Forecasting

Step 6: Model Training & Evaluations

After training the neural network on the provided data, now comes the forecasting for next month. The model predicts the future opening price and store the future values along with their corresponding dates. Using for loop, we are going to perform a rolling forecasting, the steps are as follows –

We have set the future time steps to 30 and converted the test sequence to numpy array and remove singleton dimensions using sequence_to_plot.
Then, we have converted historical_data to a Pytorch tensor. The shape of the tensor is (1, sequence_length, 1), where sequence_length is the length of the historical data sequence.
the model further predicts the next value based on the ‘historical_data_tensor’.
The prediction is then converted to a numpy array and the first element is extracted.

Once the loop ends, we get the forecasted values, which are stored in list, and future dates are generated to create index for these values.

Python3

# Define the number of future time steps to forecast
num_forecast_steps = 30
 
# Convert to NumPy and remove singleton dimensions
sequence_to_plot = X_test.squeeze().cpu().numpy()
 
# Use the last 30 data points as the starting point
historical_data = sequence_to_plot[-1]
print(historical_data.shape)
 
# Initialize a list to store the forecasted values
forecasted_values = []
 
# Use the trained model to forecast future values
with torch.no_grad():
    for _ in range(num_forecast_steps*2):
        # Prepare the historical_data tensor
        historical_data_tensor = torch.as_tensor(historical_data).view(1, -1, 1).float().to(device)
        # Use the model to predict the next value
        predicted_value = model(historical_data_tensor).cpu().numpy()[0, 0]
 
        # Append the predicted value to the forecasted_values list
        forecasted_values.append(predicted_value[0])
 
        # Update the historical_data sequence by removing the oldest value and adding the predicted value
        historical_data = np.roll(historical_data, shift=-1)
        historical_data[-1] = predicted_value
 
         
# Generate futute dates
last_date = test_data.index[-1]
 
# Generate the next 30 dates
future_dates = pd.date_range(start=last_date + pd.DateOffset(1), periods=30)
 
# Concatenate the original index with the future dates
combined_index = test_data.index.append(future_dates)

Last Step: Plotting the Prediction Graph

Once, we have forecasted the future prices, we can visualize the same using line plots. We have plotted the graph for a specific time range. The blue line is the indicator of the test data. Then we plot the last 30-time steps of the test data index using the green colored line plot.

The forecasted values are plotted using red colored line plot that uses a combined index that includes both the historic data and future dates.

Python3

#set the size of the plot 
plt.rcParams['figure.figsize'] = [14, 4] 
 
 
#Test data
plt.plot(test_data.index[-100:-30], test_data.Open[-100:-30], label = "test_data", color = "b") 
#reverse the scaling transformation
original_cases = scaler.inverse_transform(np.expand_dims(sequence_to_plot[-1], axis=0)).flatten() 
 
#the historical data used as input for forecasting
plt.plot(test_data.index[-30:], original_cases, label='actual values', color='green') 
 
#Forecasted Values 
#reverse the scaling transformation
forecasted_cases = scaler.inverse_transform(np.expand_dims(forecasted_values, axis=0)).flatten() 
# plotting the forecasted values
plt.plot(combined_index[-60:], forecasted_cases, label='forecasted values', color='red')  
 
plt.xlabel('Time Step')
plt.ylabel('Value')
plt.legend()
plt.title('Time Series Forecasting')
plt.grid(True)

Output:

Prediction of next month Opening Price for Apple Inc. Stock

By plotting the test data, actual values and model’s forecasting data. We got a clear idea of how well the forecasted values are aligning with the actual time series.

The intriguing field of time series forecasting using PyTorch and LSTM neural networks has been thoroughly examined in this paper. In order to collect historical stock market data using Yahoo Finance module, we imported the yfinance library and started the preprocessing step. Then we applied crucial actions like data loading, train-test splitting, and data scaling to make sure our model could accurately learn from the data and make predictions.

For more accurate forecasts, additional adjustments, hyperparameter tuning, and optimization are frequently needed. To improve predicting capabilities, ensemble methods and other cutting-edge methodologies can be investigated.

We have barely begun to explore the enormous field of time series forecasting in this essay. There is a ton more to learn, from managing multi-variate time series to resolving practical problems in novel ways. With this knowledge in hand, you’re prepared to use PyTorch and LSTM neural networks to go out on your own time series forecasting adventures.

Enjoy your forecasting!

Time Series Forecasting using Pytorch

Time series forecasting plays a major role in data analysis, with applications ranging from anticipating stock market trends to forecasting weather patterns. In this article, we’ll dive into the field of time series forecasting using PyTorch and LSTM (Long Short-Term Memory) neural networks. We’ll uncover the critical preprocessing procedures that underpin the accuracy of our forecasts along the way.

Table of Content

Time Series Forecasting
Implementation of Time Series Forecasting:
Step 1: Import the necessary libraries
Step2: Loading the Dataset
Step 3: Data Preprocessing
Step 4: Define LSTM class model
Step 5: Creating Data Loader for batch training
Step 6: Model Training & Evaluations
Step 7: Forecasting

Forecasting

Python3

Last Step: Plotting the Prediction Graph

Python3

Time Series Forecasting using Pytorch

Table of Content

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