Namespace com.teracloud.streams.timeseries.modeling

Operators

  • ARIMA2: The ARIMA2 operator implements the autoregressive integrated moving average (ARIMA) modeling algorithm.
  • AutoForecaster2: The AutoForecaster2 operator is a forecasting operator, which detects the algorithm that best suits the input time series data in real time and forecasts future time series.
  • BATS: The BATS operator is a forecasting operator, which can be used to do long-term forecasting of regular time series with complex seasonality by using the BATS algorithms.
  • BoundedAnomalyDetector: The BoundedAnomalyDetector operator detects anomalys (outliers) in a timeseries.
  • FMPFilter: The FMPFilter operator is an adaptive faded-memory polynomial filter.
  • GAMLearner: The GAMLearner operator applies the generalized additive model (GAM) algorithm to categorical or continuous time series data.
  • GAMScorer: The GAMScorer operator applies a generalized additive model to score the input time series values.
  • GMM: The GMM operator uses a Gaussian mixture model to estimate the probability density function (a smoothed histogram) of a time series.
  • HoltWinters2: The HoltWinters2 operator is a forecasting operator, which uses the Holt-Winters algorithm to do long-term forecasting.
  • HoltWinters3: The HoltWinters3 operator is a forecasting operator, which can be used to do long-term forecasting of seasonal regular time series by using different variants of the Holt-Winters algorithms.
  • KMeansClustering: Cluster analysis is a popular technique used to find natural grouping of a set of objects.
  • Kalman: The Kalman operator runs an adaptive filter on a time series and can used for tracking, smoothing, adaptation.
  • LPC: The Linear Predictive Coding (LPC) operator uses an autoregressive (AR) model to predict values in a time series.
  • RLSFilter: The Recursive Least Squares (RLS) is linear regression estimation algorithm that learns to predict a target time series, given inputs.
  • VAR2: The VAR2 operator tracks data movement and predicts the next expected time series by using a multivariate autoregressive model.

Types