timediffusion 0.2.1

TimeDiffusion - Unified Framework for Multiple Time Series Tasks

TimeDiffusion - Unified Framework for Multiple Time Series Tasks

Supports 2D (image) and 3D (video) data as input for research purposes.

Install

pip install timediffusion

Quick Start

Forecasting 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)

Examples

Time series tasks example

Philosophy

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

Model architecture

Presented below are diagrams depicting model components, each new scheme representing a higher level of abstraction.

• Temporal Block

• TimeDiffusionProjector

• TimeDiffusion