Discussing the article: "Neural Networks in Trading: Injection of Global Information into Independent Channels (InjectTST)"

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Check out the new article: Neural Networks in Trading: Injection of Global Information into Independent Channels (InjectTST).

Most modern multimodal time series forecasting methods use the independent channels approach. This ignores the natural dependence of different channels of the same time series. Smart use of two approaches (independent and mixed channels) is the key to improving the performance of the models.

In recent years, Transformer-based architectures for multimodal time series forecasting have gained widespread popularity and are progressively becoming one of the most preferred models for time series analysis. Increasingly, models are utilizing independent channel approaches, where the model processes each channel sequence separately, without interacting with the others.

Channel independence offers two primary advantages:

  1. Noise Suppression: Independent models can focus on predicting individual channels without being influenced by noise from other channels.
  2. Mitigating Distribution Drift: Channel independence can help address the issue of distribution drift in time series.

Conversely, mixing channels tends to be less effective in dealing with these challenges, which can result in decreased model performance. However, channel mixing does have unique advantages:

  1. High Information Capacity: Channel mixing models excel at capturing inter-channel dependencies, potentially offering more information for forecasting future values.
  2. Channel Specificity: Optimizing multiple channels within channel mixing models allows the model to fully leverage the distinctive characteristics of each channel.

Moreover, since independent channel approaches analyze individual channels through a unified model, the model cannot distinguish between channels, focusing primarily on the shared patterns across multiple channels. This leads to a loss of channel specificity and may negatively impact multimodal time series forecasting.

 

Author: Dmitriy Gizlyk

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