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PrediccionAUCORP

Prediccion de Valores en Series de Tiempo (Forecasting Time Series) usando MLP, LSTM - RNN

IMPORTANTE Entrega 2 - Python 08/09/2019: https://github.com/FreddieAbad/PrediccionAUCORP/blob/master/Entrega%202/Prediccion%20Final.ipynb

Entrega 1 - Weka: https://github.com/FreddieAbad/PrediccionAUCORP/blob/master/Entrega/Prediccion%20de%20Ventas%20en%20una%20Serie%20de%20Tiempo%20-%20P.%20Aucay.ipynb

Analisis Normalizado: https://github.com/FreddieAbad/PrediccionAUCORP/blob/master/Analisis%20Variables%20Normalizado.ipynb

Analisis No Normalizado: https://github.com/FreddieAbad/PrediccionAUCORP/blob/master/Analisis%20Variables%20Sin%20Normalizar.ipynb

CONFIGURACION

Para correr el Jupyter Notebook se puede realizar por 2 metodos:

Configuracion 1

Entorno Conda para importar: (Para importar el entorno es necesario tener instalado con Anaconda3.

La version de python y demas estan determinados en el siguiente entorno ) https://github.com/FreddieAbad/PrediccionAUCORP/blob/master/Entrega/Ambiente%20Conda/condaEnvironmentPrediccionAUCORP.yml

Configuracion 2

Configuracion Directa

Dependencias:
  • Python 3.6 (Es fundamental contar con esta version ya que Py3.7 no corren algunas funciones, Py 2.4 tiene funciones deprecated)
  • Keras
  • Tensorflow
  • Scikit Learn
  • Scipy
  • Pandas
  • Numpy
  • Jupyter Notebook for Anaconda.
Comando para instalar Tensorflow:

Si el computador tiene GPU

pip install --ignore-installed --upgrade tensorflow-gpu 

Si el computador NO tiene GPU

pip install --ignore-installed --upgrade tensorflow
Comando para instalar KERAS:
conda install mkl-service m2w64-toolchain pip install pydot keras

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Forecasting de Ventas (Forecasting Time Series) usando MLP. LSTM, RNN

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