{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# Red recurrente para predicción de los valores de una acción\n","\n","## Introducción\n","\n","En este ejercicio vamos a predecir los valores que va a tomar una acción durante un número determinado de días al final de un período. Para ello debemos **cargar en Google Colab** el fichero CSV que se nos proporcionará con los datos de la acción en cuestión."],"metadata":{"id":"JeU-g1yxcXQo"}},{"cell_type":"markdown","source":["## Inicialización\n","\n","Inicializamos las librerías y variables de configuración necesarias antes de empezar"],"metadata":{"id":"5hWkZmBGctm9"}},{"cell_type":"code","source":["# Importar módulos necesarios\n","import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","from keras.models import Sequential\n","from keras.layers import Input, Dense, Dropout, LSTM, Bidirectional\n","from sklearn.preprocessing import MinMaxScaler\n","\n","# Variables de configuración del problema\n","EPOCHS = 50 # Número de iteraciones de la red\n","NEURONAS_CAPA = 50 # Número de neuronas en cada capa oculta\n","T = 60 # Tamaño del \"timestep\""],"metadata":{"id":"FCaFgQWjYsFQ"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Ahora vamos a cargar el fichero CSV y seleccionar los datos de entrada"],"metadata":{"id":"MfmNR7vEZk9u"}},{"cell_type":"code","source":["# Carga inicial de datos, fijando como índice la columna \"Date\"\n","datos = pd.read_csv('valores_accion.csv', index_col='Date', parse_dates=['Date'])\n","datos.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":238},"id":"-TOZr8YwZo98","executionInfo":{"status":"ok","timestamp":1739008576124,"user_tz":-60,"elapsed":162,"user":{"displayName":"Nacho Iborra Baeza","userId":"05178912028373152639"}},"outputId":"801385f9-4c4b-491f-d94a-ace9a285312d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" Open High Low Close Volume Name\n","Date \n","2006-01-03 39.69 41.22 38.79 40.91 24232729 AABA\n","2006-01-04 41.22 41.90 40.77 40.97 20553479 AABA\n","2006-01-05 40.93 41.73 40.85 41.53 12829610 AABA\n","2006-01-06 42.88 43.57 42.80 43.21 29422828 AABA\n","2006-01-09 43.10 43.66 42.82 43.42 16268338 AABA"],"text/html":["\n","
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OpenHighLowCloseVolumeName
Date
2006-01-0339.6941.2238.7940.9124232729AABA
2006-01-0441.2241.9040.7740.9720553479AABA
2006-01-0540.9341.7340.8541.5312829610AABA
2006-01-0642.8843.5742.8043.2129422828AABA
2006-01-0943.1043.6642.8243.4216268338AABA
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"datos","summary":"{\n \"name\": \"datos\",\n \"rows\": 3019,\n \"fields\": [\n {\n \"column\": \"Date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2006-01-03 00:00:00\",\n \"max\": \"2017-12-29 00:00:00\",\n \"num_unique_values\": 3019,\n \"samples\": [\n \"2011-08-11 00:00:00\",\n \"2012-03-20 00:00:00\",\n \"2006-10-23 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Open\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 13.257241924440622,\n \"min\": 9.1,\n \"max\": 73.02,\n \"num_unique_values\": 1913,\n \"samples\": [\n 34.67,\n 26.41,\n 66.26\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"High\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 13.356691549246193,\n \"min\": 9.48,\n \"max\": 73.25,\n \"num_unique_values\": 1921,\n \"samples\": [\n 19.63,\n 37.84,\n 12.27\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 13.157325947582661,\n \"min\": 8.94,\n \"max\": 72.46,\n \"num_unique_values\": 1910,\n \"samples\": [\n 36.56,\n 32.54,\n 65.4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Close\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 13.258163236301503,\n \"min\": 8.95,\n \"max\": 72.93,\n \"num_unique_values\": 1960,\n \"samples\": [\n 27.34,\n 13.92,\n 28.09\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Volume\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 19262307,\n \"min\": 1939061,\n \"max\": 438231658,\n \"num_unique_values\": 3018,\n \"samples\": [\n 13301365,\n 30997807,\n 23239286\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Name\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"AABA\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":3}]},{"cell_type":"code","source":["# El conjunto de entrenamiento serán todos los valores de la acción hasta 2016 (inclusive)\n","# Nos quedaremos con la columna 1 (valor máximo de la acción o \"High\")\n","datos_train = datos[:'2016'].iloc[:,1:2]\n","# El conjunto de test a predecir serán los valores en 2017\n","datos_test = datos['2017':].iloc[:,1:2]\n","print(datos_train.head())\n","print(datos_test.head())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nDSAWYoZZz81","executionInfo":{"status":"ok","timestamp":1739008593274,"user_tz":-60,"elapsed":53,"user":{"displayName":"Nacho Iborra Baeza","userId":"05178912028373152639"}},"outputId":"4c179bf9-9a28-4e09-d8d4-2311ccbd35c5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":[" High\n","Date \n","2006-01-03 41.22\n","2006-01-04 41.90\n","2006-01-05 