timediffusion 0.1

TimeDiffusion - unified framework for multiple time series tasks

TimeDiffusion - Unified time series framework for multiple tasks

Supports 2D (image) and 3D (video) data, but is currently not suitable for working with them.

Install

pip install timediffusion

Quick Start

Forecasting time seires

# train sequence in shape [channels, sequence_length]
model = TD(input_dims=train.shape).to(device=device)
training_losses = model.fit(train)
predictions = model.forecast(horizon)

Creating synthetic time series

# sequence in shape [channels, sequence_length]
model = TD(input_dims=seq.shape).to(device=device)
training_losses = model.fit(seq)
# proximity - how close to original, samples - total synthetic time series
synthetic_data = model.synth(proximity=0.9, samples=3, batch_size=2, step_granulation=100)

Time series Imputation

# sequence in shape [channels, sequence_length]
model = TD(input_dims=seq.shape).to(device=device)
# mask - binary array of same shape, as sequence, with 1 in positions, that needed to be overlooked
training_losses = model.fit(seq, mask=mask)
restored_seq = model.restore(example=seq, mask=mask)

Examples

Time series tasks example

Philosophy**

TODO

Model architecture

TODO