READY SIGNAL CONTROL DATA

Forecast - Snowfall

Why Use This Data Source In Your Models?

Weather is utilized in order to provide a measure of behavioral changes based on variations. This can include both severe weather as well as overall shits in weather as a dynamic form of seasonality.

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 Square Root transformation, provides the best normality, the Arcsin 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 Weighted Average.

Source:
NOAA

Release:
Forecasted Weather

Units:
mm

Frequency:
Daily

Available Through:
10/03/2022

Suggested Treatment:

The data shows auto correlation and a non-normal distribution. The data should be differenced. While the Square Root transformation, provides the best normality, the Arcsin 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 Weighted Average.

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 -0.39

Trend Analysis:

The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.14 p-value = 0.07 indicates that the data is stationary.

Distribution Analysis:

The Shapiro-Wilk test returned W = 0.30 with a p-value =0.00 indicating the data does not follow a normal distribution.

A skewness score of 5.92 indicates the data are substantially skewed.

Hartigan's dip test score of 0.02 with a p-value of 0.00 inidcates the data is multimodal

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

No transform
172.59
Box-cox
NA
Log_b(x-a)
176.82
sqrt(x+a)
170.73
exp(x)
NA
arcsinh(x)
172.48
Yeo-Johnson
178.65
OrderNorm
173.41

Auto Correlation Function

Auto Correlation Function After Differencing

Partial Auto Correlation Function

Seasonal and Trend Decompostion


Data Notes:

Some weather stations, such as the State of Delaware, do not report as frequently as others.

Citation:

Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), Version 3.27; NOAA National Climatic Data Center. http://doi.org/10.7289/V5D21VHZ [access date].

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