In today’s time series analysis class, I learned about key elements and methods crucial for interpreting temporal data. We explored trend analysis, focusing on recognizing whether data exhibits a rising, falling, or constant trend over time. Another important aspect covered was seasonality, which involves understanding and adjusting for repetitive patterns at regular intervals, such as weekly, monthly, or yearly occurrences. Additionally, the concept of stationarity was emphasized, highlighting that a time series is considered stationary when statistical properties remain consistent over time, a prerequisite for many models. Finally, we discussed popular models like ARIMA, SARIMA, and advanced machine learning models like LSTM networks, providing valuable tools for forecasting, pattern recognition, and analyzing the impact of different factors on time-related data.