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›Detection

Forecasting

  • Autoregressive Neural Network (AR_net)
  • Quadratic Model
  • Linear Model
  • KatsEnsemble
  • Empirical Confidence Interval
  • STLF
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  • Holt-Winter’s
  • Prophet
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  • ARIMA

Detection

  • BOCPD: Residual Translation
  • BOCPD: Bayesian Online Changepoint Detection
  • Outlier Detection
  • ACFDetector
  • Seasonality Detector
  • Cusum Detector

TSFeatures

  • TsFeatures

Multivariate

  • Multivariate Outlier Detection
  • VAR

Utilities

  • Model Hyperparameter Tuning
  • Backtesting
  • Time Series Decomposition
  • Dataswarm Operators

ACFDetector

The detector using autocorrelation function (ACF) to detect seasonality. It will check if there are significant autocorrelations and try to figure out the length of the seasonality.

API

# Model class
class ACFDetector(data)

Method

detector(lags=None, diff=1, alpha=0.01) # run detector, returns Dict with keys seasonality_presence and seasonalities
plot() # plot ACF and decompsed resutl if decomposed
remover(decom=TimeSeriesDecomposition, model="additive", decompose_any_way=False) # remove seasonality returns decomosition results

Parameters

lags: int, unmber of lags in ACF, default is 1/3 of the length of the input data
diff: int, number of diffs apply to the data before ACF
alpha: float, significant level for the autocorrelation
decom: decomposition method in kats
model: what kind of model of the decomposition use, either "additive" or "multiplicative"
decompose_any_way: bool, decompose time serires even if not seasonality detected

Examples

from infrastrategy.kats.detectors.seasonalityDetection import ACFDetector
from infrastrategy.kats.consts import TimeSeriesData
import pandas as pd
import numpy as np
df = pd.read_csv('../data/example_air_passengers.csv')
df.rename(columns={'ds':'time'}, inplace=True)
timeseries = TimeSeriesData(df)

# initialize detector
detector = ACFDetector(timeseries)

# run detector
detector.detector(diff=1, alpha = 0.01)
{'seasonality_presence': True, 'seasonalities': [12]}
# seasonality decomposition, returns trend, seasonal, rem term
detector.remover()

# plot acf and decompsition results
detector.plot()

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