Prediction models in timeseries
Project description
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)
Project details
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