Analyze large-scale time series data. Develop advanced analytics workflows with human in the loop. Transfer insights into actionable decisions.
The ecosystem of Sintel allows you to build advanced analytic workflows with human in the loop using the Machine Learning libraries, 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 pyteller.data import load_data
from pyteller.core import Pyteller
train_data = load_data(
'pyteller/data/AL_Weather_current.csv')
test_data = load_data(
'pyteller/data/AL_Weather_input.csv')
pyteller = Pyteller(
pipeline=('pyteller/pipelines/sandbox/'
'LSTM/LSTM_offset.json'),
pred_length=6, offset=1)
pyteller.fit(
data=train_data,
timestamp_col='valid', target_signal='tmpf',
entity_col='station', entities='8A0')
pyteller.forecast(data=test_data)
Code
from orion.data import load_signal
from orion import Orion
train_data = load_signal('S-1-train')
orion = Orion(pipeline='lstm_dynamic_threshold')
orion.fit(train_data)
new_data = load_signal('S-1-new')
orion.detect(new_data)
Code
from draco.demo import load_demo
from draco.pipeline import DracoPipeline
from sklearn.model_selection import train_test_split
target_times, readings = load_demo()
train, test = train_test_split(
target_times, test_size=0.25, random_state=0)
test_targets = test.pop('target')
draco = DracoPipeline(
'classes.normalize_dfs_xgb_classifier')
draco.fit(train, readings)
draco.predict(test, readings)
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'
)
Sintel have been demonstrated in the following application domains.
Sintel is an ecosystem for time series processing, labeling, modeling, benchmarking, and user interactions. Explore the ecosystem of open source libraries supporting the Sintel. Each can be used as a standalone package for particular needs.
Draco
Classifies time series segments into particular categories.
gpe
Creates labeling functions to search occurrences of specific types of events in the past.
ml-stars
Stores a collection of pipelines that will be used by Orion, Draco, and Pyteller.
MTV
Integrates a suite of visualization techniques to support complex analytic workflow with human-in-the-loop
Orion
Identifies anomalous time series segments.
pyteller
Predicts future values by analyzing past trends.
SigPro
Featurizes time series with domain knowledge encoded for machine learning uses.
Sintel
Enables your team to build human-in-the-loop analytics workflow with predefined RESTFul APIs and MongoDB Schema.
TSGym
Benchmarks machine learning models for different tasks.
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