READY SIGNAL CONTROL DATA

Personal Consumption Expenditures: Durable Goods

Why Use This Data Source In Your Models?

Personal consumption expenditures on durable goods measures the amount of disposable income, indicating wages and economic health.

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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 Order Norm 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 -0.10

Trend Analysis:

The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.45 p-value = 0.01 indicates that the data is not stationary.

Distribution Analysis:

The Shapiro-Wilk test returned W = 0.86 with a p-value =0.00 indicating the data does not follow a normal distribution.

A skewness score of 1.07 indicates the data are substantially skewed.

Hartigan's dip test score of 0.04 with a p-value of 0.28 inidcates the data is unimodal

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

No transform

5.80

Box-cox

4.78

Log_b(x-a)

5.59

sqrt(x+a)

6.02

exp(x)

arcsinh(x)

5.59

Yeo-Johnson

3.06

OrderNorm

0.02

Auto Correlation Function

Auto Correlation Function After Differencing

Partial Auto Correlation Function

Seasonal Impact

Seasonal and Trend Decompostion

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