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minor change in comments notebook

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Tristan de Waard 2025-06-13 11:15:18 +02:00
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tsforecast.ipynb
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@ -957,7 +957,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": null,
"id": "7f54afc1", "id": "7f54afc1",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -987,7 +987,7 @@
" return plt.show()\n", " return plt.show()\n",
"\n", "\n",
"\n", "\n",
"# Function to transform TS data into a supervised learning format - \n", "# Function to transform TS data into a supervised learning format -\n",
"def windowing(X_input, y_input, history_size):\n", "def windowing(X_input, y_input, history_size):\n",
"\n", "\n",
" data = []\n", " data = []\n",
@ -998,6 +998,7 @@
"\n", "\n",
" return np.array(data), np.array(labels).reshape(-1, 1)\n", " return np.array(data), np.array(labels).reshape(-1, 1)\n",
"\n", "\n",
"\n",
"# Reshape to have all features and timesteps ina single row\n", "# Reshape to have all features and timesteps ina single row\n",
"def reshaping(X):\n", "def reshaping(X):\n",
" reshaped_x = X.reshape(-1, X.shape[1] * X.shape[2])\n", " reshaped_x = X.reshape(-1, X.shape[1] * X.shape[2])\n",
@ -1209,7 +1210,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 21, "execution_count": null,
"id": "cc4975ce", "id": "cc4975ce",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -1245,7 +1246,6 @@
} }
], ],
"source": [ "source": [
"\n",
"df_energy.index.freq = \"h\"\n", "df_energy.index.freq = \"h\"\n",
"y_train = df_energy[\"price actual\"].iloc[:train_cutoff]\n", "y_train = df_energy[\"price actual\"].iloc[:train_cutoff]\n",
"y_test = df_energy[\"price actual\"].iloc[train_cutoff:]\n", "y_test = df_energy[\"price actual\"].iloc[train_cutoff:]\n",
@ -1387,21 +1387,39 @@
], ],
"source": [ "source": [
"import warnings\n", "import warnings\n",
"\n",
"\n",
"warnings.filterwarnings(\"ignore\")\n", "warnings.filterwarnings(\"ignore\")\n",
"\n", "\n",
"\n",
"\n",
"# history size (number of hours)\n", "# history size (number of hours)\n",
"\n",
"\n",
"hist_size = 24\n", "hist_size = 24\n",
"\n", "\n",
"\n",
"\n",
"# Generate training & test datasets\n", "# Generate training & test datasets\n",
"X_train, y_train, X_test, y_test, train_cutoff, scaler_y, scaler_X = get_train_test(hist_size=hist_size)\n", "\n",
"\n",
"X_train, y_train, X_test, y_test, train_cutoff, scaler_y, scaler_X = get_train_test(\n",
" hist_size=hist_size\n",
")\n",
"\n",
"\n",
"\n", "\n",
"X_train_arima = reshaping(X_train)\n", "X_train_arima = reshaping(X_train)\n",
"\n",
"\n",
"X_test_arima = reshaping(X_test)\n", "X_test_arima = reshaping(X_test)\n",
"\n", "\n",
"\n", "\n",
"\n",
"# Fit AutoARIMA model (with exogenous variables)\n", "# Fit AutoARIMA model (with exogenous variables)\n",
"autoarima_model = auto_arima(\n", "autoarima_model = auto_arima(\n",
" y_train,\n", " y_train,\n",
"\n",
" # X=X_train_arima, # with extra features this takes a loooong time\n", " # X=X_train_arima, # with extra features this takes a loooong time\n",
