READY SIGNAL CONTROL DATA

Forecast - Snow Cover

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

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 seasonality. The data should be adjusted. While the Order Norm transformation, provides the best normality, the Log variable will also perform well.

Grain Transformation:

Data is able to be distributed by time and geography. The roll up method used is Sum.

Source:
NOAA

Release:
Forecasted Weather

Units:
mm

Frequency:
Daily

Available Through:
03/30/2024

Suggested Treatment:

The data shows seasonality. The data should be adjusted. While the Order Norm transformation, provides the best normality, the Log 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 does not show strong auto correlation indicating no need for differencing

The ACF indicates 0 order differencing is appropriate.

Following first order differencing, no further differencing is required based on the differenced ACF at lag one of -0.24

Trend Analysis:

The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.05 p-value = 0.10 indicates that the data is stationary.

Distribution Analysis:

The Shapiro-Wilk test returned W = 0.53 with a p-value =0.00 indicating the data does not follow a normal distribution.

A skewness score of 3.49 indicates the data are substantially skewed.

Hartigan's dip test score of 0.02 with a p-value of 0.99 inidcates the data is unimodal

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

No transform
43.81
Box-cox
NA
Log_b(x-a)
23.39
sqrt(x+a)
24.76
exp(x)
150.51
arcsinh(x)
40.26
Yeo-Johnson
25.18
OrderNorm
20.20

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].

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.

Scroll to Top