Supports 2D (image) and 3D (video) data as input for research purposes.
pip install timediffusionForecasting time series
# train sequence in shape [channels, sequence_length]
model = TD(input_dims=train.shape).to(device=device)
training_losses = model.fit(train)
# horizon : int - how many future values to forecast
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=10)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 are unknown
training_losses = model.fit(seq, mask=mask)
restored_seq = model.restore(example=seq, mask=mask)Time series: multiple tasks example
Forecasting bitcoin price example
Main synopsis behind TimeDiffusion model is that in reality, when working with time series we don’t have many samples, as it could be in other machine learning fields (e.g. cv, nlp). Thus, classical autoregressive approaches like ARIMA has the most suitable approach of fitting / training only on original sequence (maybe with some exogenous data).
TimeDiffusion takes inspiration from these established methods and only trains on the input sample. Model incorporates most powerful modern deep learning techniques such as diffusion process, exponential dilated convolutions, residual connections and attention mechanism (in one of the versions)
TimeDiffusionProjector- exponential dilated convolutions + residual connections.
Currently main model in use
TimeDiffusionAttention- attention mechanism on top ofTimeDiffusionProjector(q, k, v) projectors.
Currently not viable
TimeDiffusionLiquid- exponential dilated convolutions with shared middle convolutional layer weight.
Lightweight, fast, but less accurate than the main model.
Presented below are diagrams depicting model components, each new scheme representing a higher level of abstraction.
- Temporal Block
- TimeDiffusionProjector
- TimeDiffusionAttention
