About decomposition time series
This is an article about decomposed time series.
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Today we will learn about decomposed time series.
Decomposed time series analysis is a method of decomposing time series data into trend, seasonality, cycle, and random factor (Error) and analyzing each component.
We will explain the decomposition time series below.
Purpose of decomposed time series analysis
Understanding the components:
Understand the characteristics and volatility of trends, seasonality, cycles, and random factors that make up time series data.
Building a prediction model
Each component is analyzed to build a predictive model and use it to predict future values.
Understand the characteristics of your data
By identifying the characteristics of data, we understand trends and cycles and analyze changes according to trends.
Types of decomposed time series analysis
Additive Model
A method of decomposing a time series into the sum of trend, seasonality, cycle, and random factors, expressed as Y(t) = T(t) + S(t) + C(t) + E(t).
Multiplicative Model
A method of decomposing a time series into the product of trend, seasonality, cycle, and random factors, expressed as Y(t) = T(t) _ S(t) _ C(t) * E(t).
Steps of decomposition time series analysis
Visualization of time series data
Identify patterns of trends, seasonality, cycles, and random factors in time series data.
Trend estimation
Estimate long-term trends from time series data.
Seasonality estimation
Infer seasonal patterns from time series data.
Cycle estimation
Estimate long-term periodic elements from time series data.
Random factor (Error) analysis
Analyzes fluctuations other than trends, seasonality, and cycles.
uses
Understand the characteristics of time series data
Understand the structure of data by identifying the characteristics of data trends, seasonality, cycles, and random factors.
Building a prediction model
It is used to analyze each component to predict future values ββand build a predictive model.
Conclusion
Decomposed time series analysis is an important technique for identifying and predicting patterns and structures of time series data, and is actively used in various fields.
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