Time series forecasting is an essential tool in analyzing and predicting future trends based on historical data. In this blog post, we will explore how to implement time series forecasting using Nashorn, which is a JavaScript engine built into the Java Virtual Machine (JVM).

Table of Contents

• What is Nashorn?
• Time Series Forecasting
• Getting Started
• Preparing the Data
• Building the Model
• Evaluating the Model
• Conclusion

What is Nashorn?

Nashorn is a JavaScript engine developed by Oracle and introduced in Java Development Kit (JDK) 8. It enables developers to run JavaScript code on the JVM, making it possible to integrate JavaScript and Java seamlessly. Nashorn allows you to utilize the power of JavaScript for various tasks within your Java applications.

Time Series Forecasting

Time series forecasting involves predicting future values based on historical data. It finds applications in various fields, such as finance, sales forecasting, weather prediction, and many more. The goal is to analyze patterns, trends, and seasonality in the historical data to make accurate predictions for the future.

Getting Started

To get started, make sure you have JDK 8 (or a later version) installed on your machine. Nashorn comes pre-packaged with JDK 8, so no additional installation is required.

Create a new Java file, for example, TimeSeriesForecasting.java, and import the necessary classes:

import jdk.nashorn.api.scripting.ScriptObjectMirror;
import jdk.nashorn.api.scripting.NashornScriptEngineFactory;
import javax.script.Invocable;
import javax.script.ScriptEngine;
import javax.script.ScriptEngineManager;

Preparing the Data

Before building the forecasting model, we need to prepare the time series data. Typically, a time series dataset consists of a sequence of timestamped observations.

Assuming the data is stored in a CSV file, we can read and preprocess it using a library like Apache Commons CSV. Here’s an example of how to read a CSV file and convert it into an array of data points:

import org.apache.commons.csv.CSVFormat;
import org.apache.commons.csv.CSVParser;
import org.apache.commons.csv.CSVRecord;
import java.io.FileReader;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;

public class TimeSeriesForecasting {
    //...
    
    private static List<DataPoint> readCSV(String filename) throws IOException {
        List<DataPoint> dataPoints = new ArrayList<>();

        FileReader fileReader = new FileReader(filename);
        CSVParser csvParser = new CSVParser(fileReader, CSVFormat.DEFAULT);

        for (CSVRecord csvRecord : csvParser) {
            // Parse the timestamp and value from CSV record
            long timestamp = Long.parseLong(csvRecord.get(0));
            double value = Double.parseDouble(csvRecord.get(1));

            // Create a new data point object and add it to the list
            dataPoints.add(new DataPoint(timestamp, value));
        }

        csvParser.close();
        fileReader.close();

        return dataPoints;
    }
    
    //...
}

Building the Model

Once we have the data prepared, we can use Nashorn to build the forecasting model. Nashorn allows us to execute and interact with JavaScript code from within our Java application.

Here’s how you can create a simple exponential smoothing model using JavaScript with Nashorn:

public class TimeSeriesForecasting {
    //...

    private static void buildModel(List<DataPoint> dataPoints) throws Exception {
        // Create a Nashorn script engine
        ScriptEngine engine = new ScriptEngineManager().getEngineByName("nashorn");

        // Load the JavaScript code from a file or a string
        String script = "function forecast(dataPoints) { ... }"; // JavaScript code for the model
        engine.eval(script);
        
        // Bind the Java objects to JavaScript variables
        engine.put("dataPoints", dataPoints);

        // Invoke the JavaScript function
        Invocable invocable = (Invocable) engine;
        Object result = invocable.invokeFunction("forecast", dataPoints);

        // Process the result
        // ...
    }

    //...
}

In the buildModel() method, we create a Nashorn script engine and load the JavaScript code for our forecasting model. We bind the Java dataPoints list to a JavaScript variable and invoke the forecast function defined in the JavaScript code. Finally, we can process the result returned by the JavaScript function within our Java application.

Evaluating the Model

After building the forecasting model and obtaining the predictions, we need to evaluate its performance. Common evaluation metrics for time series forecasting include mean squared error (MSE), mean absolute error (MAE), and root mean square error (RMSE).

Here’s an example of how to calculate the MSE using Java:

public class TimeSeriesForecasting {
    //...
    
    private static double calculateMSE(List<Double> actual, List<Double> predicted) {
        double sum = 0.0;
        int n = actual.size();
        
        for (int i = 0; i < n; i++) {
            double error = actual.get(i) - predicted.get(i);
            sum += Math.pow(error, 2);
        }
        
        return sum / n;
    }
    
    //...
}

Conclusion

In this blog post, we explored how to implement time series forecasting using Nashorn, the JavaScript engine built into the Java Virtual Machine. We learned how to prepare the data, build a forecasting model using JavaScript, and evaluate the model’s performance. Nashorn provides a convenient way to leverage JavaScript capabilities within Java applications, opening up new possibilities for data analysis and prediction.

Start exploring Nashorn’s capabilities and unleash the power of JavaScript for your time series forecasting needs! #nashorn #forecasting