Seasonally Adjusted

Seasonally adjusted data is a statistical technique used to remove the seasonal component of a time series. It is used to compare data from different months or quarters, and to compare data from different years. Seasonally adjusted data is used to identify underlying trends in the data, and to make more accurate forecasts.

History of Seasonally Adjusted Data

Seasonally adjusted data has been used since the early 20th century. The first use of seasonally adjusted data was in the United States in the 1920s, when the Bureau of Labor Statistics began to adjust the data for seasonal variations. Since then, seasonally adjusted data has been used in many different fields, including economics, finance, and marketing.

Seasonally adjusted data is used to identify underlying trends in the data, and to make more accurate forecasts. Seasonally adjusted data is used to compare data from different months or quarters, and to compare data from different years. Seasonally adjusted data is also used to identify seasonal patterns in the data, and to make more accurate predictions about future trends.

Table of Comparisons

Summary

Seasonally adjusted data is a statistical technique used to remove the seasonal component of a time series. It is used to compare data from different months or quarters, and to compare data from different years. Seasonally adjusted data is used to identify underlying trends in the data, and to make more accurate forecasts. Seasonally adjusted data has been used since the early 20th century, and is used in many different fields. For more information about seasonally adjusted data, visit websites such as the Bureau of Labor Statistics, the Federal Reserve Bank, and the World Bank.

See Also

• Time Series
• Trend Analysis
• Forecasting
• Regression Analysis
• Time Series Decomposition
• Moving Averages
• Exponential Smoothing
• Seasonal Indexes
• Seasonal Adjustment Factors
• Seasonal Autoregressive Integrated Moving Average (SARIMA)

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