Synthesize time-series data

Using DoppelGANger to generate synthetic time-series data:

Although tabular data may be the most frequently discussed type of data, a great number of real-world domains — from traffic and daily trajectories to stock prices and energy consumption patterns — produce time-series data which introduces several aspects of complexity to synthetic data generation.

Time-series data is structured sequentially, with observations ordered chronologically based on their associated timestamps or time intervals. It explicitly incorporates the temporal aspect, allowing for the analysis of trends, seasonality, and other dependencies over time.

DoppelGANger is a model that uses a Generative Adversarial Network (GAN) framework to generate synthetic time series data by learning the underlying temporal dependencies and characteristics of the original data:

• 📑 Paper: Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions

Here’s an example of how to synthetize time-series data with DoppelGANger using the Yahoo Stock Price dataset:

# Importing necessary libraries
from ydata_synthetic.synthesizers.timeseries import TimeSeriesSynthesizer
from ydata_synthetic.preprocessing.timeseries import processed_stock
from ydata_synthetic.synthesizers import ModelParameters, TrainParameters
import pandas as pd
from os import path

# Read the data
stock_data = processed_stock(path='../../data/stock_data.csv', seq_len=24)
stock_data = [pd.DataFrame(sd, columns = ["Open", "High", "Low", "Close", "Adj_Close", "Volume"]) for sd in stock_data]
stock_data = pd.concat(stock_data).reset_index(drop=True)

# Define model parameters
model_args = ModelParameters(batch_size=100,
                             lr=0.001,
                             betas=(0.5, 0.9),
                             latent_dim=3,
                             gp_lambda=10,
                             pac=10)

train_args = TrainParameters(epochs=500, sequence_length=24,
                             measurement_cols=["Open", "High", "Low", "Close", "Adj_Close", "Volume"])

# Training the DoppelGANger synthesizer
if path.exists('doppelganger_stock'):
    model_dop_gan = TimeSeriesSynthesizer.load('doppelganger_stock')
else:
    model_dop_gan = TimeSeriesSynthesizer(modelname='doppelganger', model_parameters=model_args)
    model_dop_gan.fit(stock_data, train_args, num_cols=["Open", "High", "Low", "Close", "Adj_Close", "Volume"])
    model_dop_gan.save('doppelganger_stock')

# Generating new synthetic samples
synth_data = model_dop_gan.sample(n_samples=500)
print(synth_data[0])