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Why Use This Data Source In Your Models?

The COVID‑19 pandemic is an ongoing global pandemic of coronavirus disease 2019 (COVID‑19), caused by severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2). From the business focused data scientist there are three main measures to inform the economic impact- cases diagnosed, available hospital beds and recovered cases.

Seasonal Impact

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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 Boxcox variable will also perform well.

Grain Transformation:

Data is unable to be distributed by time or geography. The roll up method used is Max.

Auto Correlation Analysis:

Data shows auto correlation indicating a need for differencing

The ACF indicates 2 order differencing is appropriate.

Further differencing is reccommended

Trend Analysis:

The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.51 p-value = 0.01 indicates that the data is not stationary.

Distribution Analysis:

The Shapiro-Wilk test returned W = 0.93 with a p-value =0.00 indicating the data does not follow a normal distribution.

A skewness score of -0.56 indicates the data are moderately skewed.

Hartigan's dip test score of 0.03 with a p-value of 0.86 inidcates the data is unimodal

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

No transform

4.17

Box-cox

1.31

Log_b(x-a)

1.65

sqrt(x+a)

1.48

exp(x)

arcsinh(x)

1.65

Yeo-Johnson

1.31

OrderNorm

1.15

Auto Correlation Function

Auto Correlation Function After Differencing

Partial Auto Correlation Function

Seasonal and Trend Decompostion

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