8
+ "source": [
9
+ "# Import packages\n",
10
+ "import plotly.express as px\n",
11
+ "import pandas as pd\n",
12
+ "# Import the transform\n",
13
+ "from scipy.stats import boxcox"
14
+ ]
15
+ },
16
+ {
17
+ "cell_type": "code",
18
+ "execution_count": 9,
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": 2,
29
+ "metadata": {},
30
+ "outputs": [
31
+ {
32
+ "data": {
33
+ "text/html": [
34
+ "<div>\n",
35
+ "<style scoped>\n",
36
+ " .dataframe tbody tr th:only-of-type {\n",
37
+ " vertical-align: middle;\n",
38
+ " }\n",
39
+ "\n",
40
+ " .dataframe tbody tr th {\n",
41
+ " vertical-align: top;\n",
42
+ " }\n",
43
+ "\n",
44
+ " .dataframe thead th {\n",
45
+ " text-align: right;\n",
46
+ " }\n",
47
+ "</style>\n",
48
+ "<table border=\"1\" class=\"dataframe\">\n",
49
+ " <thead>\n",
50
+ " <tr style=\"text-align: right;\">\n",
51
+ " <th></th>\n",
52
+ " <th>Month</th>\n",
53
+ " <th>#Passengers</th>\n",
54
+ " </tr>\n",
55
+ " </thead>\n",
56
+ " <tbody>\n",
57
+ " <tr>\n",
58
+ " <th>0</th>\n",
59
+ " <td>1949-01</td>\n",
60
+ " <td>112</td>\n",
61
+ " </tr>\n",
62
+ " <tr>\n",
63
+ " <th>1</th>\n",
64
+ " <td>1949-02</td>\n",
65
+ " <td>118</td>\n",
66
+ " </tr>\n",
67
+ " <tr>\n",
68
+ " <th>2</th>\n",
69
+ " <td>1949-03</td>\n",
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+ " <td>132</td>\n",
71
+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>3</th>\n",
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+ " <td>1949-04</td>\n",
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+ " <td>129</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>4</th>\n",
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+ " <td>1949-05</td>\n",
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+ " <td>121</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>...</th>\n",
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+ " <td>...</td>\n",
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+ " <td>...</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>139</th>\n",
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+ " <td>1960-08</td>\n",
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+ " <td>606</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>140</th>\n",
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+ " <td>1960-09</td>\n",
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+ " <td>508</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>141</th>\n",
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+ " <td>1960-10</td>\n",
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+ " <td>461</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>142</th>\n",
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+ " <td>1960-11</td>\n",
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+ " <td>390</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>143</th>\n",
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+ " <td>1960-12</td>\n",
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+ " <td>432</td>\n",
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+ " </tr>\n",
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+ " </tbody>\n",
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+ "</table>\n",
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+ "<p>144 rows × 2 columns</p>\n",
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+ "</div>"
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+ ],
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+ "text/plain": [
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+ " Month #Passengers\n",
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+ "0 1949-01 112\n",
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+ "1 1949-02 118\n",
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+ "3 1949-04 129\n",
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+ "4 1949-05 121\n",
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+ ".. ... ...\n",
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+ "139 1960-08 606\n",
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+ "140 1960-09 508\n",
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+ "141 1960-10 461\n",
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+ "142 1960-11 390\n",
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+ "\n",
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+ "[144 rows x 2 columns]"
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+ ]
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+ },
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+ "execution_count": 2,
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+ "output_type": "execute_result"
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+ ],
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+ "source": [
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+ "data"
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+ ]
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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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+ "data": {
