Missing time-series vs. Empty time-series

Lokad is about time-series forecasting, but as simple as the time-series model may seem to be (after all a time-series is nothing more than a list of time-value pairs), there are several subtleties in the way to manage time-series. In this post, we will see how the Lokad time-series model distinguishes missing time-value pairs from empty time-value pairs. Since the topic is slightly complex, I would suggest, if you’re not familiar the Lokad technology, to have a look at our User Guide (in particular, the Forecasting tasks section).

A practical situation

Let’s start with a practical real-life situation; let’s assume that we have a time-series that include 12 time-values, one value for each month of the year 2005 (starting January 2005, ending December 2005). We can imagine that this time-series represent the monthly sales of a web shop. At the time I am writing this post, it’s the beginning of January 2007. What happen if I insert now this time-series into my Lokad account and ask for a monthly forecast? Well, there is an ambiguity in the time-series model, because there would be two possibilities:

Let’s make the things clear: Lokad has chosen the data-centric approach, if ask a monthly forecast for your 12 time-values ranging from January 2005 to December 2005, you will get a forecast for January 2006, no matter if you request the forecast at the beginning of 2006 or in a distant future. Lokad takes the last time-value pair of your time-series as a reference to compute the forecasts. This option has been chosen because we believe it’s closer to the business requirements

Some arguments supporting the data-centric approach

Let’s review the arguments in favor of the data-centric approach:

Yet, this approach involves a minor drawback: you need to handle explicitly the lack of data. For example, in the previous web shop situation, each product of the catalog may not have be sold even once a month. In such case, you must explicitly add a zero time-value in your time-series that represent this lack of sales.


Reader Comments (2)

We are using a technology developed internally at Lokad. The main reason being that our requirements are quite different from what is generally available for time-series analysis (especially in terms of scalability). Also, the Lokad technology is anything but “definitive”. We continuously monitor the forecast performance of our algorithms based on the available customer data. Those benchmarks help us to improve our algorithms in a continuous manner. 11 years ago | Joannes Vermorel


Interesting concept - applying social computing to forecasting. I would think the social hurdles are difficult but not insurmountable. Some years back I did some work at a research firm. They did industry forecasts using surveys, then gave the results back to the participants. (then they sold reports on the forecasts). What tools are you using? Open Source or something like MATLAB or Maple? Michael 11 years ago | Michael