Prediction of Lithium Carbonate Prices in China Applying a VMD–SSA–LSTM Combined Model

Given the highly nonlinear and unstable characteristics of lithium carbonate prices in China’s lithium battery industry chain, the accuracy of a single prediction model is limited. This study introduces the prices of related materials in the lithium battery industry and macro-environmental indicators as key influencing factors. This study utilizes Variational Mode Decomposition (VMD) and the Sparrow Search Algorithm (SSA) to further develop the Long Short-Term Memory (LSTM) network, resulting in a VMD–SSA–LSTM combination model for predicting lithium carbonate pricing. The research results indicate that (1) using the VMD to decompose the time series of the original lithium carbonate prices can accurately extract the core features of the prices and significantly weaken the instability of the data; (2) by leveraging SSA to perform global optimization on the three parameters of the LSTM model and fitting the optimal parameters into the LSTM network, the generalization ability and robustness of the model are enhanced; (3) on the lithium carbonate dataset, the VMD–SSA–LSTM model outperforms the typical LSTM and VMD–LSTM models, achieving the lowest prediction error and a goodness-of-fit (R2) of 0.9880, demonstrating a higher prediction accuracy for lithium carbonate prices. This study presents more precise benchmarks for resource optimization and price decisions in the lithium carbonate industry.