timeseries-agent 0.0.23

A Policy Gradient RL agent for time series prediction using PyTorch Lightning.

TimeSeries Agent is a powerful reinforcement learning library designed for time series analysis and prediction. Built on top of PyTorch and PyTorch Lightning, it provides a flexible framework for training RL agents to work with time series data.

Key Features

• Policy gradient-based reinforcement learning for time series prediction
• Easy integration with existing PyTorch workflows
• Support for custom time series datasets
• Built-in state normalization and reward calculation
• Flexible neural network architecture configuration
• Real-time prediction capabilities

Installation

pip install -i https://test.pypi.org/simple/ timeseries-agent
pip install lightning torch

Getting Started

The easiest way to get started is by following our Colab Tutorial, which walks through a complete example of training and testing a time series agent.

Using Your Own Data

To use TimeSeries Agent with your own data, you need to:

1. Prepare your time series data as a pandas DataFrame with at least one target column.

import pandas as pd
from timeseries_agent import RLTimeSeriesDataset, PolicyGradientAgent

# Load your time series data
data_df = pd.DataFrame({
    'value': your_target_values,
    'feature1': your_feature1_values,
    'feature2': your_feature2_values,
    # ... add more features as needed
})

# Configure the RL environment
LOOKBACK = 7  # Number of past time steps to consider
TARGET_COLUMN = 'value'  # Column for reward calculation
NUM_FEATURES = data_df.shape[1]  # Number of features in dataset

# Create and train the agent
agent = PolicyGradientAgent(
    full_data=data_df,
    target_column=TARGET_COLUMN,
    input_features=NUM_FEATURES,
    lookback=LOOKBACK,
    hidden_layers=[100, 100, 10],  # Customize network architecture
    normalize_state=True  # Enable state normalization
)

Key considerations when preparing your data:

• Ensure your DataFrame has no missing values
• The target column should contain the values you want to predict
• Additional features can help improve prediction accuracy
• The lookback period determines how much historical data the agent considers

License

TimeSeries Agent is released under the MIT License. See LICENSE file for details.