EnergyModels 0.0.9

Prediction models in timeseries

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)