Metadata-Version: 2.1
Name: EnergyModels
Version: 0.0.9
Summary: Prediction models in timeseries
Home-page: https://github.com/IntElligence0?tab=repositories
Author: Abd_Elrahman Basala
Author-email: Abdoubasala2001@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

# Energy Models Package

                             THIS IS A PACKAGE OF MODELS OF PREDICT IN TIMESERIES FORECASTING                
             this package helps any developer in univariate and multivariate-multi-step time series forcasting lets take a look about each type 
             Real-world time series forecasting is challenging for a whole host of reasons not limited to problem features such as having multiple input variables,the requirement 
             to predict multiple time steps,nd the need to a perform the same type of prediction for multiple physical sites.

# Installation

````
pip install EnergyModels
````

# Models list
  
  * LSTM
  * LSTM-CNN 
  * BILSTM
  * GRU
  * BIGRU
  * TimeDistributer
  * CNN
  * TCN
  * Transformer
  * Performer
  * Informer
  * Seq2Seq
  * Bert
  * Lstnet
  * DeepAr
  * FNN
  * Conv-Lstm
  * MLP
  * Nbeats
  * RBFN
  * Autoformer
  
  All models take 3 parameters except
  
  * TCN :
    
        * must take value 
          * 1 : n_steps
          * 2 : n_features 
        * default value = 1 
          * 3 : n_outputs  
        
        **TCN Model you can build it by just give it data to build function**
        
  * Transformer :
  
        * must take value 
          * 1 : input_shape
          * 2 : n_outputs
          * 3 : head_size
          * 4 : num_heads
          * 5 : ff_dim
          * 6 : num_transformer_blocks
          * 7 : mlp_units 
        * default value = 0 
          * 8 , 9 : dropout  , mlp_units
    
  * Performer :
  
          * must take value 
            * 1 : maxlen
            * 2 : n_features
            * 3 : n_outputs
            * 4 : vocab_size
            * 5 : embed_dim
            * 6 : num_heads
            * 7 : ff_dim 
          * default value:
            * 8 : method => 'linear'
            * 9 : supports => 10
            * 10 : rate => 0.1 
            
  
# Package Folders 
 
 * Energy_Models
 
# how to use the package

 first you must read the data set you want to use the models on it 
 and then preprocess the data
 
 now you can build model by import it from Energy_Models folder :
 
  ````
  from Energy_Models import ==== as m
  
  ** [====] refer to model name **
  
  model=m.lstm(21, 7 , 7 ).getModel()
  21 ==> n_steps
  7 ==> n_features
  7==>n_outputs
  ````
  
 After that you will able to predict and evaluate your models used. 
 
  ````
  y=model.predict(X)
  X==>input
  ````
     
  now you can calculate loss using metrics function for train and test both by just primt_metrix func :
  
  **exists on Evaluation_Metrix if u want to just import it  :**
   
  ````
  from Energy_Models import Evaluation_Metrix as mx
  ````
 
  ````
  mx.print_metrics(Y_train,Y_pred_train,Y_test,Y_pred_test)
  
  ```` 
