Combination Prediction Method Based on GM (Grey Model) and LSTM (Long Short-Term Memory Network)

1. Basic Principles and Complementarity of GM and LSTM

1. Core Features of the GM Model

  • Basic Principle: The original unordered sequence is transformed into a smooth sequence with exponential regularity through Accumulated Generating Operation (AGO), establishing a first-order differential equation (e.g., GM(1,1)) for prediction. Its mathematical form is:

    where X is the accumulated sequence, and a is the parameter to be estimated.

  • Advantages:

    • Requires a small sample size (≥4 data points);
    • Does not rely on data distribution assumptions, suitable for highly random and incomplete information “grey systems”;
    • High computational efficiency, suitable for short to medium-term predictions.
  • Limitations: Accuracy decreases for long-term predictions, insufficient capture of nonlinear relationships.

2. Core Features of LSTM

  • Basic Principle: Controls cell state (long-term memory) through forget gates, input gates, and output gates, solving the gradient vanishing problem of traditional RNNs. Core formulas include:

    Combination Prediction Method Based on GM (Grey Model) and LSTM (Long Short-Term Memory Network)

  • Advantages:

    • Captures long-term dependencies, suitable for complex nonlinear sequences;
    • Performs excellently in time series predictions (e.g., stock prices, wind speeds).
  • Limitations: Requires a large amount of training data, high computational cost, sensitive to parameters.

3. Complementarity Analysis

  • GM provides data preprocessing capabilities: Transforms the original sequence into a more regular accumulated sequence, reducing noise impact;
  • LSTM provides nonlinear modeling capabilities: Learns complex patterns in GM residuals;
  • Combination method: GM extracts trend components, LSTM fits residual components, or predicts results through weighted fusion.

2. Implementation Framework of GM+LSTM Combined Model

  • Steps:
  1. GM predicts the original sequence, obtaining the trend component T;
  2. Calculate the residual sequence R;
  3. LSTM trains the residual sequence, predicting the residual value R_pred;
  4. Final prediction: Y = T + R_pred.

3. Code Acquisition

Code acquisition (not for public benefit, please do not take advantage)Combination Prediction Method Based on GM (Grey Model) and LSTM (Long Short-Term Memory Network)

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