Update README.md

README.md

@@ -1 +1,53 @@
-# TimeDiffusion
+# 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](./examples/example_1d_data.ipynb)
+
+**Philosophy****
+
+TODO
+
+**Model architecture**
+
+TODO