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Related Experiment Videos

Application of Improved LSTM Algorithm in Macroeconomic Forecasting.

Shijun Chen1, Xiaoli Han2, Yunbin Shen1

  • 1School of Economics and Management, Tongji University, Shanghai 200092, China.

Computational Intelligence and Neuroscience
|November 10, 2021
PubMed
Summary
This summary is machine-generated.

The Long Short-Term Memory (LSTM) model significantly outperforms the Autoregressive Integrated Moving Average (ARIMA) model in predicting agricultural futures prices. This finding aids in economic forecasting and macro control.

Related Experiment Videos

Area of Science:

  • Agricultural Economics
  • Financial Forecasting
  • Time Series Analysis

Background:

  • Agricultural futures indices offer insights into macroeconomic trends and potential crises.
  • Accurate prediction of agricultural futures prices is crucial for government economic forecasting and policy.
  • Traditional time series models and artificial intelligence-based neural networks are common approaches for price prediction.

Purpose of the Study:

  • To compare the predictive performance of the Autoregressive Integrated Moving Average (ARIMA) model and the Long Short-Term Memory (LSTM) model for agricultural futures prices.
  • To evaluate the effectiveness of a trading strategy based on the predictions of both models.
  • To determine which model provides a more advantageous prediction for the agricultural futures market.

Main Methods:

  • Utilized closing price data for agricultural futures indices from January 10, 2012, to February 27, 2020.
  • Applied the Autoregressive Integrated Moving Average (ARIMA) time series model.
  • Implemented the Long Short-Term Memory (LSTM) neural network model.
  • Compared prediction accuracy using defined metrics and evaluated trading strategy performance.

Main Results:

  • The Long Short-Term Memory (LSTM) model demonstrated a clear advantage over the Autoregressive Integrated Moving Average (ARIMA) model in predicting agricultural futures price indices.
  • Comparative analysis of trading strategies based on model predictions indicated superior performance for the LSTM model.

Conclusions:

  • The LSTM model is a more effective tool for predicting agricultural futures prices compared to the traditional ARIMA model.
  • Enhanced predictive accuracy using LSTM can lead to improved trading strategies and better market insights.
  • Further research into advanced AI models for agricultural futures market prediction is warranted.