Powerball (Lottery) “Forecasting” with Tensorflow.js and LSTM - CoPilot Generated

Adapting the stock‐forecasting pipeline to ingest historical Powerball draws, train an LSTM on sequences of past draws, and “predict” the next draw. Note that Powerball is random—this is purely experimental and not a reliable method for winning.

• Prerequisites

  Initialize Node.js project and isntall:

  
  mkdir tfjs-powerball
  cd tfjs-powerball
  npm init ‑y
  npm install @tensorflow/tfjs-node csv-parser csv-writer
  

  Package	Purpose
  @tensorflow/tfjs-node	Run Tensorflow.js models in Node
  csv-parser	Parse historical Powerball CSV files
  csv-writer	Export results for plotting/analysis

• Obtain Historical Data

  Download a CSV of past Powerball results, with columns like:

  
    
  
    
  
      
   Draw Date
      
  White Ball 1
      
  White Ball 2
      
  White Ball 3
      
  White Ball 4
      
  White Ball 5
      
  Red Ball
    
    
  
       
   2025-07-30 
      
   4  
      
   15 
      
   35 
      
   50 
      
   64 
      
   8  
    
  
      
   ..... 
      
   ..... 
      
   ..... 
      
   ..... 
      
   ..... 
      
   ..... 
      
   ..... 
    
  
    
  
  

  Save it as data/powerball.csv.

• Preprocess: Sequences & Scaling

  
  // preprocess.js
  const fs = require('fs');
  const parse = require('csv-parser');
  
  const rows = [];
  fs.createReadStream('data/powerball.csv')
    .pipe(parse())
    .on('data', row => {
      // Convert strings to numbers
      rows.push([
        +row.w1, +row.w2, +row.w3,
        +row.w4, +row.w5, +row.pb
      ]);
    })
    .on('end', () => {
      // MinMax scale each column independently
      const transpose = m => m[0].map((_, i) => m.map(row => row[i]));
      const untrans  = (cols, rows) =>
        rows.map((_, i) => cols.map(col => col[i]));
  
      const cols = transpose(rows);
      const scaledCols = cols.map(col => {
        const min = Math.min(...col), max = Math.max(...col);
        return col.map(v => (v - min) / (max - min));
      });
  
      const scaled = untrans(scaledCols, rows);
  
      // Build sequences
      const seqLen = 10;
      const X = [], Y = [];
      for (let i = 0; i + seqLen < scaled.length; i++) {
        X.push(scaled.slice(i, i + seqLen));
        Y.push(scaled[i + seqLen]);
      }
  
      // Split 80/20
      const split = Math.floor(X.length * 0.8);
      fs.writeFileSync('data/train.json',
        JSON.stringify({ X: X.slice(0, split), Y: Y.slice(0, split) }, null, 2));
      fs.writeFileSync('data/test.json',
        JSON.stringify({ X: X.slice(split),    Y: Y.slice(split)    }, null, 2));
      console.log('Preprocessing done.');
    });
  
  Run:
  
  node preprocess.js
  

• Build & Train the LSTM Model

  
  // train_model.js
  const tf = require('@tensorflow/tfjs-node');
  const { X: trainX, Y: trainY } =
    require('./data/train.json');
  
  async function run() {
    const xs = tf.tensor3d(trainX, [
      trainX.length, trainX[0].length, 6
    ]);
    const ys = tf.tensor2d(trainY, [trainY.length, 6]);
  
    const model = tf.sequential();
    model.add(tf.layers.lstm({
      units: 128,
      inputShape: [trainX[0].length, 6]
    }));
    model.add(tf.layers.dense({ units: 6, activation: 'sigmoid' }));
    model.compile({ optimizer: 'adam', loss: 'meanSquaredError' });
  
    await model.fit(xs, ys, {
      epochs: 100,
      batchSize: 16,
      validationSplit: 0.2,
      callbacks: tf.callbacks.earlyStopping({
        monitor: 'val_loss', patience: 5
      })
    });
  
    await model.save('file://model');
    console.log('Model trained and saved.');
  }
  
  run();
  