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+ "text/plain": [
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+ "0 1949-01\n",
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+ "1 1949-02\n",
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+ "2 1949-03\n",
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+ "3 1949-04\n",
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+ "4 1949-05\n",
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+ " ... \n",
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+ "139 1960-08\n",
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+ "140 1960-09\n",
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+ "141 1960-10\n",
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+ "142 1960-11\n",
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+ "143 1960-12\n",
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+ "Name: Month, Length: 144, dtype: object"
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+ ]
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+ },
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+ "cell_type": "code",
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+ "execution_count": 4,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "# def plotting(title, data, x, y, x_label, y_label, text=False, lam=None):\n",
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+ "title=\"Airline Passengers\"\n",
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+ "data=data\n",
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+ "x=\"Month\"\n",
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+ "y=\"#Passengers\"\n",
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+ "x_label=\"Date\"\n",
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+ "y_label=\"Passengers\"\n",
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+ "text=False\n",
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+ "lam=None"
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 5,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "\"\"\"General function to plot the passenger data.\"\"\"\n",
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+ "fig = px.line(data, x=data[x], y=data[y], labels={x: x_label, y: y_label})"
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+ ]
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+ },
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+ "cell_type": "code",
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+ "gridcolor": "rgb(232,232,232)",
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+ "gridwidth": 2,
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2496
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2497
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+ "gridwidth": 2,
2500
+ "linecolor": "rgb(36,36,36)",
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2502
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2504
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2506
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+ }
2508
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+ "shapedefaults": {
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2511
+ "line": {
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+ "width": 0
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+ "opacity": 0.3
2515
+ },
2516
+ "ternary": {
2517
+ "aaxis": {
2518
+ "gridcolor": "rgb(232,232,232)",
2519
+ "linecolor": "rgb(36,36,36)",
2520
+ "showgrid": false,
2521
+ "showline": true,
2522
+ "ticks": "outside"
2523
+ },
2524
+ "baxis": {
2525
+ "gridcolor": "rgb(232,232,232)",
2526
+ "linecolor": "rgb(36,36,36)",
2527
+ "showgrid": false,
2528
+ "showline": true,
2529
+ "ticks": "outside"
2530
+ },
2531
+ "bgcolor": "white",
2532
+ "caxis": {
2533
+ "gridcolor": "rgb(232,232,232)",
2534
+ "linecolor": "rgb(36,36,36)",
2535
+ "showgrid": false,
2536
+ "showline": true,
2537
+ "ticks": "outside"
2538
+ }
2539
+ },
2540
+ "title": {
2541
+ "x": 0.05
2542
+ },
2543
+ "xaxis": {
2544
+ "automargin": true,
2545
+ "gridcolor": "rgb(232,232,232)",
2546
+ "linecolor": "rgb(36,36,36)",
2547
+ "showgrid": false,
2548
+ "showline": true,
2549
+ "ticks": "outside",
2550
+ "title": {
2551
+ "standoff": 15
2552
+ },
2553
+ "zeroline": false,
2554
+ "zerolinecolor": "rgb(36,36,36)"
2555
+ },
2556
+ "yaxis": {
2557
+ "automargin": true,
2558
+ "gridcolor": "rgb(232,232,232)",
2559
+ "linecolor": "rgb(36,36,36)",
2560
+ "showgrid": false,
2561
+ "showline": true,
2562
+ "ticks": "outside",
2563
+ "title": {
2564
+ "standoff": 15
2565
+ },
2566
+ "zeroline": false,
2567
+ "zerolinecolor": "rgb(36,36,36)"
2568
+ }
2569
+ }
2570
+ },
2571
+ "title": {
2572
+ "text": "Airline Passengers",
2573
+ "x": 0.5
2574
+ },
2575
+ "width": 650,
2576
+ "xaxis": {
2577
+ "anchor": "y",
2578
+ "domain": [
2579
+ 0,
2580
+ 1
2581
+ ],
2582
+ "title": {
2583
+ "text": "Date"
2584
+ }
2585
+ },
2586
+ "yaxis": {
2587
+ "anchor": "x",
2588
+ "domain": [
2589
+ 0,
2590
+ 1
2591
+ ],
