READY SIGNAL CONTROL DATA

GDP, Total US - Seas Adj Annual Rate

Why Use This Data Source In Your Models?

Gross Domestic Product (GDP) in the United States, adjusted for seasonal fluctuations, stands as a cornerstone metric for economists, policymakers, and businesses alike. This figure represents the total monetary value of all goods and services produced within the country's borders, providing a comprehensive snapshot of the nation's economic health. GDP is indicative of recessions, wages, & the employment situation. Data scientists rely on GDP data to analyze long-term economic trends, offering invaluable insights into the economy's growth trajectory and stability. Policymakers utilize this metric to formulate fiscal and monetary policies, ensuring they align with the nation's economic performance. For businesses, GDP data serves as a critical indicator of market demand, aiding in strategic planning and investment decisions. Additionally, investors use GDP figures to assess overall economic vitality, guiding them in portfolio management. Economists rely on this data to compare economic performance globally and to make predictions about future economic developments. In essence, GDP, Total US - Seas Adj Annual Rate, offers a panoramic view of the nation's economic landscape, empowering stakeholders to make informed decisions and navigate the complexities of the ever-changing economic environment.

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Automated Data Profiling

Ready Signal automatically profiles each data set and offers up suggested industry standard data science treatments to utilize with these data in your models.

Suggested Treatment:

The data shows auto correlation and a non-normal distribution. The data should be differenced. While the Untransformed transformation, provides the best normality, the Yeo Johnson variable will also perform well.

Grain Transformation:

Data is able to be distributed by time and geography. The roll up method used is Sum.

Auto Correlation Analysis:

Data shows auto correlation indicating a need for differencing

The ACF indicates 1 order differencing is appropriate.

Following first order differencing, no further differencing is required based on the differenced ACF at lag one of -0.13

Trend Analysis:

The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.12 p-value = 0.10 indicates that the data is stationary.

Distribution Analysis:

The Shapiro-Wilk test returned W = 0.95 with a p-value =0.08 indicating the data follows a normal distribution.

A skewness score of 0.53 indicates the data are moderately skewed.

Hartigan's dip test score of 0.04 with a p-value of 0.90 inidcates the data is unimodal

Statistics (Pearson P/ df, lower => more normal)

No transform

0.80

Box-cox

1.28

Log_b(x-a)

1.68

sqrt(x+a)

1.68

exp(x)

arcsinh(x)

1.68

Yeo-Johnson

1.22

OrderNorm

1.65

Auto Correlation Function

Auto Correlation Function After Differencing

Partial Auto Correlation Function

Seasonal Impact

Seasonal and Trend Decompostion

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