41.73\n","2006-01-06 43.57\n","2006-01-09 43.66\n"," High\n","Date \n","2017-01-03 39.18\n","2017-01-04 40.25\n","2017-01-05 41.37\n","2017-01-06 41.34\n","2017-01-09 41.66\n"]}]},{"cell_type":"markdown","source":["Mostramos un gráfico combinado de los valores de entrenamiento y test, para ver lo que vamos a predecir"],"metadata":{"id":"KpJJjlSwa2cO"}},{"cell_type":"code","source":["datos[\"High\"][:'2016'].plot(figsize=(16,4),legend=True)\n","datos[\"High\"]['2017':].plot(figsize=(16,4),legend=True)\n","plt.legend(['Valores acción hasta 2017','Valores acción en 2017+'])\n","plt.title('Precio máximo acción')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":390},"id":"9Wm46sPEa89R","executionInfo":{"status":"ok","timestamp":1739008603242,"user_tz":-60,"elapsed":1165,"user":{"displayName":"Nacho Iborra Baeza","userId":"05178912028373152639"}},"outputId":"2ac7ed0f-7f9e-4b1d-a339-a2dd5166f7f2"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["Pasamos a escalar los datos de entrenamiento y test en el rango 0-1"],"metadata":{"id":"Z4nDN_Iycggm"}},{"cell_type":"code","source":["sc = MinMaxScaler(feature_range=(0,1))\n","datos_train = sc.fit_transform(datos_train)\n","datos_test = sc.transform(datos_test)"],"metadata":{"id":"my_88-LQcj4L"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Ahora construimos las secuencias de entrada con el *timestep* T indicado"],"metadata":{"id":"gOSZTwRNc0I7"}},{"cell_type":"code","source":["X_train = []\n","y_train = []\n","for i in range(T, len(datos_train)):\n"," X_train.append(datos_train[i-T:i, 0])\n"," y_train.append(datos_train[i, 0])\n","X_train, y_train = np.array(X_train), np.array(y_train)\n","print(X_train.shape)\n","print(y_train.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Bw2nvSh2czYp","executionInfo":{"status":"ok","timestamp":1739008798461,"user_tz":-60,"elapsed":24,"user":{"displayName":"Nacho Iborra Baeza","userId":"05178912028373152639"}},"outputId":"4fe9943d-0b0b-4981-863e-fff6be008ec7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(2708, 60)\n","(2708,)\n"]}]},{"cell_type":"markdown","source":["## Ejercicio 1. Construcción del modelo (RNR)\n","\n","Vamos a construir ahora una red neuronal recurrente con las siguientes características:\n","\n","- 4 capas LSTM con tantas neuronas como hayamos indicado en la variable NEURONAS_CAPA\n","- Función de coste MSE\n","- Optimizador RMSProp"],"metadata":{"id":"ws9Wi_ANdkQm"}},{"cell_type":"code","source":["modelo1 = Sequential()\n","# TAREA: construye una red neuronal recurrente con:\n","# - 4 capas LSTM de 50 neuronas cada una\n","# - Optimizador 'rmsprop'\n","# - Función de coste 'mse'\n","# Entrena durante 50 epochs con los datos anteriores\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SD2nsulWeADB","executionInfo":{"status":"ok","timestamp":1739009700817,"user_tz":-60,"elapsed":792646,"user":{"displayName":"Nacho Iborra Baeza","userId":"05178912028373152639"}},"outputId":"22270cb5-4108-4c2e-8aa6-26c2892ccd3c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 132ms/step - loss: 0.0308\n","Epoch 2/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 116ms/step - loss: 0.0069\n","Epoch 3/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 111ms/step - loss: 0.0052\n","Epoch 4/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 123ms/step - loss: 0.0052\n","Epoch 5/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 122ms/step - loss: 0.0045\n","Epoch 6/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 128ms/step - loss: 0.0039\n","Epoch 7/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 133ms/step - loss: 0.0040\n","Epoch 8/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 142ms/step - loss: 0.0037\n","Epoch 9/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 126ms/step - loss: 0.0026\n","Epoch 10/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 112ms/step - loss: 0.0028\n","Epoch 11/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 127ms/step - loss: 0.0029\n","Epoch 12/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 126ms/step - loss: 0.0027\n","Epoch 13/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 115ms/step - loss: 0.0028\n","Epoch 14/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 125ms/step - loss: 0.0023\n","Epoch 15/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 121ms/step - loss: 0.0023\n","Epoch 16/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 127ms/step - loss: 0.0022\n","Epoch 17/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 124ms/step - loss: 0.0022\n","Epoch 18/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 106ms/step - loss: 0.0022\n","Epoch 19/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 145ms/step - loss: 0.0022\n","Epoch 20/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 127ms/step - loss: 0.0021\n","Epoch 21/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 123ms/step - loss: 0.0021\n","Epoch 22/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 127ms/step - loss: 