Generate timeseries datasets

There are a number of algorithms that can be used to generate synthetic datasets for time series. Some of the most common methods include:

Autoregressive integrated moving average (ARIMA): ARIMA is a statistical model that can be used to model time series data. ARIMA models are typically used to forecast future values of a time series, but they can also be used to generate synthetic data.

Exponential smoothing: Exponential smoothing is a forecasting method that uses weighted averages of past data to predict future values. Exponential smoothing can be used to generate synthetic data by repeatedly applying the forecasting method to the previous data point.

Generative adversarial networks (GANs): GANs are a type of deep learning model that can be used to generate new data that is similar to existing data. GANs work by training two models against each other: a generator and a discriminator. The generator is responsible for creating new data, while the discriminator is responsible for distinguishing between real and synthetic data.

Probabilistic graphical models (PGMs): PGMs are a type of statistical model that can be used to represent the relationships between variables in a dataset. PGMs can be used to generate synthetic data by sampling from the probability distribution of the model.