Multiplicative decomposition is a method used in time series analysis to separate a series into its constituent components: trend, seasonal, and irregular components. It assumes that these components multiply together to form the observed data, making it particularly suitable for data where variations increase proportionally with the level of the series.

Multiplicative Decomposition

Multiplicative decomposition

Time Series Analysis involves the study of data points collected or recorded at specific time intervals to identify patterns, trends, and seasonal variations. It is crucial for forecasting future values and making informed decisions in various fields like finance, weather forecasting, and economics.

time series analysis

constituent components

trend

Seasonal components

irregular components

Observed data refers to information collected through direct measurement or observation of a phenomenon, often used as the foundation for statistical analysis and empirical research. It is crucial for validating hypotheses and building models that reflect real-world conditions and behaviors.

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