🔍 Find out how LSTM Time Series Forecasting transformed the approach to data prediction—and why it beats traditional models For decades, time series forecasting was synonymous with models like AR, MA, and ARIMA—mathematical frameworks built on assumptions of linearity, stationarity, and seasonality. While powerful in their domain, these models often struggled with nonlinear patterns, sudden regime shifts, and multivariate complexity found in modern datasets. Enter LSTM (Long Short-Term Memory)—a specialized neural network architecture designed to capture long-range dependencies in sequential data. Unlike traditional models, LSTMs don’t require manual feature engineering or rigid assumptions. They learn directly from the data—detecting trends,…