" start_p=0,\n", " start_p=0,\n",
" start_q=0,\n", " start_q=0,\n",
@ -1419,43 +1437,82 @@
" D=None,\n", " D=None,\n",
" trace=True,\n", " trace=True,\n",
" error_action=\"ignore\",\n", " error_action=\"ignore\",\n",
"\n",
" suppress_warnings=True,\n", " suppress_warnings=True,\n",
" stepwise=True,\n", " stepwise=True,\n",
" n_jobs=-1,\n", " n_jobs=-1,\n",
" method=\"nm\",\n", " method=\"nm\",\n",
" maxiter=100\n", " maxiter=100,\n",
")\n", ")\n",
"#TODO : Try out different values for m (seasonality) -- can influence comp. cost\n", "\n",
"#TODO : Perform feature engineering, e.g. rolling means\n", "\n",
"#TODO : Try out different history sizes\n", "\n",
"# TODO : Try out different values for m (seasonality) -- can influence comp. cost\n",
"\n",
"\n",
"# TODO : Perform feature engineering, e.g. rolling means\n",
"\n",
"\n",
"# TODO : Try out different history sizes\n",
"\n",
"\n",
"\n", "\n",
"# Forecast\n", "# Forecast\n",
"y_pred = autoarima_model.predict(n_periods=len(y_test))\n", "y_pred = autoarima_model.predict(n_periods=len(y_test))\n",
"\n", "\n",
"\n",
"\n",
"# Inverse transform the predictions and actual values\n", "# Inverse transform the predictions and actual values\n",
"\n",
"\n",
"y_pred_actual = y_pred.reshape(-1, 1)\n", "y_pred_actual = y_pred.reshape(-1, 1)\n",
"\n",
"\n",
"y_test_inv = y_test.reshape(-1, 1)\n", "y_test_inv = y_test.reshape(-1, 1)\n",
"\n", "\n",
"\n",
"\n",
"# Calculate MAE\n", "# Calculate MAE\n",
"\n",
"\n",
"mae = mean_absolute_error(y_pred_actual, y_test_inv)\n", "mae = mean_absolute_error(y_pred_actual, y_test_inv)\n",
"\n", "\n",
"\n",
"\n",
"# Plot the results\n", "# Plot the results\n",
"\n",
"\n",
"fig, axes = plt.subplots(figsize=(14, 6))\n", "fig, axes = plt.subplots(figsize=(14, 6))\n",
"\n",
"\n",
"axes.plot(\n", "axes.plot(\n",
" df_energy[\"price actual\"].iloc[train_cutoff:].index[:len(y_test_inv)],\n", " df_energy[\"price actual\"].iloc[train_cutoff:].index[: len(y_test_inv)],\n",
" y_test_inv,\n", " y_test_inv,\n",
" \"k.-\",\n", " \"k.-\",\n",
" label=\"Actual\",\n", " label=\"Actual\",\n",
")\n", ")\n",
"\n",
"\n",
"\n",
"axes.plot(\n", "axes.plot(\n",
" df_energy[\"price actual\"].iloc[train_cutoff:].index[:len(y_pred_actual)],\n", " df_energy[\"price actual\"].iloc[train_cutoff:].index[: len(y_pred_actual)],\n",
" y_pred_actual,\n", " y_pred_actual,\n",
"\n",
" \"b.-\",\n", " \"b.-\",\n",
" label=\"Predicted\",\n", " label=\"Predicted\",\n",
")\n", ")\n",
"\n",
"\n",
"\n",
"axes.set(xlabel=\"Date\", ylabel=\"Price actual\")\n", "axes.set(xlabel=\"Date\", ylabel=\"Price actual\")\n",
"\n",
"\n",
"axes.set_title(f\"SARIMAX Predictions vs Actual Prices (MAE={mae:.3f})\")\n", "axes.set_title(f\"SARIMAX Predictions vs Actual Prices (MAE={mae:.3f})\")\n",
"\n",
"\n",
"axes.legend()\n", "axes.legend()\n",
"\n",
"\n",
"plt.show()" "plt.show()"
] ]
}, },
@ -1506,9 +1563,9 @@
" hist_size=hist_size\n", " hist_size=hist_size\n",
")\n", ")\n",
"\n", "\n",
"#TODO : Perform feature engineering, e.g. rolling means\n", "# TODO : Perform feature engineering, e.g. rolling means\n",
"#TODO : Perform feature selection and combine with e.g. PCA\n", "# TODO : Perform feature selection and combine with e.g. PCA\n",
