Develop actionable time series analysis workflows with ease!
The Sintel ecosystem allows you to build advanced analytic workflows using Machine Learning, RESTful APIs, and Visualization tools.
Sintel
Code
from sigpro import SigPro
from sigpro.demo import (
get_amplitude_demo,
load_demo_primitives
)
data = get_amplitude_demo()
trans, aggre = load_demo_primitives()
sigpro = SigPro(
transformations=trans,
aggregations=aggre,
keep_columns=True
)
sigpro.process_signal(data)
Code
from orion.data import load_signal
from orion import Orion
train_data = load_signal('S-1-train')
orion = Orion('aer')
orion.fit(train_data)
new_data = load_signal('S-1-new')
orion.detect(new_data)
Code
from orion.benchmark import benchmark
pipelines = [
'arima',
'lstm_dynamic_threshold'
]
signals = ['S-1', 'P-1']
metrics = ['f1', 'accuracy', 'recall', 'precision']
benchmark(
pipelines=pipelines,
datasets=signals,
metrics=metrics,
rank='f1'
)
Explore the Sintel ecosystem and its open source libraries. These libraries support a variety of time series tasks including generative modeling for time series, labeling, benchmarking, and user interactions.
Orion
Python library for unsupervised time series anomaly detection.
sigllm
Detecting anomalies in time series using large language models.
SigPro
Featurizing time series data for downstream machine learning tasks.
ml-stars
Providing primitives and pipelines for time series tasks.
zephyr
A machine learning library for assisting in the generation of machine learning problems for wind farms operations data by analyzing past occurrences of events.
MTV
A visualization platform for complex time series analytics.
Sintel
Providing RESTful APIs to process signals.
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