Conference Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 194 - Time Series in Federal Statistics
Type: Topic Contributed
Date/Time: Monday, August 8, 2022 : 2:00 PM to 3:50 PM
Sponsor: Government Statistics Section
Abstract #323087
Title: The Dark Side of the Moon: Searching for the Other Half of Seasonality
Author(s): Gary Joseph Cornwall* and Jeffrey Chen
Companies: Bureau of Economic Analysis and Bennett Institute for Public Policy: University of Cambridge
Keywords: ARIMA; Simulation Study; Negative Seasonality; Random Forest; Seasonal Adjustment; Residual Seasonality
Abstract:

Seasonality is among the most visible properties in time series data, yet a multitude of statistical tests devised over decades of research have only achieved limited success in its detection. In this paper we examine eight existing tests of seasonality and show that there is significant variation in how they classify a series. We then show how this variation, combined with characteristics of the time series (e.g. auto-correlation, frequency, skewness, kurtosis, etc.), can be exploited by a Random Forest (RF) framework to map the hypothesis test space and make more accurate predictions regarding the seasonal disposition of a series. Our proposed method reduces Type II errors by approximately sixty percentage points over the next best alternative.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2022 program