Model: FluX - Recurrent Neural Network (Long Short-Term Memory) API

    Name: Recurrent Neural Network (Long Short-Term Memory)
    Abbreviation: LSTM
    Owner: kgle
    Project: CDC Real-time Forecast
    Team name: FluX
    Description: Team name: FluX
    Team members: Lijing Wang, Aniruddha Adiga, Srini Venkatramanan, Bryan Lewis, Jiangzhuo Chen, Anil Vullikanti, Madhav Marathe
    Data source(s): ILINet
    Methods: An LSTM based deep learning predictor which learns patterns from historical time series. It models non-linear relationships between weekly data points and makes forecasts by looking back to the time sequence of data points of previous weeks. The model provides probabilistic forecasting by using MC-Dropout technology and considers uncertainty from models, predictions, and backfill data. Out-of-sample forecasts were generated by using data from Delphi's Epidata API. Missing weeks in early seasons (e.g., EW20-40 for HHS regions before 2014) which were retrospectively filled in 2013EW52, were treated as missing, and filled by forward fill.
    Home: https://github.com/FluSightNetwork/cdc-flusight-ensemble/tree/master/model-forecasts/component-models/FluX_LSTM
    Auxiliary data: (No auxiliary data)

    Forecasts (3)

    Timezero Data Source Upload Date
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    2019-10-14 EW42-2019-FluX_LSTM.csv 2019-11-08 12:40:21
    2019-10-21 EW43-2019-FluX_LSTM.csv 2019-11-08 12:41:12
    2019-10-28 EW44-2019-FluX_LSTM.csv 2019-11-15 10:02:31
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