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May 10, 2024
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Update README examples #68

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38 changes: 20 additions & 18 deletions README.md
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Expand Up @@ -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
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What about pandas requirement?

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

Expand All @@ -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]
)
```

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

# visualize the forecast
forecast_index = range(len(df), len(df) + prediction_length)
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))
Expand Down Expand Up @@ -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

Expand Down