# NP-Free: A Real-Time Normalization-free and Parameter-tuning-free Representation Approach for Open-ended Time Series

arXiv: https://arxiv.org/abs/2304.06168 · HTML: https://mlsanity.com/paper/2304.06168/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 12 Apr 2023.

## Builds on (most cited first, 10 of 10)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [Automatic Anomaly Detection in the Cloud Via Statistical Learning](https://mlsanity.com/paper/1704.07706/) (2017)
- [RePAD: Real-time Proactive Anomaly Detection for Time Series](https://mlsanity.com/paper/2001.08922/) (2020)
- [ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series](https://mlsanity.com/paper/2004.02319/) (2020)
- [How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?](https://mlsanity.com/paper/2102.06560/) (2021)
- [SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Series](https://mlsanity.com/paper/2104.09968/) (2021)
- [Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection](https://mlsanity.com/paper/1801.03168/) (2018)

## Built on by (most cited first, 1 of 1)

- [Evaluation of k-means time series clustering based on z-normalization and NP-Free](https://mlsanity.com/paper/2401.15773/) (2024)

## Abstract (arXiv)

As more connected devices are implemented in a cyber-physical world and data is expected to be collected and processed in real time, the ability to handle time series data has become increasingly significant. To help analyze time series in data mining applications, many time series representation approaches have been proposed to convert a raw time series into another series for representing the original time series. However, existing approaches are not designed for open-ended time series (which is a sequence of data points being continuously collected at a fixed interval without any length limit) because these approaches need to know the total length of the target time series in advance and pre-process the entire time series using normalization methods. Furthermore, many representation approaches require users to configure and tune some parameters beforehand in order to achieve satisfactory representation results. In this paper, we propose NP-Free, a real-time Normalization-free and Parameter-tuning-free representation approach for open-ended time series. Without needing to use any normalization method or tune any parameter, NP-Free can generate a representation for a raw time series on the fly by converting each data point of the time series into a root-mean-square error (RMSE) value based on Long Short-Term Memory (LSTM) and a Look-Back and Predict-Forward strategy. To demonstrate the capability of NP-Free in representing time series, we conducted several experiments based on real-world open-source time series datasets. We also evaluated the time consumption of NP-Free in generating representations.