autotimeseries

Nixtla SDK。可扩展的时间序列预测管道。

autotimeseries 是一个python SDK，用于消费在 https://github.com/Nixtla/nixtla 开发的API。

安装

PyPI

pip install autotimeseries

如何使用

查看以下示例以获取完整的管道

• M5最先进的再现.
• M5最先进的Colab再现

基本用法

import os

from autotimeseries.core import AutoTS

autotimeseries = AutoTS(bucket_name=os.environ['BUCKET_NAME'],
                        api_id=os.environ['API_ID'], 
                        api_key=os.environ['API_KEY'],
                        aws_access_key_id=os.environ['AWS_ACCESS_KEY_ID'], 
                        aws_secret_access_key=os.environ['AWS_SECRET_ACCESS_KEY'])

将数据集上传到S3

train_dir = '../data/m5/parquet/train'
# File with target variables
filename_target = autotimeseries.upload_to_s3(f'{train_dir}/target.parquet')
# File with static variables
filename_static = autotimeseries.upload_to_s3(f'{train_dir}/static.parquet')
# File with temporal variables
filename_temporal = autotimeseries.upload_to_s3(f'{train_dir}/temporal.parquet')

上传数据集中的每个时间序列由列 item_id 定义。同时时间列由 timestamp 定义，目标列由 demand 定义。我们需要将这些参数传递给每个调用。

columns = dict(unique_id_column='item_id',
               ds_column='timestamp',
               y_column='demand')

发送作业以进行预测

response_forecast = autotimeseries.tsforecast(filename_target=filename_target,
                                              freq='D',
                                              horizon=28, 
                                              filename_static=filename_static,
                                              filename_temporal=filename_temporal,
                                              objective='tweedie',
                                              metric='rmse',
                                              n_estimators=170,
                                              **columns)

下载预测

autotimeseries.download_from_s3(filename='forecasts_2021-10-12_19-04-32.csv', filename_output='../data/forecasts.csv')