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 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)
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
EnergyModels-0.0.9.tar.gz
(33.6 kB
view hashes)
Built Distribution
Close
Hashes for EnergyModels-0.0.9-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 32bb50ae74890ff902cb48844c15c8827a3966fa17003eafb5af9d6277f59837 |
|
MD5 | b12f00af5ec876ce2eead1e7e206c9eb |
|
BLAKE2b-256 | 440ee0846beb7855e74beeb6383cb88d4d9f4e3b0ef2bd85b4fcb64d028e5c2c |