Metadata-Version: 2.1
Name: ESRNN
Version: 0.1.1
Summary: Pytorch implementation of the ESRNN
Home-page: https://github.com/kdgutier/esrnn_torch
Author: Kin Gutierrez, Cristian Challu, Federico Garza
Author-email: kin.gtz.olivares@gmail.com, cristianichallu@gmail.com, fede.garza.ramirez@gmail.com
License: UNKNOWN
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        # Pytorch Implementation of the ES-RNN
        In this project we coded a pytorch class for the ES-RNN algorithm proposed by Smyl, winning submission of the M4 Forecasting Competition. The class wraps fit and predict methods to facilitate interaction with Machine Learning pipelines along with evaluation and data wrangling utility.
        
        ## Installation Prerequisites
        * numpy==1.16.1
        * pandas==0.25.2
        * pytorch>=1.3.1
        
        ## Installation
        
        This code is a work in progress, any contributions or issues are welcome on
        GitHub at: https://github.com/kdgutier/esrnn_torch
        
        You can install the *released version* of `ESRNN` from the [Python package index](https://pypi.org) with:
        
        ```python
        pip install ESRNN
        ```
        
        ## Usage Example
        Make sure on use of the model that the dataframes to fit satisfy being **balanced**,
        and there are **no zeros** at the beginning of the series, there are  **no negative values**, since that
        has bad interactions with the multiplicative model.
        
        ```python
        from ESRNN.m4_data import prepare_m4_data
        from ESRNN.utils_evaluation import evaluate_prediction_owa
        
        from ESRNN import ESRNN
        
        X_train_df, y_train_df, X_test_df, y_test_df = prepare_m4_data(dataset_name='Yearly',
                                                                       directory = './data',
                                                                       num_obs=1000)
        
        # Instantiate model
        model = ESRNN(max_epochs=25, freq_of_test=5, batch_size=4, learning_rate=1e-4,
                      per_series_lr_multip=0.8, lr_scheduler_step_size=10,
                      lr_decay=0.1, gradient_clipping_threshold=50,
                      rnn_weight_decay=0.0, level_variability_penalty=100,
                      testing_percentile=50, training_percentile=50,
                      ensemble=False, max_periods=25, seasonality=[],
                      input_size=4, output_size=6,
                      cell_type='LSTM', state_hsize=40,
                      dilations=[[1], [6]], add_nl_layer=False,
                      random_seed=1, device='cpu')
        
        # Fit model
        # If y_test_df is provided the model
        # will evaluate predictions on
        # this set every freq_test epochs
        model.fit(X_train_df, y_train_df, X_test_df, y_test_df)
        
        # Predict on test set
        y_hat_df = model.predict(X_test_df)
        
        # Evaluate predictions
        final_owa, final_mase, final_smape = evaluate_prediction_owa(y_hat_df, y_train_df,
                                                                     X_test_df, y_test_df,
                                                                     naive2_seasonality=1)
        ```
        ## Overall Weighted Average
        
        A metric that is useful for quantifying the aggregate error of a specific model for various time series is the Overall Weighted Average (OWA) proposed for the M4 competition. This metric is calculated by obtaining the average of the symmetric mean absolute percentage error (sMAPE) and the mean absolute scaled error (MASE) for all the time series of the model and also calculating it for the Naive2 predictions. Both sMAPE and MASE are scale independent. These measurements are calculated as follows:
        
        ![OWA](.github/images/metrics.png)
        
        
        
        ## Current Results
        Here we used the model directly to compare to the original implementation. It is worth noticing that these results do not include the ensemble methods mentioned in the [ESRNN paper](https://www.sciencedirect.com/science/article/pii/S0169207019301153).<br/>
        [Results of the M4 competition](https://www.researchgate.net/publication/325901666_The_M4_Competition_Results_findings_conclusion_and_way_forward).
        <br/>
        
        | DATASET   | OUR OWA | M4 OWA (Smyl) |
        |-----------|:---------:|:--------:|
        | Yearly    | 0.785   | 0.778  |
        | Quarterly | 0.879   | 0.847  |
        | Monthly   | 0.872   | 0.836  |
        | Hourly    | 0.615   | 0.920  |
        | Weekly    | 0.952   | 0.920  |
        | Daily     | 0.968   | 0.920  |
        
        
        ## Replicating M4 results
        
        
        Replicating the M4 results is as easy as running the following line of code (for each frequency) after installing the package via pip:
        
        ```console
        python -m ESRNN.m4_run --dataset 'Yearly' --results_directory '/some/path' \
                               --gpu_id 0 --use_cpu 0
        ```
        
        Use `--help` to get the description of each argument:
        
        ```console
        python -m ESRNN.m4_run --help
        ```
        
        ## Authors
        * **Kin Gutierrez** - [kdgutier](https://github.com/kdgutier)
        * **Cristian Challu** - [cristianchallu](https://github.com/cristianchallu)
        * **Federico Garza** - [FedericoGarza](https://github.com/FedericoGarza)
        
        ## License
        This project is licensed under the MIT License - see the [LICENSE](https://github.com/kdgutier/esrnn_torch/blob/master/LICENSE) file for details.
        
        
        ## REFERENCES
        1. [A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting](https://www.sciencedirect.com/science/article/pii/S0169207019301153)
        2. [The M4 Competition: Results, findings, conclusion and way forward](https://www.researchgate.net/publication/325901666_The_M4_Competition_Results_findings_conclusion_and_way_forward)
        3. [M4 Competition Data](https://github.com/M4Competition/M4-methods/tree/master/Dataset)
        4. [Dilated Recurrent Neural Networks](https://papers.nips.cc/paper/6613-dilated-recurrent-neural-networks.pdf)
        5. [Residual LSTM: Design of a Deep Recurrent Architecture for Distant Speech Recognition](https://arxiv.org/abs/1701.03360)
        6. [A Dual-Stage Attention-Based recurrent neural network for time series prediction](https://arxiv.org/abs/1704.02971)
        
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
