C# equivalent to Python's "Prophet" library

Summary

The Prophet library in Python is a popular choice for forecasting time series data, but when used in Power BI, it can cause issues when exporting reports. To overcome this, we can use C# to forecast the data outside of Power BI and then import the results. This approach allows us to leverage our existing EF Core setup to access the data stored in an Azure SQL Server database.

Root Cause

The root cause of the issue is the limitation of Power BI’s Python script visual when exporting reports, which replaces the forecast with an error message if the report creator isn’t a premium subscriber. The key causes are:

Power BI limitations: The Python script visual has limitations when exporting reports.
Prophet library: The Prophet library is not native to C# and requires a Python environment.
EF Core setup: The existing EF Core setup is not compatible with the Python script visual.

Why This Happens in Real Systems

This issue occurs in real systems due to:

Technical debt: The use of a Python script visual in Power BI can lead to technical debt when exporting reports.
Limited compatibility: The Prophet library is not compatible with all systems, including C#.
Scalability issues: The Python script visual can cause scalability issues when dealing with large datasets.

Real-World Impact

The real-world impact of this issue is:

Inaccurate forecasting: The error message replacing the forecast can lead to inaccurate decision-making.
Increased costs: The need for a premium subscription to access the forecast can increase costs.
Limited accessibility: The limitation of the Python script visual can limit accessibility to the forecast for non-premium subscribers.

Example or Code (if necessary and relevant)

using System;
using System.Collections.Generic;
using Microsoft.EntityFrameworkCore;
using MathNet.Numerics.Statistics;

public class DonationForecast
{
    public DateTime Date { get; set; }
    public double Donation { get; set; }
}

public class DonationContext : DbContext
{
    public DbSet Donations { get; set; }

    protected override void OnConfiguring(DbContextOptionsBuilder optionsBuilder)
    {
        optionsBuilder.UseSqlServer(@"Server=tcp:localhost,1433;Database=Donations;User ID=username;Password=password;");
    }
}

class Program
{
    static void Main(string[] args)
    {
        using (var context = new DonationContext())
        {
            var donations = context.Donations.ToList();

            // Perform forecasting using a C# library like MathNet.Numerics
            var forecast = ForecastDonations(donations);

            // Import the forecast into Power BI
            ImportForecastIntoPowerBI(forecast);
        }
    }

    static List ForecastDonations(List donations)
    {
        // Implement forecasting logic using a C# library like MathNet.Numerics
        return donations;
    }

    static void ImportForecastIntoPowerBI(List forecast)
    {
        // Implement logic to import the forecast into Power BI
    }
}

How Senior Engineers Fix It

Senior engineers fix this issue by:

Using a C# library: They use a C# library like MathNet.Numerics to perform forecasting.
Leveraging EF Core: They leverage their existing EF Core setup to access the data stored in the Azure SQL Server database.
Implementing forecasting logic: They implement forecasting logic using a C# library and import the results into Power BI.

Why Juniors Miss It

Juniors may miss this issue due to:

Lack of experience: They may not have experience with Power BI limitations or C# libraries for forecasting.
Limited knowledge: They may not be familiar with EF Core or Azure SQL Server databases.
Insufficient training: They may not have received sufficient training on forecasting or data analysis.

C# equivalent to Python’s “Prophet” library