Sintel

Analyze, Interact, Decide

The Sintel ecosystem allows you to build advanced analytic workflows using Machine Learning, RESTful APIs, and Visualization tools.

Sintel

Featurization

Featurize time series with domain knowledge encoded for machine learning uses.

Anomaly Detection

Identify anomalous time series segments.

Classification

Classify time series segments into particular categories.

Forecasting

Predict future values by analyzing past trends.

Benchmarking

Benchmark machine learning models for different tasks.

Data Visualization

Use a suite of visualization techniques to support complex analytic workflow with human-in-the-loop

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(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 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.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'
)

Open Source

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.