READY SIGNAL CONTROL DATA

Heavy Weight Truck Retail Sales - Seas Adj

Why Use This Data Source In Your Models?

Sales of heavy weight trucks reflect business confidence and outlook. When the economy turns weak, companies are the first to react by lowering spending and procurement, causing heavy logistics to decrease. Therefore, in the past five economic cycles in the U.S., the index has preceded the occurrence of recessions. Conversely, when economic conditions improves, the purchase of trucks would rebound.

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Suggested Treatment:

The data shows auto correlation and a non-normal distribution. The data should be differenced. 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 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 -0.28

Trend Analysis:

The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.16 p-value = 0.04 indicates that the data is not stationary.

Distribution Analysis:

The Shapiro-Wilk test returned W = 0.98 with a p-value =0.07 indicating the data follows a normal distribution.

A skewness score of 0.08 indicates the data are fairly symmetrical.

Hartigan's dip test score of 0.04 with a p-value of 0.28 inidcates the data is unimodal

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

No transform

2.11

Box-cox

1.55

Log_b(x-a)

1.30

sqrt(x+a)

1.55

exp(x)

2.21

arcsinh(x)

1.72

Yeo-Johnson

1.72

OrderNorm

0.13

Auto Correlation Function

Auto Correlation Function After Differencing

Partial Auto Correlation Function

Seasonal Impact

Seasonal and Trend Decompostion

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