Time series analysis is a vital method for analyzing data that changes over time. This approach includes several important aspects, such as identifying trends, recognizing repeating patterns (known as seasonality), and noting longer-term fluctuations (cyclic patterns). To refine the analysis, techniques like moving averages and exponential smoothing are used to highlight trends. Another valuable technique is decomposition, which separates the data into its core elements: trend, seasonality, and residual, allowing for a more transparent analysis.
To ensure the data is stationary, meaning its statistical characteristics are constant over time, methods like differencing or transformations are often necessary. The use of autocorrelation and partial autocorrelation functions is key in uncovering dependencies and relationships between data points at varying time intervals.
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