READY SIGNAL CONTROL DATA

PPI: All Commodities

Why Use This Data Source In Your Models?

The producer price index for all commodities represents the average movement in selling prices from domestic production of all goods over time. This indicates supply and demand for goods, disposable income, and real estate prices.

PPI: All Commodities

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 auto correlation and a non-normal distribution. The data should be differenced. While the Order Norm transformation, provides the best normality, the Yeo Johnson variable will also perform well.

Grain Transformation:

Data is unable to be distributed by time or geography. The roll up method used is Weighted Average.

Source:
U.S. Bureau of Labor Statistics

Release:
Producer Price Index

Units:
Index 1982=100, Not Seasonally Adjusted

Frequency:
Monthly

Available Through:
04/30/2023

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 Yeo Johnson variable will also perform well.

Grain Transformation:

Data is unable to be distributed by time or geography. The roll up method used is Weighted Average.

Auto Correlation Analysis:

Data shows auto correlation indicating a need for differencing

The ACF indicates 2 order differencing is appropriate.

Further differencing is reccommended

Trend Analysis:

The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.45 p-value = 0.01 indicates that the data is not stationary.

Distribution Analysis:

The Shapiro-Wilk test returned W = 0.73 with a p-value =0.00 indicating the data does not follow a normal distribution.

A skewness score of 2.01 indicates the data are substantially skewed.

Hartigan's dip test score of 0.02 with a p-value of 0.94 inidcates the data is unimodal

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

No transform
9.65
Box-cox
7.86
Log_b(x-a)
8.21
sqrt(x+a)
8.40
exp(x)
144.46
arcsinh(x)
8.21
Yeo-Johnson
6.36
OrderNorm
0.09

Auto Correlation Function

Auto Correlation Function After Differencing

Partial Auto Correlation Function

Seasonal Impact

Seasonal and Trend Decompostion

Citation:

U.S. Bureau of Labor Statistics, Producer Price Index for All Commodities [PPIACO], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/PPIACO, December 15, 2019.

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.

From data ingestion to applying data science treatments to control data, Let Ready Signal save you time and effort in your analytics and modeling, helping you reduce error and improve model accuracy.