[D] Doubts on the implementation of LSTMs for timeseries prediction (like including weather forecasts)

• neuralforecast

  84 2,405 8.9 Python

  Scalable and user friendly neural :brain: forecasting algorithms.

  

• Time-Series-Library

  3 4,259 8.3 Shell

  A Library for Advanced Deep Time Series Models.

  

Don't use an LSTM. Get up to date with SoTA methods and read the papers in the field. LSTMs are not the way forward. Read the papers I suggested. It would be very useful to come to grips with both the Time Series Repository (https://github.com/thuml/Time-Series-Library) and Darts (https://github.com/unit8co/darts) as these are widely used for research and in industry.

• InfluxDB

  www.influxdata.com sponsored

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• darts

  47 7,272 9.1 Python

  A python library for user-friendly forecasting and anomaly detection on time series.

  

Don't use an LSTM. Get up to date with SoTA methods and read the papers in the field. LSTMs are not the way forward. Read the papers I suggested. It would be very useful to come to grips with both the Time Series Repository (https://github.com/thuml/Time-Series-Library) and Darts (https://github.com/unit8co/darts) as these are widely used for research and in industry.

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