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Complex neural network examples for Flux.jl.

This package contains a loose collection of (slightly) more advanced neural network architectures, mostly centered around time series forecasting.


To install FluxArchitectures, type ] to activate the package manager, and type

add FluxArchitectures

for installation. After using FluxArchitectures, the following functions are exported:

  • prepare_data
  • get_data
  • DSANet
  • LSTnet

See their docstrings, the documentation, and the examples folder for details.


  • LSTnet: This "Long- and Short-term Time-series network" follows the paper by Lai et. al..

  • DARNN: The "Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction" is based on the paper by Qin et. al..

  • TPA-LSTM: The Temporal Pattern Attention LSTM network is based on the paper "Temporal Pattern Attention for Multivariate Time Series Forecasting" by Shih et. al..

  • DSANet: The "Dual Self-Attention Network for Multivariate Time Series Forecasting" is based on the paper by Siteng Huang et. al.


Activate the package and load some sample-data:

using FluxArchitectures
poollength = 10; horizon = 15; datalength = 1000;
input, target = get_data(:exchange_rate, poollength, datalength, horizon)

Define a model and a loss function:

model = LSTnet(size(input, 1), 2, 3, poollength, 120)
loss(x, y) = Flux.mse(model(x), y')

Train the model:

Flux.train!(loss, Flux.params(model),Iterators.repeated((input, target), 20), Adam(0.01))