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- Re: Understanding Forecast Distance

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Hi Folks, Can you please share a detailed info about the forecast distance in Time series. Like what is it? how it is interpreted? . Along with that Can some one explain me about, how to interpret forecast distance in the following - 1. When we get the prediction using forecast point. (Future forecast) 2. When we get the prediction using forecast range. (Historic prediction) Please do let me know if I shall explain any point in detail to help you in helping me.

amodi

Image Sensor

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4 weeks ago

7 Replies

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lhaviland

Community Team

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4 weeks ago

Have you seen some of the community content for setting/using forecast distance? Here are a few things that may help:

Time Series—Classification

Demand Forecast—Multiseries

Data Setup for Time Series Predictions

Hoping other communuity members can help you out too.

-linda

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katychowhaynie

Data Scientist

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4 weeks ago

Hi Amodi!

Let's set up a few parameters and then talk through how to interpret forecast distance. First, let's pretend in your set up you are predicting 1 to 7 days out. We will use today as our forecast point. How I like to think about forecast distance is if we made the prediction today you will have a prediction for tomorrow, the day after, ... all the way to 7 days from today. Tomorrow would be a forecast distance of 1, the day after will be forecast distance from 2, and 7 days from now will be a forecast distance of 7. Now, let's pretend we are making the prediction again with tomorrow being our forecast point. The day after tomorrow's forecast distance is now 1. How I generally think of forecast distance is from the point in time you are making the prediction how far into the future are you predicting.

When you are predicting for a forecast range let's say last week, every one of those days from last week can have a prediction from different points in time from the previous week meaning that for Monday of last week you can have a forecast distance of 1 to 7.

Hope this helps!

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Hi Katy, Thanks a lot for taking taking out your precious time to answer this. It really helps. In follow up to the answer I would also like to understand the exportable output file that we get from DR's predict tab or via python API for historic data (i.e. using Prediction Range). Premise - Let's take a dataset that has both quarterly and monthly seasonality with derivation window of 12 weeks and forecast window of 12 weeks. And we make a prediction using prediction range. In that case we will get an output that will have 12 forecast point for each weekly date. My doubt here is - how should we get the final trend line using this data because we have 2 seasonality and given the auto correlation based on seasonality (monthly and quarterly) we will have to use different forecast points. I am just trying to relate to classic ARIMA based forecast, where we get a single line and not based on multiple forecast points. Please feel free to correct me if I am wrong anywhere as I am new to time series world.

amodi

Image Sensor

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4 weeks ago

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katychowhaynie

Data Scientist

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4 weeks ago

Hi Amodi!

What a thoughtful question! Inside DataRobot, when we create all of the other time series derived features, it takes into account the quarterly and monthly seasonality if you set the feature derivation window far back enough to span that time. When you make a range prediction, the question you are trying to ask is which forecast points do you use to determine the final trends. I hope I am understanding that correctly. Two things to keep in mind when making those predictions are at what point in time will you make the prediction and when will you use them. If you are making a weekly prediction that will forecast out 12 weeks in advance every time, wouldn't you just look at the first forecast distance for every week that you made the prediction since that contains the most information about the following week? If you are making a prediction this week for the next 12 weeks and you will not be making another prediction until 12 weeks from now then you would use all 12 forecast distances. I think using the range predictions is good for historical data, but if you are truly predicting into the future then I would use a single forecast point. Again, keep in mind how often you will make predictions, and when you will execute on these predictions and that should help you decide what to use.

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The historical / forecast range predictions are generally used as part of a model evaluation process. An example would be training the model on 2019 data and then seeing how it would have preformed in the first half of 2020. To do this would require stepping through 26 forecasts, assuming you were making a forecast every week (i.e. W1 forecast out 12 weeks, W2 forecast out 12 weeks, W3 forecast out 12 weeks, etc.). We do that stepping process automatically to generate for every week, what were the 12 different forecasts (1 week out, 2 weeks out, 3 weeks out, etc.) so that you can do your own analysis (e.g. to compare to a different forecasting methodology, to look for issues, to estimate ROI etc.).

MSteadman

DataRobot Employee

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4 weeks ago

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amodi

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4 weeks ago

Thanks a lot for time to answer this query.

Yes, I understand that historical / forecast range predictions are generally used as part of a model evaluation process and various forecast distance are going to be used for historic analysis. But how does a forecast distance is related back to my forecast (future unseen prediction). For ex. - I set the derivation and forecast window to 12 and, In my analysis I found out that forecast distance 7 was able to capture a better trend with good score of metric. Then how do I use THAT (forecast distance 7) in making my prediction and should I?

I am sorry but I have few scenarios where i would want more clarity-

- how would we relate to a data that has 2 seasonality monthly and quarterly. Lets take an example for sales - What if sales on 30th June is auto-correlated to previous quarter sales and previous month sale. In this scenario how should we choose a forecast point.
- What if there is no seasonality and no trend (random time series), the also would it make sense to use forecast points.
- What if there is a cyclic seasonality which is too big for our derivation window to catch.

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MSteadman

DataRobot Employee

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4 weeks ago

> how would we relate to a data that has 2 seasonality monthly and quarterly. Lets take an example for sales - What if sales on 30th June is auto-correlated to previous quarter sales and previous month sale. In this scenario how should we choose a forecast point.

The forecast point is related to describing what is in the future vs. what is in the past. Say you are trying to make a forecast for the 30th from the 23rd. Then I would have the 23rd be the forecast point,. You can then set the feature derivation window start date and end dates if you want to leverage different points in the history (e.g. given a long enough history we will generate averages over the same week in the previous month or the average over the previous month or quarter) or specify different seasonalities (e.g. 90 day seasonality) directly under the advanced options. That will make it easy to compare different approaches like-for-like as the forecast windows will align, and also mean you can compare to the simple approaches (like using the latest known value as the forecast).

> What if there is no seasonality and no trend (random time series), the also would it make sense to use forecast points.

Yes. We still need to know what points you want forecasts and what to use for them (even if just to know whether you want to predict into the same data that was used for training.

> What if there is a cyclic seasonality which is too big for our derivation window to catch

Many of these are designed to be captured using the seasonal dummies in the models (e.g. by learning per-month effects to capture the yearly seasonal pattern). You can also specify a seasonality separate and longer than the FDW (e.g. generate stats and lags for the past month but difference relative to the same value last year) in the advanced options.

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