2592
+ "title": {
2593
+ "text": "Passengers"
2594
+ }
2595
+ }
2596
+ }
2597
+ }
2598
+ },
2599
+ "metadata": {},
2600
+ "output_type": "display_data"
2601
+ }
2602
+ ],
2603
+ "source": [
2604
+ "fig.update_layout(\n",
2605
+ " template=\"simple_white\",\n",
2606
+ " font=dict(size=18),\n",
2607
+ " title_text=title,\n",
2608
+ " width=650,\n",
2609
+ " title_x=0.5,\n",
2610
+ " height=400,\n",
2611
+ ")\n",
2612
+ "\n",
2613
+ "if text:\n",
2614
+ " fig.add_annotation(\n",
2615
+ " x=\"1952-12-20\",\n",
2616
+ " y=10,\n",
2617
+ " text=f\"Lambda = {lam:.3f}\",\n",
2618
+ " align=\"left\",\n",
2619
+ " yanchor=\"bottom\",\n",
2620
+ " showarrow=False,\n",
2621
+ " font=dict(size=20, color=\"black\", family=\"Courier New, monospace\"),\n",
2622
+ " bordercolor=\"black\",\n",
2623
+ " borderwidth=2,\n",
2624
+ " bgcolor=\"white\",\n",
2625
+ " )\n",
2626
+ "\n",
2627
+ "fig.show()"
2628
+ ]
2629
+ },
2630
+ {
2631
+ "cell_type": "code",
2632
+ "execution_count": 10,
2633
+ "metadata": {},
2634
+ "outputs": [
2635
+ {
2636
+ "data": {
2637
+ "text/plain": [
2638
+ "(array([ 6.82748858, 6.93282073, 7.16188991, 7.1146092 , 6.98378534,\n",
2639
+ " 7.20826381, 7.39959625, 7.39959625, 7.22352672, 6.94993037,\n",
2640
+ " 6.67929971, 6.93282073, 6.88073999, 7.06638224, 7.29843681,\n",
2641
+ " 7.20826381, 7.0500891 , 7.41371315, 7.69297573, 7.69297573,\n",
2642
+ " 7.5372583 , 7.17744676, 6.86312241, 7.28363791, 7.3567524 ,\n",
2643
+ " 7.42774956, 7.79166114, 7.60332502, 7.71801212, 7.79166114,\n",
2644
+ " 8.03379761, 8.03379761, 7.86322462, 7.59025116, 7.37111692,\n",
2645
+ " 7.64214072, 7.70552511, 7.81574099, 7.96692819, 7.82769554,\n",
2646
+ " 7.85143679, 8.23478318, 8.35415586, 8.46833523, 8.14152245,\n",
2647
+ " 7.94424459, 7.71801212, 7.97819498, 8.00058091, 8.00058091,\n",
2648
+ " 8.41186391, 8.40233336, 8.34441344, 8.47763088, 8.66568394,\n",
2649
+ " 8.73398059, 8.42136011, 8.16253865, 7.81574099, 8.05570584,\n",
2650
+ " 8.08822246, 7.9098368 , 8.40233336, 8.32481936, 8.3927682 ,\n",
2651
+ " 8.66568394, 8.9757346 , 8.90544135, 8.62209773, 8.34441344,\n",
2652
+ " 8.07742912, 8.34441344, 8.46833523, 8.38316815, 8.69149921,\n",
2653
+ " 8.70857243, 8.71706852, 9.07418213, 9.41661368, 9.30252135,\n",
2654
+ " 9.05177502, 8.75078704, 8.42136011, 8.78408875, 8.83328384,\n",
2655
+ " 8.77580178, 9.08901941, 9.05926438, 9.09640816, 9.48162253,\n",
2656
+ " 9.72178824, 9.67414826, 9.35679145, 9.00640452, 8.72553785,\n",
2657
+ " 9.00640452, 9.07418213, 8.96801306, 9.36350176, 9.30936305,\n",
2658
+ " 9.35679145, 9.77445246, 10.01358765, 10.02424443, 9.66813702,\n",
2659
+ " 9.30252135, 8.99876921, 9.22613239, 9.25415342, 9.09640816,\n",
2660
+ " 9.40342965, 9.30936305, 9.41002941, 9.84885828, 10.1491833 ,\n",
2661
+ " 10.21968053, 9.66813702, 9.38353677, 9.03673475, 9.23316418,\n",
2662
+ " 9.39018342, 9.26805875, 9.68014687, 9.61958525, 9.76283258,\n",
2663
+ " 10.05071724, 10.4262609 , 10.47688178, 10.00289175, 9.68613291,\n",
2664
+ " 9.40342965, 9.67414826, 9.74531406, 9.58881435, 9.75700495,\n",
2665
+ " 9.99215641, 10.05071724, 10.36530783, 10.75144927, 10.68404571,\n",
2666
+ " 10.23457009, 9.99215641, 9.58261997, 9.83185756]),\n",
2667
+ " 0.14802256973464573)"
2668
+ ]
2669
+ },
2670
+ "execution_count": 10,
2671
+ "metadata": {},
2672
+ "output_type": "execute_result"
2673
+ }
2674
+ ],
2675
+ "source": [
2676
+ "boxcox(data['#Passengers'])"
2677
+ ]
2678
+ },
2679
+ {
2680
+ "cell_type": "code",
2681
+ "execution_count": 11,
2682
+ "metadata": {},
2683
+ "outputs": [],
2684
+ "source": [
2685
+ "# Apply box-cox transform and plot it\n",
2686
+ "data['Passengers_box_cox'], lam = boxcox(data['#Passengers'])"
2687
+ ]
2688
+ },
2689
+ {
2690
+ "cell_type": "code",
2691
+ "execution_count": null,
2692
+ "metadata": {},
2693
+ "outputs": [],
2694
+ "source": [
2695
+ "plotting(title='Airline Passengers', data=data, x='Month', y='Passengers_box_cox',\n",
2696
+ " x_label='Date', y_label='Passengers<br>Box-Cox Transform', text=True, lam=lam)"
2697
+ ]
2698
+ }
2699
+ ],
2700
+ "metadata": {
2701
+ "kernelspec": {
2702
+ "display_name": "py311-kfp240-airflow251",
2703
+ "language": "python",
2704
+ "name": "python3"
2705
+ },
2706
+ "language_info": {
2707
+ "codemirror_mode": {
2708
+ "name": "ipython",
2709
+ "version": 3
2710
+ },
2711
+ "file_extension": ".py",
2712
+ "mimetype": "text/x-python",
2713
+ "name": "python",
2714
+ "nbconvert_exporter": "python",
2715
+ "pygments_lexer": "ipython3",
2716
+ "version": "3.11.5"
2717
+ }
2718
+ },
2719
+ "nbformat": 4,
2720
+ "nbformat_minor": 2
2721
+ }
coal-price-forecast.ipynb CHANGED
@@ -13,7 +13,7 @@
13
  },
14
  {
15
  "cell_type": "code",
16
- "execution_count": 2,
17
  "metadata": {},
18
  "outputs": [
19
  {
@@ -155,16 +155,23 @@
155
  "[145 rows x 5 columns]"
156
  ]
157
  },
158
- "execution_count": 2,
159
  "metadata": {},
160
  "output_type": "execute_result"
161
  }
162
  ],
163
  "source": [
164
- "df = pd.read_csv(\"../coal-price-data/coal_price_data.csv\")\n",
165
- "df"
166
  ]
167
  },
 
