Setting Up Time Series Forecasting on Ubuntu using LLaMA.cpp

This guide provides step-by-step instructions to set up a Time Series Forecasting system on Ubuntu using LLaMA.cpp. Time series forecasting involves predicting future values based on previously observed values.

1. Install System Prerequisites

Start by updating your Ubuntu system and installing essential development tools. Open a terminal and run:

sudo apt update
sudo apt upgrade
sudo apt install python3 python3-pip git cmake build-essential
    

2. Install LLaMA.cpp

Clone the LLaMA.cpp repository and build it. This library will be used to handle the model for inference:

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
mkdir build
cd build
cmake ..
make
    

3. Obtain a Pre-trained Time Series Model

LLaMA.cpp requires a compatible model for time series forecasting. Obtain such a model and place it in a folder named models:

mkdir -p ../models
cp /path/to/your/model.bin ../models/
    

4. Install Python Dependencies

Install additional libraries required for data manipulation and forecasting:

pip install numpy pandas matplotlib scikit-learn
    

5. Prepare Your Time Series Dataset

Ensure your dataset is in a CSV format and save it under a folder named data:

mkdir data
cp /path/to/your/timeseries_data.csv data/
    

6. Write the Time Series Forecasting Script

Create a Python script named time_series_forecasting.py with the following content:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
import llama_cpp as llcpp  # Replace with actual llama_cpp import

# Load the dataset
data = pd.read_csv('data/timeseries_data.csv')
target_column = 'value'  # Replace with the actual target column name

# Prepare the data for time series forecasting
values = data[target_column].values.reshape(-1, 1)

# Normalize the data
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_values = scaler.fit_transform(values)

# Prepare the input and output for LLaMA model
def create_dataset(dataset, time_step=1):
    X, y = [], []
    for i in range(len(dataset) - time_step - 1):
        a = dataset[i:(i + time_step), 0]
        X.append(a)
        y.append(dataset[i + time_step, 0])
    return np.array(X), np.array(y)

# Set the time step for the forecasting model
time_step = 10
X, y = create_dataset(scaled_values, time_step)

# Reshape input for LLaMA (samples, time steps, features)
X = X.reshape(X.shape[0], X.shape[1], 1)

# Load the model
model_path = '../models/your_model.bin'  # Update with your model path
model = llcpp.LLaMA(model_path)

# Function to predict future values using LLaMA
def predict_future(input_sequence):
    prediction = model.infer(input_sequence.flatten())  # Adjust based on the LLaMA inference method
    return prediction[0]  # Adjust based on output format

# Forecasting future values
predictions = []
last_sequence = X[-1]  # Start with the last sequence from training data

for _ in range(10):  # Predict the next 10 time steps
    next_value = predict_future(last_sequence)
    predictions.append(next_value)
    
    # Update the input sequence
    last_sequence = np.append(last_sequence[1:], next_value).reshape(1, time_step, 1)

# Inverse transform predictions to original scale
predictions = scaler.inverse_transform(np.array(predictions).reshape(-1, 1))

# Plot the results
plt.plot(data[target_column].values, label='Original Data')
plt.plot(range(len(data), len(data) + len(predictions)), predictions, label='Predictions', color='red')
plt.legend()
plt.show()
    

Make sure to replace your_model.bin with the path to your LLaMA model and adjust the model inference function to match the LLaMA version used.

7. Run the Time Series Forecasting Script

Execute the script to run the time series forecasting:

python3 time_series_forecasting.py
    

This command will output a plot showing the original time series data along with the forecasted values.

8. Additional Adjustments

Consider experimenting with different time steps, scaling methods, and model configurations to optimize the forecasting accuracy for your specific dataset.

Conclusion

You have successfully set up a Time Series Forecasting system on Ubuntu using LLaMA.cpp. This framework can be further customized and optimized for various forecasting tasks based on your specific needs.