Fix README example to use predict_quantiles (#220)

*Issue #, if available:*

*Description of changes:* `predict` returns different things based on
model type. This fixes the example to use `predict_quantiles` which will
give correct quantiles.


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Co-authored-by: Abdul Fatir Ansari <[email protected]>

README.md

@@ -114,16 +114,16 @@ df = pd.read_csv(
 
 # context must be either a 1D tensor, a list of 1D tensors,
 # or a left-padded 2D tensor with batch as the first dimension
-# The original Chronos models generate forecast samples, so forecast has shape
-# [num_series, num_samples, prediction_length].
-# Chronos-Bolt models generate quantile forecasts, so forecast has shape
-# [num_series, num_quantiles, prediction_length].
-forecast = pipeline.predict(
-    context=torch.tensor(df["#Passengers"]), prediction_length=12
+# quantiles is an fp32 tensor with shape [batch_size, prediction_length, num_quantile_levels]
+# mean is an fp32 tensor with shape [batch_size, prediction_length]
+quantiles, mean = pipeline.predict_quantiles(
+    context=torch.tensor(df["#Passengers"]),
+    prediction_length=12,
+    quantile_levels=[0.1, 0.5, 0.9],
 )
 ```
 
-More options for `pipeline.predict` can be found with:
+For the original Chronos models, `pipeline.predict` can be used to draw forecast samples. More options for `predict_kwargs` in `pipeline.predict_quantiles` can be found with:
 
 ```python
 from chronos import ChronosPipeline, ChronosBoltPipeline
@@ -136,10 +136,9 @@ 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)
+low, median, high = quantiles[0, :, 0], quantiles[0, :, 1], quantiles[0, :, 2]
 
 plt.figure(figsize=(8, 4))
 plt.plot(df["#Passengers"], color="royalblue", label="historical data")