Time series analysis involves examining patterns and dependencies within a sequence of data points collected over time. The process starts with collecting and visualizing time-stamped data to identify trends and outliers. An initial understanding of the data is gained through descriptive statistics such as mean and standard deviation.

Decomposition techniques break down the time series into components like trend, seasonality, and residual error. Ensuring stationarity, often through differencing, is crucial for many time series models. To understand the temporal dependencies in the data, autocorrelation and partial autocorrelation functions are used. Model selection involves choosing appropriate models such as ARIMA or SARIMA based on the characteristics of the time series. For more complex patterns, machine learning models like Random Forests or LSTM can be employed.

Evaluation metrics such as Mean Squared Error or Mean Absolute Error are used to assess the accuracy of the model on a test set. Once the model is trained, it can be used for forecasting future values. Continuous monitoring of model performance is essential, and periodic updates with new data ensure the model remains relevant. The process is dynamic, and the choice of techniques depends on the specific nature and goals of the time series analysis. Various tools and libraries such as pandas and statsmodels in Python or their counterparts in R facilitate the implementation of these techniques.