 
 
 
 
 
 
168
  {
169
  "cell_type": "code",
170
  "execution_count": 5,
 
13
  },
14
  {
15
  "cell_type": "code",
16
+ "execution_count": 3,
17
  "metadata": {},
18
  "outputs": [
19
  {
 
155
  "[145 rows x 5 columns]"
156
  ]
157
  },
158
+ "execution_count": 3,
159
  "metadata": {},
160
  "output_type": "execute_result"
161
  }
162
  ],
163
  "source": [
164
+ "df_coal = pd.read_csv(\"../coal-price-data/coal_price_data.csv\")\n",
165
+ "df_coal"
166
  ]
167
  },
168
+ {
169
+ "cell_type": "code",
170
+ "execution_count": null,
171
+ "metadata": {},
172
+ "outputs": [],
173
+ "source": []
174
+ },
175
  {
176
  "cell_type": "code",
177
  "execution_count": 5,
cross_validation.ipynb CHANGED
@@ -2474,11 +2474,9 @@
2474
  "from sklearn.model_selection import KFold\n",
2475
  "\n",
2476
  "\n",
2477
- "def plot_cross_val(n_splits: int,\n",
2478
- " splitter_func,\n",
2479
- " df: pd.DataFrame,\n",
2480
- " title_text: str) -> None:\n",
2481
- " \n",
2482
  " \"\"\"Function to plot the cross validation of various\n",
2483
  " sklearn splitter objects.\"\"\"\n",
2484
  "\n",
@@ -2486,51 +2484,63 @@
2486
  " plot_data = []\n",
2487
  "\n",
2488
  " for train_index, valid_index in splitter_func(n_splits=n_splits).split(df):\n",
2489
- " plot_data.append([train_index, 'Train', f'{split}'])\n",
2490
- " plot_data.append([valid_index, 'Test', f'{split}'])\n",
2491
  " split += 1\n",
2492
  "\n",
2493
- " plot_df = pd.DataFrame(plot_data,\n",
2494
- " columns=['Index', 'Dataset', 'Split'])\\\n",
2495
- " .explode('Index')\n",
2496
  "\n",
2497
  " fig = go.Figure()\n",
2498
- " for _, group in plot_df.groupby('Split'):\n",
2499
- " fig.add_trace(go.Scatter(x=group['Index'].loc[group['Dataset'] == 'Train'],\n",
2500
- " y=group['Split'].loc[group['Dataset'] == 'Train'],\n",
2501
- " name='Train',\n",
2502
- " line=dict(color=\"blue\", width=10)\n",
2503
- " ))\n",
2504
- " fig.add_trace(go.Scatter(x=group['Index'].loc[group['Dataset'] == 'Test'],\n",
2505
- " y=group['Split'].loc[group['Dataset'] == 'Test'],\n",
2506
- " name='Test',\n",
2507
- " line=dict(color=\"goldenrod\", width=10)\n",
2508
- " ))\n",
 
