Why Use This Data Source In Your Models?
Motor vehicle sales: Heavy Weight Trucks indicates the number of drivers, number of vehicles, and vehicle supply and demand.
Heavy Weight Truck Retail Sales - Seas Adj
U.S. Bureau of Economic Analysis
Millions of Units, Seasonally Adjusted Annual Rate
The data shows auto correlation and a non-normal distribution. The data should be differenced. While the Yeo Johnson transformation, provides the best normality, the Log variable will also perform well.
Data is able to be distributed by time but not by geography. The roll up method used is Sum.
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 -0.31
The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.23 p-value = 0.01 indicates that the data is not stationary.
The Shapiro-Wilk test returned W = 0.96 with a p-value =0.01 indicating the data does not follow a normal distribution.
A skewness score of 0.43 indicates the data are fairly symmetrical.
Hartigan's dip test score of 0.04 with a p-value of 0.54 inidcates the data is unimodal
Auto Correlation Function
Auto Correlation Function After Differencing
Partial Auto Correlation Function
Seasonal and Trend Decompostion
U.S. Bureau of Economic Analysis, Motor Vehicle Retail Sales: Heavy Weight Trucks [HTRUCKSSAAR], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/HTRUCKSSAAR, December 16, 2019.
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