"#TODO : Try out different history sizes\n", "# TODO : Try out different history sizes\n",
"\n", "\n",
"X_train_xgb = reshaping(X_train)\n", "X_train_xgb = reshaping(X_train)\n",
"X_test_xgb = reshaping(X_test)\n", "X_test_xgb = reshaping(X_test)\n",
@ -1565,9 +1622,12 @@
" verbose=False,\n", " verbose=False,\n",
")\n", ")\n",
"\n", "\n",
"# perform predictions\n",
"y_pred = xgb_model.predict(X_test_xgb)\n", "y_pred = xgb_model.predict(X_test_xgb)\n",
"y_pred_actual = scaler_y.inverse_transform(y_pred.reshape(-1, 1))\n", "y_pred_actual = scaler_y.inverse_transform(y_pred.reshape(-1, 1))\n",
"y_test_inv = scaler_y.inverse_transform(y_test)\n", "y_test_inv = scaler_y.inverse_transform(y_test)\n",
"\n",
"# Calculate MAE\n",
"mae = mean_absolute_error(y_pred_actual, y_test_inv)\n", "mae = mean_absolute_error(y_pred_actual, y_test_inv)\n",
"\n", "\n",
"\n", "\n",
@ -1662,7 +1722,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 40, "execution_count": null,
"id": "f531a91a", "id": "f531a91a",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -38792,7 +38852,6 @@
} }
], ],
"source": [ "source": [
"\n",
"# Initialize LIME explainer\n", "# Initialize LIME explainer\n",
"lime_explainer = lime.lime_tabular.LimeTabularExplainer(\n", "lime_explainer = lime.lime_tabular.LimeTabularExplainer(\n",
" training_data=np.array(X_train_xgb),\n", " training_data=np.array(X_train_xgb),\n",
@ -38810,7 +38869,9 @@
" predict_fn=xgb_model.predict,\n", " predict_fn=xgb_model.predict,\n",
" num_features=len(X_train_xgb.columns),\n", " num_features=len(X_train_xgb.columns),\n",
" )\n", " )\n",
" display(HTML(explanation.as_html()))" " display(HTML(explanation.as_html()))\n",
" # Or, you can save the explanation to an HTML file:\n",
" # explanation.save_to_file(f\"lime_explanation_{i}.html\")"
] ]
}, },
{ {
@ -39033,20 +39094,20 @@
"n_features = X_train.shape[2] # Number of features in the input\n", "n_features = X_train.shape[2] # Number of features in the input\n",
"\n", "\n",
"\n", "\n",
"# Build LSTM model\n", "# Build a simple LSTM model\n",
"model = Sequential()\n", "model = Sequential()\n",
"model.add(Input(shape=(hist_size, n_features)))\n", "model.add(Input(shape=(hist_size, n_features)))\n",
"model.add(LSTM(50, activation=\"relu\"))\n", "model.add(LSTM(50, activation=\"relu\"))\n",
"model.add(Dense(1))\n", "model.add(Dense(1))\n",
"model.compile(optimizer=\"adam\", loss=\"mse\")\n", "model.compile(optimizer=\"adam\", loss=\"mse\")\n",
"model.summary()\n", "model.summary()\n",
"#TODO : Try out different architectures, like Conv1D, Bidirectional LSTM, etc.\n", "# TODO : Try out different architectures, like Conv1D, Bidirectional LSTM, etc.\n",
"#TODO : Try out different history sizes\n", "# TODO : Try out different history sizes\n",
"\n", "\n",
"# fit the model\n", "# fit the model\n",
"model.fit(X_train, y_train, epochs=50)\n", "model.fit(X_train, y_train, epochs=50)\n",
"\n", "\n",
"\n", "# Predict on the test set\n",
"y_pred = model.predict(X_test)\n", "y_pred = model.predict(X_test)\n",
"\n", "\n",
"\n", "\n",
@ -39141,7 +39202,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 43, "execution_count": null,