 
 
 
 
 
2509
  "\n",
2510
- " fig.update_layout(template=\"simple_white\", font=dict(size=20),\n",
2511
- " title_text=title_text, title_x=0.5, width=850,\n",
2512
- " height=450, xaxis_title='Index', yaxis_title='Split')\n",
 
 
 
 
 
 
 
2513
  "\n",
2514
  " legend_names = set()\n",
2515
  " fig.for_each_trace(\n",
2516
- " lambda trace:\n",
2517
- " trace.update(showlegend=False)\n",
2518
- " if (trace.name in legend_names) else legend_names.add(trace.name))\n",
 
2519
  "\n",
2520
  " return fig.show()\n",
2521
- " \n",
2522
- " \n",
2523
- "if __name__ == \"__main__\":\n",
2524
  "\n",
 
 
2525
  " # Read in the data\n",
2526
- " data = pd.read_csv('../coal-price-data/AirPassengers.csv')\n",
2527
- " data['Month'] = pd.to_datetime(data['Month'])\n",
2528
- " \n",
2529
  " # Plot the cross validation\n",
2530
- " plot_cross_val(n_splits=5,\n",
2531
- " splitter_func=KFold,\n",
2532
- " df=data,\n",
2533
- " title_text='Cross-Validation')"
2534
  ]
2535
  },
2536
  {
@@ -4525,10 +4535,12 @@
4525
  "from sklearn.model_selection import TimeSeriesSplit\n",
4526
  "\n",
4527
  "# Plot the time series cross validation splits\n",
4528
- "plot_cross_val(n_splits=5,\n",
4529
- " splitter_func=TimeSeriesSplit,\n",
4530
- " df=data,\n",
4531
- " title_text='Time Series Cross-Validation')"
 
 
4532
  ]
4533
  },
4534
  {
@@ -5764,23 +5776,31 @@
5764
  "\n",
5765
  "def plot_time_series(df: pd.DataFrame) -> None:\n",
5766
  " \"\"\"General function to plot the passenger data.\"\"\"\n",
5767
- " \n",
5768
- " fig = px.line(df, x='Month', y='#Passengers',\n",
5769
- " labels={'Month': 'Date', '#Passengers': 'Passengers'})\n",
5770
- " \n",
5771
- " fig.update_layout(template=\"simple_white\", font=dict(size=18),\n",
5772
- " title_text='Airline Passengers', width=650,\n",
5773
- " title_x=0.5, height=400)\n",
 
 
 
 
 
 
 
 
 
5774
  "\n",
5775
  " return fig.show()\n",
5776
- " \n",
5777
- " \n",
5778
- "if __name__ == \"__main__\":\n",
5779
  "\n",
 
