My work on long-short-term memory (LSTM) networks has provided valuable insights. A characteristic ability of LSTMs is to deal with extended dependencies in sequential data, which is a common challenge. Their integrated memory element and three dedicated gates—forget, input, and output—allow LSTMs to retain or discard data. This allows them to collect important information through long series, which turns out to be very useful for natural language processing and my time series projects. In addition, the study of time series models has been rewarding. Time series analysis assumes that data points collected over time are related and order is important. I mainly focused on two types of time series models: univariate and multivariate. Univariate models such as ARIMA and exponential smoothing highlight trends and seasonality in individual variables, while multivariate models such as Vector Autoregression (VAR) and Structural Time Series provide a bigger picture by looking at several interrelated variables. I will try to express the key points in my own words keeping the meaning you conveyed about LSTM networks and time series analysis.