This　paper　focuses　on　the　application　of　advanced　neural　network　frameworks,　specifically　Long　Short-Term　Memory　(LSTM)　networks,　to　tackle　complex　time　series　data　prediction　challenges.　It　aims　to　elucidate　the　methodology,　model　architecture,　parameter　optimization,　and　potential　applications　of　LSTM-based　models　in　the　context　of　time　series　forecasting.　Additionally,　the　study　incorporates　supplementary　techniques　such　as　dynamic　windows　to　enhance　prediction　accuracy.　LSTM　networks　are　employed　as　a　central　component　of　this　research　due　to　their　recurrent　nature　and　memory　retention　capabilities,　which　make　them　well-suited　to　capturing　temporal　dependencies　and　patterns　inherent　in　time　series　data.　The　model　architecture　is　thoughtfully　designed,　considering　factors　such　as　the　number　of　LSTM　layers,　hidden　units,　and　dropout　rates,　to　align　with　the　specific　characteristics　of　the　dataset.　Parameter　tuning　is　performed　through　an　extensive　iterative　process,　encompassing　over　200　training　and　validation　iterations,　to　maximize　model　performance.　To　illustrate　the　proposed　methodology＇s　practicality,　the　study　includes　a　real-world　example　involving　＇Wordle＇　lexical　puzzles.　This　application　serves　as　empirical　evidence　of　the　effectiveness　and　applicability　of　LSTM-based　models　in　solving　complex　time　series　prediction　problems.　Looking　forward,　LSTM-based　models　hold　significant　promise　in　various　domains,　including　finance,　weather　forecasting,　and　healthcare,　among　others.　As　computational　resources　continue　to　advance,　the　ability　to　train　more　intricate　LSTM　architectures　offers　the　potential　to　enhance　predictive　accuracy　further.　©　2023　IEEE.