Discussing the article: "Neural Networks in Trading: An End-to-End Multivariate Time Series Forecasting Model (GinAR)"

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Check out the new article: Neural Networks in Trading: An End-to-End Multivariate Time Series Forecasting Model (GinAR).

We invite you to explore an innovative approach to forecasting time series with missing data using the GinAR framework. The article demonstrates the implementation of key components using OpenCL, which ensures high performance. In our next publication, we will take a detailed look at how to integrate these solutions into MQL5. This will help understand how to apply the method in practice in trading.

The architecture of the GinAR framework follows the classic Encoder → Decoder scheme. A stack of GinAR layers is used as the Encoder, while the Decoder is implemented using a multilayer perceptron (MLP). A key component of the system is the GinAR cell— a module based on the principle of recursive modeling. When fed incomplete data — that is, time series with missing variables — the model is capable of forecasting future values for all variables simultaneously, even those for which a complete history is missing.

When designing the internal logic of GinAR, the framework’s authors proposed replacing all fully connected layers in the classic recursive structure of SRU with two specialized components: IA (Interpolation Attention) and AGCN (Adaptive Graph Convolution Network).

The first component imputes missing variables using information from available neighbors. This is particularly important in the context of financial markets: if data for key assets are missing, even partially, the model can lose a significant amount of accuracy. The IA mechanism prevents this by reconstructing meaningful representations of the missing time series based on the available data and minimizing the impact of noise.

The second component, AGCN, moves away from fixed dependency graphs, such as predefined relationships between assets, indices, and sectors, and learns an adaptive graph structure directly during training. This makes it possible to flexibly capture spatial relationships between variables, even if they change over time or are not immediately obvious.

The encoder of GinAR is implemented using a recursive scheme in which, at each time step, the current features and the internal state from the previous step are fed as inputs. The GinAR cell processes them, updates the state, and generates a hidden representation of the current time step. This approach makes it possible to simultaneously impute missing data, reconstruct the dependency graph, and extract dynamic patterns — all within a single module. Thanks to residual connections (skip connections), the model can be deep without sacrificing training stability. This is particularly important when predicting complex market signals, where both short-term fluctuations and long-term trends are significant.

Author: Dmitriy Gizlyk