query: Does it make sense to train kalman filter on unscaled (Timeseries-)data, to clean it?
positive_passages.docid: 150016
positive_passages.text: How to apply Kalman Filter for Cleaning Timeseries Data effectively without much optimization?
negative_passages.docid: 64962
negative_passages.text: Ignore Pulses Less than a Given Width in a Pulse Train
negative_passages.docid: 26550
negative_passages.text: ARMA/GARCH estimation in sequence
negative_passages.docid: 108624
negative_passages.text: Bayesian updating with new data
negative_passages.docid: 9357
negative_passages.text: Time series modeling with high-frequency data
negative_passages.docid: 24569
negative_passages.text: Why is forecasting of ARMA models performed by Kalman filter
negative_passages.docid: 69347
negative_passages.text: Fitting Methods in Arima
negative_passages.docid: 135432
negative_passages.text: Historical data appears seasonal, but forecasts from auto.arima are linear
negative_passages.docid: 156972
negative_passages.text: Scale-invariant analysis of time series
negative_passages.docid: 75754
negative_passages.text: Is it OK to remove outliers from data?
negative_passages.docid: 81611
negative_passages.text: How do I estimate a smooth cdf from a set of observations?
negative_passages.docid: 128695
negative_passages.text: Why do people use real values for the Wick-rotated time $\tau$?
negative_passages.docid: 157675
negative_passages.text: Prediction interval on untransformed scale
negative_passages.docid: 139577
negative_passages.text: Bootstrap methodology. Why resample "with replacement" instead of random subsampling?
negative_passages.docid: 4926
negative_passages.text: How to make BSplineFunction pass each data point and naturally smooth?
negative_passages.docid: 94994
negative_passages.text: Optimal estimation of a single parameter given data at different scales
negative_passages.docid: 80686
negative_passages.text: Simple algorithm for online outlier detection of a generic time series