Time Series Forecasting
Chronos
Safetensors
t5
time series
forecasting
pretrained models
foundation models
time series foundation models
time-series
Instructions to use amazon/chronos-bolt-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Chronos
How to use amazon/chronos-bolt-tiny with Chronos:
pip install chronos-forecasting
import pandas as pd from chronos import BaseChronosPipeline pipeline = BaseChronosPipeline.from_pretrained("amazon/chronos-bolt-tiny", device_map="cuda") # Load historical data context_df = pd.read_csv("https://autogluon.s3.us-west-2.amazonaws.com/datasets/timeseries/misc/AirPassengers.csv") # Generate predictions pred_df = pipeline.predict_df( context_df, prediction_length=36, # Number of steps to forecast quantile_levels=[0.1, 0.5, 0.9], # Quantiles for probabilistic forecast id_column="item_id", # Column identifying different time series timestamp_column="Month", # Column with datetime information target="#Passengers", # Column(s) with time series values to predict ) - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "autogluon/chronos-bolt-tiny", | |
| "architectures": [ | |
| "ChronosBoltModelForForecasting" | |
| ], | |
| "chronos_config": { | |
| "context_length": 2048, | |
| "input_patch_size": 16, | |
| "input_patch_stride": 16, | |
| "prediction_length": 64, | |
| "quantiles": [ | |
| 0.1, | |
| 0.2, | |
| 0.3, | |
| 0.4, | |
| 0.5, | |
| 0.6, | |
| 0.7, | |
| 0.8, | |
| 0.9 | |
| ], | |
| "use_reg_token": true | |
| }, | |
| "chronos_pipeline_class": "ChronosBoltPipeline", | |
| "classifier_dropout": 0.0, | |
| "d_ff": 1024, | |
| "d_kv": 64, | |
| "d_model": 256, | |
| "decoder_start_token_id": 0, | |
| "dense_act_fn": "relu", | |
| "dropout_rate": 0.1, | |
| "eos_token_id": 1, | |
| "feed_forward_proj": "relu", | |
| "initializer_factor": 0.05, | |
| "is_encoder_decoder": true, | |
| "is_gated_act": false, | |
| "layer_norm_epsilon": 1e-06, | |
| "model_type": "t5", | |
| "n_positions": 512, | |
| "num_decoder_layers": 4, | |
| "num_heads": 4, | |
| "num_layers": 4, | |
| "pad_token_id": 0, | |
| "reg_token_id": 1, | |
| "relative_attention_max_distance": 128, | |
| "relative_attention_num_buckets": 32, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.39.3", | |
| "use_cache": true, | |
| "vocab_size": 2 | |
| } | |