lymboy-lstm 1.5

Some out-of-the-box lstm-based time series models

[英文文档](./README.md) ## Introduce lymboy-lstm contains several commonly used LSTM models for time series forecasting. Currently only univariate time series forecasting is supported. Currently built-in models are: LSTM BiLSTM CNN_LSTM CNN_BiLSTM Other models are under study… (XNN_BiLSTM_Attention, Encoder-Decoder Model, Multivariate Time Prediction Support) Please look forward to it~

## Packaging method `shell python ./setup.py sdist bdist_wheel pip install dist/lymboy-lstm-[latest-version].tar.gz # Upload to pypi # pip install twine # twine upload dist/* `

## How to install? `shell pip install lymboy-lstm `

## How to use? ### Taking LSTM model to predict power consumption as an example + Import lib `python import pandas as pd import numpy as np from lstm import LSTM from lstm.util import plot ` + Read dataset `python file = './dataset/power/power_consumption_A.csv' df = pd.read_csv(file, index_col=0) sequence = df.load ` + Modeling `python # Use the data of the past 96 times to predict the data of the next 10 times in the future model = LSTM(n_steps=96, n_output=10) # Process sequence data as model input, specifying a test set ratio of 20% model.createXY(sequence, test_size=0.2) model.fit(epochs=500, verbose=True) print(model.score()) ` 

`python plot(model.y_hat[:,0], model.y_test[:,0]) ` 

### CNN_BiLSTM predicts transformer oil temperature + Import lib `python import pandas as pd import numpy as np from lstm import LSTM, BiLSTM, CNN_BiLSTM from lstm.util import plot ` + Read dataset `python file = './dataset/ETT/ETTh1.csv' df = pd.read_csv(file, index_col=0) sequence = df.OT ` + Modeling `python model = CNN_BiLSTM(n_steps=96, n_output=24, n_seq=6) model.createXY(sequence) model.fit(epochs=500, verbose=True) print(model.score()) `


## Parameter Description

• n_steps: training step size, representing the step size of historical data, int

• n_output: predicted output length, int

• n_seq: subsequence, int (note that n_seq should be divisible by n_steps, the minimum is 1)

Other parameters are consistent with tensorflow

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