READY SIGNAL CONTROL DATA

Total Public Debt

Why Use This Data Source In Your Models?

The total public debt meausres the amount of money that the US government owes to outside entites. This is indicative of ??

Total Public Debt

Instantly Download this Data Using Our Automated Feature Engineering Tool.

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 Boxcox transformation, provides the best normality, the Yeo Johnson variable will also perform well.

Grain Transformation:

Data is able to be distributed by time but not by geography. The roll up method used is Sum.

Source:
U.S. Dept. of the Treasury, Fiscal Service

Release:
Federal Debt

Units:
Millions of Dollars, Not Seasonally Adjusted

Frequency:
Quarterly, end of period

Available Through:
12/31/2022

Suggested Treatment:

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

Grain Transformation:

Data is able to be distributed by time but not by geography. The roll up method used is Sum.

Auto Correlation Analysis:

Data shows auto correlation indicating a need for differencing

The ACF indicates 2 order differencing is appropriate.

Following first order differencing, no further differencing is required based on the differenced ACF at lag one of -0.03

Trend Analysis:

The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.25 p-value = 0.01 indicates that the data is not stationary.

Distribution Analysis:

The Shapiro-Wilk test returned W = 0.89 with a p-value =0.00 indicating the data does not follow a normal distribution.

A skewness score of 0.80 indicates the data are moderately skewed.

Hartigan's dip test score of 0.05 with a p-value of 0.69 inidcates the data is unimodal

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

No transform
3.00
Box-cox
1.22
Log_b(x-a)
1.41
sqrt(x+a)
1.33
exp(x)
NA
arcsinh(x)
1.41
Yeo-Johnson
1.31
OrderNorm
1.62

Auto Correlation Function

Auto Correlation Function After Differencing

Partial Auto Correlation Function

Seasonal Impact

Seasonal and Trend Decompostion

Citation:

U.S. Department of the Treasury. Fiscal Service, Federal Debt: Total Public Debt [GFDEBTN], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/GFDEBTN, December 19, 2019.

Designed For Data Scientists and Analysts

400+ Data Sources

Use our platform to aggregate, normalize, and profile open source and premium control data. Spend less time finding and wrangling data, and more time building efficient and feature-rich machine learning data pipelines.

Data Science Treatments

Instantly apply industry-standard
data science treatments and transformations, including (but not limited to) Differencing, Lead/Lag, Box Cox. Easily manipulate data across different time and geographic grains.

Auto Discovery

Our Patent Pending iterative testing engine allows you to upload your target variable, and the platform will test for possible statistical relationships across all available data sources. Saving you time and removing analyst bias.

Data Ingestion

Easily integrate your Ready Signal data to the data science platform of your choice. Connect directly to Ready Signal through our API or using one of our pre-built data connectors or download directly in Excel or CSV format.

From data ingestion to applying data science treatments to control data, Let Ready Signal save you time and effort in your analytics and modeling, helping you reduce error and improve model accuracy.