Prophet and short-term forecasting

Prophet and short-term forecasting

Can Prophet excel at analysing shorter time periods? However, trend, seasonality and changepoints can often be more defined across a longer time series, as longer-term characteristics of the series become more apparent. For this example, Prophet is used to conduct forecasts across two time series.

Prophet is a time series library developed by Facebook. Prophet is particularly adept at modelling a time series with significant seasonal trends, as well as those with various “changepoints” present, i.e. structural breaks in the time series.

However, trend, seasonality and changepoints can often be more defined across a longer time series, as longer-term characteristics of the series become more apparent.

For this example, Prophet is used to conduct forecasts across two time series.

  • Short-term series: Weekly hotel cancellations across 115 weeks of data (July 2015 to August 2017).
  • Long-term series: Monthly air passenger numbers for the airline KLM (enplaned) from the period May 2005 to March 2016 — data sourced from San Francisco Open Data.

While it is debatable as to what specifically consists of a “short” and “long” term time series, it will be assumed for this purpose that the weekly hotel cancellations is a short-term time series, given that there is two years of data — which is likely long enough to extrapolate long-term trend and seasonal factors.

Forecasting Monthly Air Passenger Numbers (Long-Term Series)

This example is elaborated on further in a previous article titled, “Time Series Analysis with Prophet: Air Passenger Data”. The full details of the analysis as well as the relevant link to the GitHub repository containing the dataset and Jupyter Notebook can be found there.

A summary of the results are included here for illustration.

Prophet was able to outperform an ARIMA model in forecasting air passenger numbers.

However, air passenger data typically follows a predictable seasonal pattern — with passenger numbers rising throughout the summer and then descending through the winter months. Moreover, the data under analysis was over the course of a decade, which allows Prophet to factor in long-term trends and seasonality shifts into the analysis.

machine-learning data-science statistics timeseries

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