I’ve pushed a minor update to the forecast package to CRAN. Some highlights are listed here.

# Tag / seasonality

# The thief package for R: Temporal HIErarchical Forecasting

I have a new R package available to do temporal hierarchical forecasting, based on my paper with George Athanasopoulos, Nikolaos Kourentzes and Fotios Petropoulos. (Guess the odd guy out there!)

It is called “thief” – an acronym for Temporal HIErarchical Forecasting. The idea is to take a seasonal time series, and compute all possible temporal aggregations that result in an integer number of observations per year. For example, a quarterly time series is aggregated to biannual and annual; while a monthly time series is aggregated to 2-monthly, quarterly, 4-monthly, biannual and annual. Each of the resulting time series are forecast, and then the forecasts are reconciled using the hierarchical reconciliation algorithm described in our paper.

It turns out that this tends to give better forecasts, even though no new information has been added, especially for time series with long seasonal periods. It also allows different types of forecasts for different forecast horizons to be combined in a consistent manner.

# Seasonal periods

I get questions about this almost every week. Here is an example from a recent comment on this blog:

I have two large time series data. One is separated by seconds intervals and the other by minutes. The length of each time series is 180 days. I’m using R (3.1.1) for forecasting the data. I’d like to know the value of the “frequency” argument in the ts() function in R, for each data set. Since most of the examples and cases I’ve seen so far are for months or days at the most, it is quite confusing for me when dealing with equally separated seconds or minutes. According to my understanding, the “frequency” argument is the number of observations per season. So what is the “season” in the case of seconds/minutes? My guess is that since there are 86,400 seconds and 1440 minutes a day, these should be the values for the “freq” argument. Is that correct?

# ABS seasonal adjustment update

Since my last post on the seasonal adjustment problems at the Australian Bureau of Statistics, I’ve been working closely with people within the ABS to help them resolve the problems in time for tomorrow’s release of the October unemployment figures.

Now that the ABS has put out a statement about the problem, I thought it would be useful to explain the underlying methodology for those who are interested. Continue reading →

# Explaining the ABS unemployment fluctuations

Although the *Guardian* claimed yesterday that I had explained “what went wrong” in the July and August unemployment figures, I made no attempt to do so as I had no information about the problems. Instead, I just explained a little about the purpose of seasonal adjustment.

However, today I learned a little more about the ABS unemployment data problems, including what may be the explanation for the fluctuations. This explanation was offered by Westpac’s chief economist, Bill Evans (see here for a video of him explaining the issue). Continue reading →

# Seasonal adjustment in the news

It’s not every day that seasonal adjustment makes the front page of the newspapers, but it has today with the ABS saying that the recent seasonally adjusted unemployment data would be revised.

I was interviewed about the underlying concepts for the *Guardian* in this piece.

Further comment from me about users paying for the ABS data is here.