Time series analysis involves studying and analyzing data points collected over time to uncover patterns, trends, and relationships within the data. This analysis often includes techniques such as Time Series Decomposition, Stationarity and Differencing, Autocorrelation and Partial Autocorrelation analysis, and Lag Selection to understand the underlying structure of the time series data. Time series forecasting models like ARIMA, Exponential Smoothing, and Prophet are commonly used to make predictions based on historical data. Additionally, advanced methods such as Machine Learning algorithms, including Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), are employed for more complex time series analysis tasks.
