Stock Price Forecasting using Tensorflow.js and LSTM, based on a well-documented project: - CoPilot Generated

Project Example: Stock Price Forecasting with Tensorflow.js

A GitHub project by jinglescode demonstrates how to:

• Pull stock prices using the Alpha Vantage API
• Preprocess data using Simple Moving Average
• Train an LSTM neural network in Tensorflow.js
• Predict future stock prices and compare them to actual values

Why LSTM for Time Series?

LSTM (Long Short-Term Memory) networks are ideal for time series because they:

• Capture long-term dependencies
• Handle non-linear relationships
• Work with variable-length sequences
• Automatically learn relevant features from raw data

🛠️ Tensorflow.js Implementation Tips

To build your own LSTM time series model in TensorFlow.js:

1. Prepare your data: Normalize and shape it into sequences.
2. Define the model:

   
   const model = tf.sequential();
   model.add(tf.layers.lstm({ units: 50, returnSequences: false, inputShape: [sequenceLength, numFeatures] }));
   model.add(tf.layers.dense({ units: 1 }));
   model.compile({ optimizer: 'adam', loss: 'meanSquaredError' });
   

3. Train the model:

   
    await model.fit(trainX, trainY, {
     epochs: 50,
     batchSize: 32,
     validationSplit: 0.2,
     callbacks: tf.callbacks.earlyStopping({ monitor: 'val_loss', patience: 5 })
   });

4. Make predictions and visualize results using libraries like Chart.js or D3.js.

🧪 Step-by-Step: LSTM Time Series Forecasting in Tensorflow.js

1. 📊 Choose or Load Your Dataset

   You’ll need a time series dataset. Here are a few options:

   • Weather data (e.g., daily temperature)
   • Stock prices (e.g., closing prices)
   • Sensor readings (e.g., IoT data)

2. 🧹 Preprocess the Data
   • Normalize values (e.g., MinMax scaling)
   • Create sequences of fixed length (e.g., 30 days of data to predict the next day)

     
      function createSequences(data, sequenceLength) {
       const sequences = [];
       const labels = [];
       for (let i = 0; i < data.length - sequenceLength; i++) {
         sequences.push(data.slice(i, i + sequenceLength));
         labels.push(data[i + sequenceLength]);
       }
       return { sequences, labels };
     } 
     

3. 🧠 Build the LSTM Model

   
     	const model = tf.sequential();
     	model.add(tf.layers.lstm({
     	units: 64,
     	inputShape: [sequenceLength, 1],
     	returnSequences: false
   	}));
   	model.add(tf.layers.dense({ units: 1 }));
   	model.compile({ optimizer: 'adam', loss: 'meanSquaredError' });
   

4. 🏋️ Train the Model

   
     await model.fit(trainX, trainY, {
     epochs: 50,
     batchSize: 32,
     validationSplit: 0.2,
     callbacks: tf.callbacks.earlyStopping({ monitor: 'val_loss', patience: 5 })
   });
   

5. 🔮 Make Predictions

   
   const prediction = model.predict(tf.tensor3d([yourSequence], [1, sequenceLength, 1]));
   prediction.print();
   

6. 📈 Visualize Results

   Use Chart.js or D3.js to plot actual vs predicted values.

🧰 Tools You’ll Need

• Tensorflow.js: npm install @tensorflow/tfjs
• Chart.js (optional): npm install chart.js