EnergyModels 0.0.2

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 in house-power-consumption dataset 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

• BILSTM

• GRU

• BIGRU

• TimeDistributer

• CNN

• TCN 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

Package Folders

• Data
• models

how to use the package

first you must read the data set you want to use the models on it and then import preprocess_data from Data folder :

from Data import preprocess_data as pr

df = pd.read_csv('Data.txt',sep=';', 
                 parse_dates={'date_time' : ['Date', 'Time']}, infer_datetime_format=True, 
                 low_memory=False, na_values=['nan','?'], index_col='date_time')

pr.fill_missing(df.values)
df.to_csv('new_data.csv')

df = pd.read_csv('new_data.csv',parse_dates=['date_time'], index_col= 'date_time')

next step you can use functions on preprocess_data to split and scale the data .

X_train, X_test = pr.train_test_split(df)
X_train, X_test, scaler = pr.scale_data(X_train, X_test)

After that converting the data to supervised. now you can build model by import it from models folder :

from models import Models as m
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

Models have evaluation function you can give it the model and (actual , predicted) values

m.evaluate(model,actual,pred)

Else you can calculate loss using metrics function for train and test both :

m.print_metrics(model,Y_train,Y_pred_train,Y_test,Y_pred_test)