Metadata-Version: 2.1
Name: deepforecast
Version: 0.0.3
Summary: An easy-to-use deep model for time series forecast
Home-page: https://github.com/NELSONZHAO/DeepForecast
Author: Nelson Zhao
Author-email: dutzhaoyeyu@163.com
License: UNKNOWN
Description: # **DeepForecast**
        
        An Easy-to-use Deep Model for Time Series Forecast
        
        ## Methods
        
        - STTF
        - Seq2Seq
        
        ## How to install
        
        ```pip install deepforecast```
        
        ## How to use
        
        Use STTF as an exapmle:
        ```python
        from tensorflow.keras.utils import plot_model
        from deepforecast.features import SparseColumn, SequenceColumn
        from deepforecast.models import STTF
        
        attr_feats = ["age", "user", "platform"]
        sequence_feats = ["history", "future"]
        
        attr_columns = []
        for feat in attr_feats:
            col = SparseColumn(name=feat, vocab_size=10, embed_dim=8)
            attr_columns.append(col)
        
        sequence_columns = []
        hist_col = SequenceColumn(name="history", num_seq=5, seq_steps=28, dim=1)
        sequence_columns.append(hist_col)
        fut_col = SequenceColumn(name="future", num_seq=4, seq_steps=7, dim=1)
        sequence_columns.append(fut_col)
        
        model = STTF(attr_columns, sequence_columns, attr_attention_embed_dim=12)
        model.summary()
        plot_model(model, show_shapes=True)
        model.compile(optimizer="rmsprop",
                      loss=["mse", "mse"],
                      loss_weights=[0.2, 0.8],
                      metrics=["mse"])
        ```
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
