Why Use This Data Source In Your Models?
The industrial production index is an economic indicator that measures the real production output of manufacturing, mining, and utilities. This is indicative of the overall economic situation and GDP in the United States.
Industrial Production Index
Board of Governors of Fed Reserve System
Industrial Production and Capacity Utilization
Index 2012=100, Seasonally Adjusted
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.
Data is unable to be distributed by time or geography. The roll up method used is Weighted Average.
Data shows auto correlation indicating a need for differencing
The ACF indicates 1 order differencing is appropriate.
Further differencing is reccommended
The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.33 p-value = 0.01 indicates that the data is not stationary.
The Shapiro-Wilk test returned W = 0.94 with a p-value =0.00 indicating the data does not follow a normal distribution.
A skewness score of 0.37 indicates the data are fairly symmetrical.
Hartigan's dip test score of 0.04 with a p-value of 0.19 inidcates the data is unimodal
Auto Correlation Function
Auto Correlation Function After Differencing
Partial Auto Correlation Function
Seasonal and Trend Decompostion
Board of Governors of the Federal Reserve System (US), Industrial Production Index [INDPRO], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/INDPRO, December 15, 2019.
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.
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.
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.
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.