Elgato_dark/chronos-forecasting
1
0
Fork 0
mirror of https://github.com/amazon-science/chronos-forecasting synced 2026-05-24 01:58:27 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

Update README examples (#68)

Browse source 
*Description of changes:* Simplifies content in the "Usage" section, fix
a link.


By submitting this pull request, I confirm that you can use, modify,
copy, and redistribute this contribution, under the terms of your
choice.
This commit is contained in:
Lorenzo Stella 2024-05-10 16:36:37 +02:00 committed by GitHub
parent 6732b1c2e3
commit 9500eefd47
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
1 changed files with 20 additions and 18 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

38
README.md
View file

@ -68,18 +68,12 @@ pip install git+https://github.com/amazon-science/chronos-forecasting.git
> [!TIP]
> The recommended way of using Chronos for production use cases is through [AutoGluon](https://auto.gluon.ai), which features ensembling with other statistical and machine learning models for time series forecasting as well as seamless deployments on AWS with SageMaker 🧠. Check out the AutoGluon Chronos [tutorial](https://auto.gluon.ai/stable/tutorials/timeseries/forecasting-chronos.html).
> [!NOTE]
> We have added 🧪experimental support for [MLX](https://github.com/ml-explore/mlx) inference. If you have an Apple Silicon Mac, check out the [`mlx`](https://github.com/amazon-science/chronos-forecasting/tree/mlx) branch of this repository for instructions on how to install and use the MLX version of Chronos.
### Forecasting
A minimal example showing how to perform forecasting using Chronos models:
```python
# for plotting, run: pip install pandas matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd # requires: pip install pandas
import torch
from chronos import ChronosPipeline
@ -93,19 +87,27 @@ df = pd.read_csv("https://raw.githubusercontent.com/AileenNielsen/TimeSeriesAnal
# context must be either a 1D tensor, a list of 1D tensors,
# or a left-padded 2D tensor with batch as the first dimension
context = torch.tensor(df["#Passengers"])
prediction_length = 12
# forecast shape: [num_series, num_samples, prediction_length]
forecast = pipeline.predict(
context,
prediction_length,
context=torch.tensor(df["#Passengers"]),
prediction_length=12,
num_samples=20,
temperature=1.0,
top_k=50,
top_p=1.0,
) # forecast shape: [num_series, num_samples, prediction_length]
)
```
# visualize the forecast
forecast_index = range(len(df), len(df) + prediction_length)
More options for `pipeline.predict` can be found with:
```python
print(ChronosPipeline.predict.__doc__)
```
We can now visualize the forecast:
```python
import matplotlib.pyplot as plt # requires: pip install matplotlib
import numpy as np
forecast_index = range(len(df), len(df) + 12)
low, median, high = np.quantile(forecast[0].numpy(), [0.1, 0.5, 0.9], axis=0)
plt.figure(figsize=(8, 4))
@ -142,7 +144,7 @@ embeddings, tokenizer_state = pipeline.embed(context)
### Pretraining and fine-tuning
Scripts for pretraining and fine-tuning Chronos models can be found in [this folder](./scripts/training).
Scripts for pretraining and fine-tuning Chronos models can be found in [this folder](./scripts/).
## 🔥 Coverage