 
5780
  " # Read in the data\n",
5781
- " data = pd.read_csv('../coal-price-data/AirPassengers.csv')\n",
5782
- " data['Month'] = pd.to_datetime(data['Month'])\n",
5783
- " \n",
5784
  " # Plot the time series\n",
5785
  " plot_time_series(df=data)"
5786
  ]
@@ -5799,42 +5819,51 @@
5799
  "from statsmodels.tsa.holtwinters import ExponentialSmoothing\n",
5800
  "\n",
5801
  "\n",
5802
- "def hyperparameter_tuning_season_cv(n_splits: int,\n",
5803
- " gammas: list[float],\n",
5804
- " df: pd.DataFrame) -> pd.DataFrame: \n",
5805
  " \"\"\"Function to carry out cross-validation hyperparameter tuning\n",
5806
- " for the seasonal parameter in a Holt Winters' model. \"\"\"\n",
5807
  "\n",
5808
  " tscv = TimeSeriesSplit(n_splits=n_splits)\n",
5809
  " error_list = []\n",
5810
  "\n",
5811
  " for gamma in gammas:\n",
5812
- " \n",
5813
  " errors = []\n",
5814
- " \n",
5815
  " for train_index, valid_index in tscv.split(df):\n",
5816
  " train, valid = df.iloc[train_index], df.iloc[valid_index]\n",
5817
- " \n",
5818
- " model = ExponentialSmoothing(train['#Passengers'], trend='mul',\n",
5819
- " seasonal='mul', seasonal_periods=12) \\\n",
5820
- " .fit(smoothing_seasonal=gamma)\n",
5821
- " \n",
 
 
 
5822
  " forecasts = model.forecast(len(valid))\n",
5823
- " errors.append(mean_absolute_percentage_error(valid['#Passengers'], forecasts))\n",
 
 
5824
  "\n",
5825
  " error_list.append([gamma, sum(errors) / len(errors)])\n",
5826
  "\n",
5827
- " return pd.DataFrame(error_list, columns=['Gamma', 'MAPE'])\n",
5828
- " \n",
5829
- " \n",
5830
- "def plot_error_cv(df: pd.DataFrame,\n",
5831
- " title: str) -> None: \n",
5832
  " \"\"\"Bar chart to plot the errors from the different\n",
5833
  " hyperparameters.\"\"\"\n",
5834
  "\n",
5835
- " fig = px.bar(df, x='Gamma', y='MAPE')\n",
5836
- " fig.update_layout(template=\"simple_white\", font=dict(size=18), title_text=title,\n",
5837
- " width=800, title_x=0.5, height=400)\n",
 
 
 
 
 
 
5838
  "\n",
5839
  " return fig.show()"
5840
  ]
@@ -5846,8 +5875,8 @@
5846
  "outputs": [],
5847
  "source": [
5848
  "# Read in the data\n",
5849
- "data = pd.read_csv('../coal-price-data/AirPassengers.csv')\n",
5850
- "data['Month'] = pd.to_datetime(data['Month'])"
5851
  ]
5852
  },
5853
  {
@@ -5980,9 +6009,9 @@
5980
  ],
5981
  "source": [
5982
  "# Carry out cv for hyperparameter tuning for the seasonal parameter\n",
5983
- "error_df = hyperparameter_tuning_season_cv(df=data,\n",
5984
- " n_splits=4,\n",
5985
- " gammas=list(np.arange(0, 1.1, 0.1)))"
5986
  ]
5987
  },
5988
  {
@@ -7062,7 +7091,7 @@
7062
  ],
7063
  "source": [
7064
  "# Plot the tuning results\n",
7065
- "plot_error_cv(df=error_df, title='Hyperparameter Results')"
7066
  ]
7067
  },
7068
  {
 
2474
  "from sklearn.model_selection import KFold\n",
2475
  "\n",
2476
  "\n",
2477
+ "def plot_cross_val(\n",
2478
+ " n_splits: int, splitter_func, df: pd.DataFrame, title_text: str\n",
2479
+ ") -> None:\n",
 
 
2480
  " \"\"\"Function to plot the cross validation of various\n",
2481
  " sklearn splitter objects.\"\"\"\n",
2482
  "\n",
 