  
  Run:
  
  node train_model.js
  
  

• Predict & Invert Scaling

  
  // predict.js
  const tf = require('@tensorflow/tfjs-node');
  const fs = require('fs');
  const { X: testX, Y: testY } =
    require('./data/test.json');
  
  // Load raw closes for inverse scaling
  const raw = require('fs').readFileSync(
    'data/powerball.csv', 'utf8'
  ).split('\n').slice(1)
    .map(r => r.split(',').slice(1).map(Number));
  
  const transpose = m => m[0].map((_, i) => m.map(r => r[i]));
  const cols      = transpose(raw);
  const mins = cols.map(c => Math.min(...c));
  const maxs = cols.map(c => Math.max(...c));
  const inv = row =>
    row.map((v,i) => Math.round(v * (maxs[i]-mins[i]) + mins[i]));
  
  async function run() {
    const model = await tf.loadLayersModel('file://model/model.json');
    const xs = tf.tensor3d(testX, [testX.length, testX[0].length, 6]);
    const preds = model.predict(xs).arraySync();
  
    const results = preds.map(inv);
    console.log('Predicted next draws (rounded):', results.slice(-5));
  }
  
  run();
  
  Run:
  
  node predict.js
  
  

• Visualize (Optional)

  Export predictions.csv via csv-writer and plot in a browser with Chart.js, similar to our stock pipeline.

Tuning LSTM Units: Capacity vs. Efficiency

Choosing the right number of LSTM units is critical for balancing your model’s ability to learn patterns in Powerball sequences against training time and overfitting risk.

What Are LSTM Units?

Each LSTM unit corresponds to one memory cell that can store, read, and forget information. The units parameter defines how many of these cells exist in a layer.

How Units Affect Model Behavior

• Too few units: the model may underfit, failing to capture sequence dependencies.
• Too many units: training slows down, memory usage spikes, and overfitting becomes likely.
• Moderate units: strike a balance for learning without over-parameterizing.

Rule of Thumb and Search Strategies

1. Start small: try 32 or 64 units on your first run.
2. Double or halve based on performance: if underfitting, move to 128 or 256; if training is too slow or validation loss rises, step down.
3. Automate search: use grid search or random search over [32, 64, 128, 256] with 3–5 values.
4. Combine with early stopping: this prevents overfitting when exploring larger unit counts.

Practical Tips for Powerball Sequences

• Sequence length matters: longer sequences (e.g., 20 draws) may benefit from more units.
• Feature dimensionality: we have six features per timestep (five white balls + Powerball). More features sometimes call for more units.
• Dataset size: with ~3,000 draws, keep units under 256 to avoid overfitting.

Code Snippet: Adjusting Units

const model = tf.sequential();
model.add(tf.layers.lstm({
  units: 128,              // you can swap 128 for 64 or 256
  inputShape: [seqLen, 6],
  returnSequences: false
}));
model.add(tf.layers.dense({ units: 6, activation: 'sigmoid' }));
model.compile({ optimizer: 'adam', loss: 'meanSquaredError' });

Swap 128 with your candidate values, retrain, and compare validation loss curves.

Automated Grid Search for LSTM Units

This guide shows how to run an automated grid search over different LSTM unit counts and visualize training vs. validation loss. You’ll generate a JSON of results and plot it in the browser with Chart.js

1. Prerequisites
   • A Node.js project with preprocessed data in data/train.json.
   • Tensorflow.js for Node:

     npm install @tensorflow/tfjs-node

   • Chart.js (for the browser plot): no install needed—loaded via CDN.
2. Grid Search Script (grid_search_units.js)

   Save this file in your project root. It will:

   • Load train.json.
   • Loop over defined LSTM unit values.
   • Train models and record final losses.
   • Write grid_results.json.

