Time Series
This directory contains 17 workflows.
Aggregation Granularity
This component aggregates values in a selected numeric or string column by timestamps extracted from a column of type Date&Time. The granularity of the […]
Analyze ARIMA Residuals
This component analyzes the residuals of an ARIMA (AutoRegressive Integrated Moving Average) model by 1. visualizing auto correlation of the residuals 2. […]
ARIMA Learner
Trains an AutoRegressive Integrated Moving Average (ARIMA) model. ARIMA model captures temporal structures in time series data in the following […]
ARIMA Predictor
Computes predictions from an estimated AutoRegressive Integrated Moving Average (ARIMA) model. Two types of predictions are computed: 1. Forecast: […]
Auto ARIMA Learner
Trains AutoRegressive Integrated Moving Average (ARIMA) models and returns the best model according to the search criterion (AIC, BIC) within the provided […]
Auto-SARIMA
This component uses a heuristic approach to analyze the target series and fit a (S)ARIMA model for forecasting with automatically configured […]
Decompose Signal
Decomposes selected Time-Series or IoT signal into Trend, 2 Seasonal Components, and the remaining Residual. Signal = T + S1 + S2 + R [T] Trend Component: […]
Discrete Wavelet Transform (DWT)
Applies the Discrete Wavelet Transform (DWT) to selected input column with selected window sizes and steps for the selected wavelet. The following wavelets […]
Fast Fourier Transform (FFT)
This nodes performs a Fast Fourier Transform in a desired numeric timeseries data column. It requires the KNIME Python extensions with Python 3 set up. The […]
Forecast Horizon
This component visualizes how different error metrics change as a forecast horizon grows. Use this component to better understand how far into the future […]