2484
  " plot_data = []\n",
2485
  "\n",
2486
  " for train_index, valid_index in splitter_func(n_splits=n_splits).split(df):\n",
2487
+ " plot_data.append([train_index, \"Train\", f\"{split}\"])\n",
2488
+ " plot_data.append([valid_index, \"Test\", f\"{split}\"])\n",
2489
  " split += 1\n",
2490
  "\n",
2491
+ " plot_df = pd.DataFrame(\n",
2492
+ " plot_data, columns=[\"Index\", \"Dataset\", \"Split\"]\n",
2493
+ " ).explode(\"Index\")\n",
2494
  "\n",
2495
  " fig = go.Figure()\n",
2496
+ " for _, group in plot_df.groupby(\"Split\"):\n",
2497
+ " fig.add_trace(\n",
2498
+ " go.Scatter(\n",
2499
+ " x=group[\"Index\"].loc[group[\"Dataset\"] == \"Train\"],\n",
2500
+ " y=group[\"Split\"].loc[group[\"Dataset\"] == \"Train\"],\n",
2501
+ " name=\"Train\",\n",
2502
+ " line=dict(color=\"blue\", width=10),\n",
2503
+ " )\n",
2504
+ " )\n",
2505
+ " fig.add_trace(\n",
2506
+ " go.Scatter(\n",
2507
+ " x=group[\"Index\"].loc[group[\"Dataset\"] == \"Test\"],\n",
2508
+ " y=group[\"Split\"].loc[group[\"Dataset\"] == \"Test\"],\n",
2509
+ " name=\"Test\",\n",
2510
+ " line=dict(color=\"goldenrod\", width=10),\n",
2511
+ " )\n",
2512
+ " )\n",
2513
  "\n",
2514
+ " fig.update_layout(\n",
2515
+ " template=\"simple_white\",\n",
2516
+ " font=dict(size=20),\n",
2517
+ " title_text=title_text,\n",
2518
+ " title_x=0.5,\n",
2519
+ " width=850,\n",
2520
+ " height=450,\n",
2521
+ " xaxis_title=\"Index\",\n",
2522
+ " yaxis_title=\"Split\",\n",
2523
+ " )\n",
2524
  "\n",
2525
  " legend_names = set()\n",
2526
  " fig.for_each_trace(\n",
2527
+ " lambda trace: trace.update(showlegend=False)\n",
2528
+ " if (trace.name in legend_names)\n",
2529
+ " else legend_names.add(trace.name)\n",
2530
+ " )\n",
2531
  "\n",
2532
  " return fig.show()\n",
 
 
 
2533
  "\n",
2534
+ "\n",
2535
+ "if __name__ == \"__main__\":\n",
2536
  " # Read in the data\n",
2537
+ " data = pd.read_csv(\"../coal-price-data/AirPassengers.csv\")\n",
2538
+ " data[\"Month\"] = pd.to_datetime(data[\"Month\"])\n",
2539
+ "\n",
2540
  " # Plot the cross validation\n",
2541
+ " plot_cross_val(\n",
2542
+ " n_splits=5, splitter_func=KFold, df=data, title_text=\"Cross-Validation\"\n",
2543
+ " )"
 
2544
  ]
2545
  },
2546
  {
 
4535
  "from sklearn.model_selection import TimeSeriesSplit\n",
4536
  "\n",
4537
  "# Plot the time series cross validation splits\n",
4538
+ "plot_cross_val(\n",
4539
+ " n_splits=5,\n",
4540
+ " splitter_func=TimeSeriesSplit,\n",
4541
+ " df=data,\n",
4542
+ " title_text=\"Time Series Cross-Validation\",\n",
4543
+ ")"
4544
  ]
4545
  },
4546
  {
 
5776
  "\n",
5777
  "def plot_time_series(df: pd.DataFrame) -> None:\n",
5778
  " \"\"\"General function to plot the passenger data.\"\"\"\n",
5779
+ "\n",
5780
+ " fig = px.line(\n",
5781
+ " df,\n",
5782
+ " x=\"Month\",\n",
5783
+ " y=\"#Passengers\",\n",
5784
+ " labels={\"Month\": \"Date\", \"#Passengers\": \"Passengers\"},\n",
5785
+ " )\n",
5786
+ "\n",
5787
+ " fig.update_layout(\n",
5788
+ " template=\"simple_white\",\n",
5789
+ " font=dict(size=18),\n",
5790
+ " title_text=\"Airline Passengers\",\n",
5791
+ " width=650,\n",
5792
+ " title_x=0.5,\n",
5793
+ " height=400,\n",
5794
+ " )\n",
5795
  "\n",
5796
  " return fig.show()\n",
 