   
       // grid_search_units.js
   const tf   = require('@tensorflow/tfjs-node');
   const fs   = require('fs');
   
   // Load preprocessed training data
   function loadData() {
     const { X, Y } = JSON.parse(fs.readFileSync('data/train.json'));
     const seqLen   = X[0].length;
     const xs       = tf.tensor3d(X, [X.length, seqLen, X[0][0].length]);
     const ys       = tf.tensor2d(Y, [Y.length, Y[0].length]);
     return { xs, ys, seqLen };
   }
   
   // Define the grid of LSTM units to try
   const unitGrid = [32, 64, 128, 256];
   
   async function runGridSearch() {
     const { xs, ys, seqLen } = loadData();
     const results = [];
   
     for (const units of unitGrid) {
       // Build the model
       const model = tf.sequential();
       model.add(tf.layers.lstm({
         units,
         inputShape: [seqLen, ys.shape[1]]
       }));
       model.add(tf.layers.dense({ units: ys.shape[1], activation: 'sigmoid' }));
       model.compile({ optimizer: 'adam', loss: 'meanSquaredError' });
   
       // Train with early stopping
       const history = await model.fit(xs, ys, {
         epochs: 50,
         batchSize: 32,
         validationSplit: 0.2,
         callbacks: tf.callbacks.earlyStopping({
           monitor: 'val_loss',
           patience: 5,
           restoreBestWeight: true
         }),
         verbose: 0
       });
   
       // Capture final losses
       const trainLoss = history.history.loss.pop();
       const valLoss   = history.history.val_loss.pop();
       console.log(`Units ${units} → train: ${trainLoss.toFixed(4)}, val: ${valLoss.toFixed(4)}`);
   
       results.push({ units, trainLoss, valLoss });
     }
   
     // Save results for plotting
     fs.writeFileSync('grid_results.json', JSON.stringify(results, null, 2));
     console.log('Grid search complete. Results saved to grid_results.json');
   }
   
   runGridSearch();
   
   

3. Run the Grid Search

   Execute the script in your terminal:

   node grid_search_units.js

   This will produce grid_results.json, e.g.:

   
    [
     { "units": 32,  "trainLoss": 0.0123, "valLoss": 0.0345 },
     { "units": 64,  "trainLoss": 0.0098, "valLoss": 0.0291 },
     { "units": 128, "trainLoss": 0.0087, "valLoss": 0.0282 },
     { "units": 256, "trainLoss": 0.0079, "valLoss": 0.0298 }
   ]
   

4. Visualize with Chart.js

   Create plot_units.html:

   
    <!DOCTYPE html>
   <html>
   <head>
     <meta charset="UTF-8" />
     <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
     <title>LSTM Units Grid Search</title>
   </head>
   <body>
     <canvas id="lossChart" width="600" height="400"></canvas>
     <script>
       fetch('grid_results.json')
         .then(res => res.json())
         .then(data => {
           const labels      = data.map(d => d.units);
           const trainLosses = data.map(d => d.trainLoss);
           const valLosses   = data.map(d => d.valLoss);
   
           new Chart(document.getElementById('lossChart'), {
             type: 'line',
             data: {
               labels,
               datasets: [
                 {
                   label: 'Training Loss',
                   data: trainLosses,
                   borderColor: 'blue',
                   fill: false
                 },
                 {
                   label: 'Validation Loss',
                   data: valLosses,
                   borderColor: 'red',
                   fill: false
                 }
               ]
             },
             options: {
               scales: {
                 x: { title: { display: true, text: 'LSTM Units' } },
                 y: { title: { display: true, text: 'Loss' } }
               }
             }
           });
         });
     </script>
   </body>
   </html>
   

Serve your project folder (e.g. npx serve .), then open plot_units.html to see how loss varies with unit count.

Where to go from here:

• Adjust unitGrid to include other values (e.g., 16, 512).
• Experiment with different batch sizes or epochs.
• Analyze results to pick the optimal LSTM unit count for your Powerball—or any—time series.