"id": "7a3da2a1", "id": "7a3da2a1",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -40179,7 +40240,7 @@
"source": [ "source": [
"from ShapTime import ShapleyValues, TimeImportance, TimeHeatmap\n", "from ShapTime import ShapleyValues, TimeImportance, TimeHeatmap\n",
"\n", "\n",
"\n", "# calculate ShapTime values -- beware this can take a long time\n",
"Tn = 10 # number of time steps\n", "Tn = 10 # number of time steps\n",
"shap_values = ShapleyValues(model, X_train, Tn)\n", "shap_values = ShapleyValues(model, X_train, Tn)\n",
"time_columns = [f\"Time Step {i+1}\" for i in range(Tn)]\n", "time_columns = [f\"Time Step {i+1}\" for i in range(Tn)]\n",
@ -40188,7 +40249,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 44, "execution_count": null,
"id": "a1c887c3", "id": "a1c887c3",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -40215,8 +40276,6 @@
], ],
"source": [ "source": [
"TimeImportance(Tn, shap_values, time_columns)\n", "TimeImportance(Tn, shap_values, time_columns)\n",
"# ax = TimeHeatmap(Tn, shap_values, time_columns)\n",
"\n",
"\n", "\n",
"# Normalize SHAP values for color mapping\n", "# Normalize SHAP values for color mapping\n",
"norm = plt.Normalize(shap_values.min(), shap_values.max())\n", "norm = plt.Normalize(shap_values.min(), shap_values.max())\n",
@ -40435,17 +40494,21 @@
"# Prepare training data\n", "# Prepare training data\n",
"df_train = df_energy.iloc[:train_cutoff].copy()\n", "df_train = df_energy.iloc[:train_cutoff].copy()\n",
"df_train = df_train.rename(columns={\"price actual\": \"y\"}) # Prophet requires 'y'\n", "df_train = df_train.rename(columns={\"price actual\": \"y\"}) # Prophet requires 'y'\n",
"df_train[\"ds\"] = df_train.index.to_series().dt.tz_localize(None) # Prophet requires 'ds' without timezone\n", "df_train[\"ds\"] = df_train.index.to_series().dt.tz_localize(\n",
" None\n",
") # Prophet requires 'ds' without timezone\n",
"\n", "\n",
"# Initialize and fit the Prophet model\n", "# Initialize and fit the Prophet model\n",
"prophet_model = prophet.Prophet()\n", "prophet_model = prophet.Prophet()\n",
"#TODO : Add any additional Prophet parameters if needed\n", "# TODO : Add any additional Prophet parameters if needed\n",
"#TODO : Add extra regressors if needed\n", "# TODO : Add extra regressors if needed\n",
"prophet_model.fit(df_train)\n", "prophet_model.fit(df_train)\n",
"\n", "\n",
"# Create future dataframe for prediction\n", "# Create future dataframe for prediction\n",
"df_future = df_energy.iloc[train_cutoff:].copy()\n", "df_future = df_energy.iloc[train_cutoff:].copy()\n",
"df_future[\"ds\"] = df_future.index.to_series().dt.tz_localize(None) # Prophet requires 'ds' without timezone\n", "df_future[\"ds\"] = df_future.index.to_series().dt.tz_localize(\n",
" None\n",
") # Prophet requires 'ds' without timezone\n",
"\n", "\n",
"# Make forecasts\n", "# Make forecasts\n",
"df_forecast = prophet_model.predict(df_future)\n", "df_forecast = prophet_model.predict(df_future)\n",
@ -40490,7 +40553,7 @@
"axes[1].legend()\n", "axes[1].legend()\n",
"\n", "\n",
"plt.tight_layout()\n", "plt.tight_layout()\n",
"plt.show()\n" "plt.show()"
] ]
} }
], ],