چکیده مقاله
Accurate forecasting of time series data is a significant challenge thatdemands the capabilities of advanced machine learning models In thisstudy, we introduce an innovative hybrid model combining Long Short Term Memory LSTM with an attention mechanism, further enhancedby the Random Forest algorithm This hybrid approach enhances themodel's ability to learn from complex features and long termdependencies in time series data, providing more precise forecasts Themain purpose of this study is to examine the effect of this modelcombination on improving the accuracy of forecasting time series data We benchmarked the proposed model against several other approaches,including GRU, LSTM, and attention based LSTM models Theperformance of these models was evaluated using RMSE and MAEmetrics on two distinct datasets, revealing that the RF ATLSTM modelachieved the highest accuracy in predicting electricity consumption andfinance forecasting The findings suggest that the integration ofRandom Forest with LSTM and the attention mechanism not onlyboosts the neural network's learning capacity but also significantlyenhances the model's ability to minimize prediction errors
کلیدواژهها
نویسندگان
Ali Kangari
Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
شیوه ارجاع
Kangari, Ali,1403,Enhancing LSTM-based Time Series Forecasting with RandomForest algorithm and Attention Mechanism,The 4th international conference on artificial intelligence and its future prospects in electrical, computer, mechanical and telecommunication engineering sciences,Mashhad
ارائهشده در
مجموعه مقالات چهارمین کنفرانس بین المللی هوش مصنوعی و چشم انداز آینده آن در علوم مهندسی برق ، کامپیوتر ، مکانیک و مخابرات18 آذر 1403 · مشهد