0.0018\n","Epoch 23/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 108ms/step - loss: 0.0020\n","Epoch 24/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 129ms/step - loss: 0.0019\n","Epoch 25/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 125ms/step - loss: 0.0017\n","Epoch 26/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 122ms/step - loss: 0.0018\n","Epoch 27/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 128ms/step - loss: 0.0016\n","Epoch 28/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 117ms/step - loss: 0.0018\n","Epoch 29/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 147ms/step - loss: 0.0016\n","Epoch 30/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 120ms/step - loss: 0.0018\n","Epoch 31/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 125ms/step - loss: 0.0016\n","Epoch 32/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 117ms/step - loss: 0.0016\n","Epoch 33/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 109ms/step - loss: 0.0016\n","Epoch 34/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 125ms/step - loss: 0.0016\n","Epoch 35/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 123ms/step - loss: 0.0016\n","Epoch 36/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 128ms/step - loss: 0.0015\n","Epoch 37/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 125ms/step - loss: 0.0018\n","Epoch 38/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 106ms/step - loss: 0.0014\n","Epoch 39/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 125ms/step - loss: 0.0015\n","Epoch 40/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 134ms/step - loss: 0.0012\n","Epoch 41/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 125ms/step - loss: 0.0015\n","Epoch 42/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 127ms/step - loss: 0.0014\n","Epoch 43/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 105ms/step - loss: 0.0015\n","Epoch 44/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 127ms/step - loss: 0.0015\n","Epoch 45/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 125ms/step - loss: 0.0015\n","Epoch 46/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 118ms/step - loss: 0.0015\n","Epoch 47/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 112ms/step - loss: 0.0014\n","Epoch 48/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 126ms/step - loss: 0.0013\n","Epoch 49/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 127ms/step - loss: 0.0013\n","Epoch 50/50\n","\u001b[1m85/85\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 116ms/step - loss: 0.0014\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":10}]},{"cell_type":"markdown","source":["## Predicción y evaluación de resultados\n","\n","Vamos ahora a predecir los valores del conjunto de test, y a compararlos con los reales.\n","\n","En primer lugar, construimos una secuencia que aúne los datos de entrenamiento y a continuación los de test, ya que para poder predecir el primer valor de test necesitaremos los T valores previos de entrenamiento, y así sucesivamente"],"metadata":{"id":"Mw2FBLWShg8y"}},{"cell_type":"code","source":["datos_enlazados = np.concatenate((datos_train, datos_test),axis=0)\n","entradas = datos_enlazados[len(datos_enlazados)-len(datos_test) - T:]"],"metadata":{"id":"s_dEmf_Phz4K"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Ahora vamos a construir el conjunto de test con bloques de T valores"],"metadata":{"id":"_8B3-jBvimr6"}},{"cell_type":"code","source":["X_test = []\n","for i in range(T,len(entradas)):\n"," X_test.append(entradas[i-T:i,0])\n","X_test = np.array(X_test)\n","X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[1],1))\n","X_test.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fE_WH5L7ivO7","executionInfo":{"status":"ok","timestamp":1739009707931,"user_tz":-60,"elapsed":48,"user":{"displayName":"Nacho Iborra Baeza","userId":"05178912028373152639"}},"outputId":"d029df3f-06db-4df8-fda6-e00fab455e6f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(251, 60, 1)"]},"metadata":{},"execution_count":12}]},{"cell_type":"markdown","source":["Predecimos los resultados para cada secuencia del test, y comparamos los resultados obtenidos con los datos reales del conjunto de test"],"metadata":{"id":"cN4aWeVujGdY"}},{"cell_type":"code","source":["# TAREA: recoger las predicciones sobre X_test\n","# y mostrar un gráfico comparativo con los valores\n","# reales de \"datos_test\"\n","# Recuerda des-normalizar los datos de ambas secuencias\n","# Función auxiliar para predecir resultados\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":489},"id":"lNLuiY-YjLmF","executionInfo":{"status":"ok","timestamp":1739009754463,"user_tz":-60,"elapsed":3201,"user":{"displayName":"Nacho Iborra Baeza","userId":"05178912028373152639"}},"outputId":"2a4e8115-0db9-47da-c202-9c9aa3bbc00b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 131ms/step\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]}]}