 
 
5797
  "\n",
5798
+ "\n",
5799
+ "if __name__ == \"__main__\":\n",
5800
  " # Read in the data\n",
5801
+ " data = pd.read_csv(\"../coal-price-data/AirPassengers.csv\")\n",
5802
+ " data[\"Month\"] = pd.to_datetime(data[\"Month\"])\n",
5803
+ "\n",
5804
  " # Plot the time series\n",
5805
  " plot_time_series(df=data)"
5806
  ]
 
5819
  "from statsmodels.tsa.holtwinters import ExponentialSmoothing\n",
5820
  "\n",
5821
  "\n",
5822
+ "def hyperparameter_tuning_season_cv(\n",
5823
+ " n_splits: int, gammas: list[float], df: pd.DataFrame\n",
5824
+ ") -> pd.DataFrame:\n",
5825
  " \"\"\"Function to carry out cross-validation hyperparameter tuning\n",
5826
+ " for the seasonal parameter in a Holt Winters' model.\"\"\"\n",
5827
  "\n",
5828
  " tscv = TimeSeriesSplit(n_splits=n_splits)\n",
5829
  " error_list = []\n",
5830
  "\n",
5831
  " for gamma in gammas:\n",
 
5832
  " errors = []\n",
5833
+ "\n",
5834
  " for train_index, valid_index in tscv.split(df):\n",
5835
  " train, valid = df.iloc[train_index], df.iloc[valid_index]\n",
5836
+ "\n",
5837
+ " model = ExponentialSmoothing(\n",
5838
+ " train[\"#Passengers\"],\n",
5839
+ " trend=\"mul\",\n",
5840
+ " seasonal=\"mul\",\n",
5841
+ " seasonal_periods=12,\n",
5842
+ " ).fit(smoothing_seasonal=gamma)\n",
5843
+ "\n",
5844
  " forecasts = model.forecast(len(valid))\n",
5845
+ " errors.append(\n",
5846
+ " mean_absolute_percentage_error(valid[\"#Passengers\"], forecasts)\n",
5847
+ " )\n",
5848
  "\n",
5849
  " error_list.append([gamma, sum(errors) / len(errors)])\n",
5850
  "\n",
5851
+ " return pd.DataFrame(error_list, columns=[\"Gamma\", \"MAPE\"])\n",
5852
+ "\n",
5853
+ "\n",
5854
+ "def plot_error_cv(df: pd.DataFrame, title: str) -> None:\n",
 
5855
  " \"\"\"Bar chart to plot the errors from the different\n",
5856
  " hyperparameters.\"\"\"\n",
5857
  "\n",
5858
+ " fig = px.bar(df, x=\"Gamma\", y=\"MAPE\")\n",
5859
+ " fig.update_layout(\n",
5860
+ " template=\"simple_white\",\n",
5861
+ " font=dict(size=18),\n",
5862
+ " title_text=title,\n",
5863
+ " width=800,\n",
5864
+ " title_x=0.5,\n",
5865
+ " height=400,\n",
5866
+ " )\n",
5867
  "\n",
5868
  " return fig.show()"
5869
  ]
 
5875
  "outputs": [],
5876
  "source": [
5877
  "# Read in the data\n",
5878
+ "data = pd.read_csv(\"../coal-price-data/AirPassengers.csv\")\n",
5879
+ "data[\"Month\"] = pd.to_datetime(data[\"Month\"])"
5880
  ]
5881
  },
5882
  {
 
6009
  ],
6010
  "source": [
6011
  "# Carry out cv for hyperparameter tuning for the seasonal parameter\n",
6012
+ "error_df = hyperparameter_tuning_season_cv(\n",
6013
+ " df=data, n_splits=4, gammas=list(np.arange(0, 1.1, 0.1))\n",
6014
+ ")"
6015
  ]
6016
  },
6017
  {
 
7091
  ],
7092
  "source": [
7093
  "# Plot the tuning results\n",
7094
+ "plot_error_cv(df=error_df, title=\"Hyperparameter Results\")"
7095
  ]
7096
  },
7097
  {
multiple_timeseries_forecast.ipynb CHANGED
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