Clarifications Regarding ForeCastBySsa #6720

Activity

1. superichmann

   added

   enhancementNew feature or requestNew feature or request

   on Jun 1, 2023
2. ghost added

   untriagedNew issue has not been triagedNew issue has not been triaged

   on Jun 1, 2023
3. LittleLittleCloud commented on Jun 6, 2023

   

   LittleLittleCloud

   on Jun 6, 2023

   Member

   More actions

   Let's break down one by one

   How can I call the functionality of mlnet forecasting from a C# codefile (something like RegressionExperiment but for ForeCastBySsa).

   You can use Process.start to run mlnet as a seperate process. However that's not suggested. We currently don't have an end to end automl experiment for forecasting and you probably need to create your own forecasting AutoMLExperiment. Here's an example of how to do it
   Forecasting with Luna

   How can I pass several features (columns) to ForeCastBySsa? do I just put them in the IDataView when I call Fit? what do I specify in the inputColumnName parameter? I have tried adding more columns to the IDataView but the results are exactly the same as without the new columns

   ForecastBySSA is a univariant forecasting trainer, which means it only accepts a single numeric column as feature input. If you want to use several features instead, you can probably transform your forecasting problem into a regression task first, and then you should be able to pass multiple features into regressor
   Here's an example of how to use autoregression to resolve a forecasting problem
   Forecasting with Luna and autoregression

   How do I specify which columns are which type in the same manner we do in ColumnInformation? Categorical, etc.

   see examples above

   Where can I find detailed non scientific documentation on what is the functionality of each parameter in ForeCastBySsa and how is it actually affects the prediction. I have already searched the official documentation, All-Samples and the source code of TimeSeries class but couldn't find detailed data.

   See examples above

   Thanks!

   And you are welcome. And if you can create an issue to ask for an AutoML forecasting experiment that would be great. As it will help us shape out the delivery for next release

   👍React with 👍1Reacted by superichmann
4. superichmann commented on Jun 12, 2023

   

   superichmann

   on Jun 12, 2023

   Author

   More actions

   Thanks! I have tried your notebooks but couldn't make the parameter finding process automatic. I will post here again with the details
5. superichmann commented on Jun 28, 2023

   

   superichmann

   on Jun 28, 2023

   Author

   More actions

   @LittleLittleCloud Hi again!
   On the example code, why do you call Predict twice? isn't that moving forward the prediction engine twice?

       // firstly, get next n predict where n is horizon
       var predict = predictEngine.Predict();
   
       predictLoads1H.Add(predict.Predict[0]);
   
       // update model with truth value
       predictEngine.Predict(new ForecastInput()
       {
           Load = load,
       });
   

   Why not just call Fit and Transform and then parse the IDataView?
6. LittleLittleCloud commented on Jun 28, 2023

   

   LittleLittleCloud

   on Jun 28, 2023

   Member

   More actions

   @superichmann
   It's because forecasting model can only predict the next value at t. To make the model predict t+1, you need to update the model with value at time t
7. superichmann commented on Jun 29, 2023

   

   superichmann

   on Jun 29, 2023

   Author

   More actions

   @LittleLittleCloud Thanks. and what do you think about this way of evaluating for one forecast at a time? I get "too good" results from this

   class MyDataForecast
   {
       public float Truth { get; set; }
       public float[] Forecast { get; set; }
   }
   

   var xmodel = X.Forecasting.ForecastBySsa("Forecast", "Truth", 2, 5, 88, 1);
   var xtransformer = xmodel.Fit(trainData);
   var transformed = xtransformer.Transform(TestData);
   var forecastData = X.Data.CreateEnumerable<MyDataForecast>(transformed, reuseRowObject: false)
   .Select(row => new
   {
       Truth= row.Truth,
       Forecast = row.Forecast[0]
   });
   var forecastDataView = X.Data.LoadFromEnumerable(forecastData);
   var metrics = X.Regression.Evaluate(forecastDataView, labelColumnName: "Truth", scoreColumnName: "Forecast");
   Console.WriteLine(metrics.MeanAbsoluteError);
   
8. rosebyte

   added

   area-TimeSeriesTime series analysis and forecastingTime series analysis and forecasting

   on Sep 8, 2026
9. rosebyte

   added

   yellowmaintenance onlymaintenance only

   on Sep 21, 2026