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.
The data shows seasonality. The data should be adjusted. While the Order Norm transformation, provides the best normality, the Yeo Johnson variable will also perform well.
Data is able to be distributed by time but not by geography. The roll up method used is Weighted Average.
Auto Correction Function
Auto Correlation Function After Differencing
Partial Auto Correlation Function
Seasonal and Trend Decompostion
Data does not show strong autocorrectation 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 -0.14
The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.13 p-value = 0.08 indicates that the data is stationary.
The Shapiro-Wilk test returned W = 0.98 with a p-value =0.00 indicating the data does not follow a normal distribution.
A skewness score of -0.31 indicates the data are fairly symmetrical.
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)
Some weather stations, such as the State of Delaware, do not report as frequently as others.
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].