Forecast - Minimum Temperature
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
Suggested Treatment:
Grain Transformation:
Source:
NOAA
Release:
Forecasted Weather
Units:
Degrees Celsius
Frequency:
Daily
Available Through:
10/04/2023
Suggested Treatment:
The data shows seasonality. The data should be adjusted. While the Order Norm transformation, provides the best normality, the Square Root 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 does not show strong auto correlation indicating no need for differencing
The ACF indicates 0 order differencing is appropriate.
Further differencing is reccommended
Trend Analysis:
The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.01 p-value = 0.10 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 0.00 indicates the data are fairly symmetrical.
Hartigan's dip test score of 0.04 with a p-value of 0.12 inidcates the data is unimodal
Statistics (Pearson P/ df, lower => more normal)
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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