READY SIGNAL CONTROL DATA

Disposable Income

Why Use This Data Source In Your Models?

Disposable income measures the total income remaining after deduction of taxes & other mandatory charges. This is used to indicate American's financial health and predict consumer behavior and economic growth.

Disposable Income

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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 geography but not by time. 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 -0.54

Trend Analysis:

The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.13 p-value = 0.08 indicates that the data is stationary.

Distribution Analysis:

The Shapiro-Wilk test returned W = 0.95 with a p-value =0.00 indicating the data does not follow a normal distribution.

A skewness score of 0.60 indicates the data are moderately skewed.

Hartigan's dip test score of 0.05 with a p-value of 0.03 inidcates the data is multimodal

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

No transform

4.92

Box-cox

2.55

Log_b(x-a)

3.59

sqrt(x+a)

4.42

exp(x)

arcsinh(x)

3.59

Yeo-Johnson

2.25

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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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

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Data Ingestion

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