LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies
By Kamil Kashif, Robert Ślepaczuk
Rating
1239
Battle Count: 228
Relevance
9/10
Directly applicable to algorithmic trading strategies with promising results on major equity indices
Implementation Complexity
7/10
Requires implementation of both ARIMA and LSTM models, as well as their integration and walk-forward optimization
Reproducibility
4/5
Detailed methodology and hyperparameters provided, but exact dataset splits not specified
About this paper
Methodology: LSTM-ARIMA Hybrid. Problem types: Time Series Forecasting, Algorithmic Trading.
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