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Multiple change point detection based on Hodrick-Prescott and $l_1$ filtering method for random walk time series data
Methodology · Submitted 21 Jan 2025
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We also demonstrate the practical application of our approach to real-world SNP stock data, showcasing its effectiveness in detecting change points.
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The abstract, sentence by sentence
We propose new methods for detecting multiple change points in time series, specifically designed for random walk processes, where stationarity and variance changes present challenges. Our approach combines two trend estimation methods: the Hodrick Prescott (HP) filter and the l1 filter. A major challenge in these methods is selecting the tuning parameter lambda, which we address by introducing two selection techniques. For the HP based change point detection, we propose a probability-based threshold to select lambda under the assumption of an exponential distribution. For the l1 based method, we suggest a selection strategy assuming normality. Additionally, we introduce a technique to estimate the maximum number of change points in time segments using the l1 based method. We validate our methods by comparing them to similar techniques, such as PELT, using simulated data. We also demonstrate the practical application of our approach to real-world SNP stock data, showcasing its effectiveness in detecting change points.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | We propose new methods for detecting multiple change points in time series, specifically designed for random walk processes, where stationarity and variance changes present challenges. | contribution 0.96 | 0.65 | 0.13 | asserted 1.00 |
| s1 | Our approach combines two trend estimation methods: the Hodrick Prescott (HP) filter and the l1 filter. | method 0.81 | 0.84 | 0.05 | asserted 1.00 |
| s2 | A major challenge in these methods is selecting the tuning parameter lambda, which we address by introducing two selection techniques. | contribution 0.66 | 0.84 | 0.04 | asserted 1.00 |
| s3 | For the HP based change point detection, we propose a probability-based threshold to select lambda under the assumption of an exponential distribution. | method 0.77 | 0.94 | 0.04 | asserted 1.00 |
| s4 | For the l1 based method, we suggest a selection strategy assuming normality. | method 0.75 | 0.89 | 0.04 | asserted 0.58 |
| s5 | Additionally, we introduce a technique to estimate the maximum number of change points in time segments using the l1 based method. | contribution 0.72 | 0.94 | 0.05 | asserted 1.00 |
| s6 | We validate our methods by comparing them to similar techniques, such as PELT, using simulated data. | method 0.80 | 0.94 | 0.09 | asserted 1.00 |
| s7 | We also demonstrate the practical application of our approach to real-world SNP stock data, showcasing its effectiveness in detecting change points. | result 0.64 | 0.94 | 0.34 | asserted 1.00 |
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The paper
Original abstract
We propose new methods for detecting multiple change points in time series, specifically designed for random walk processes, where stationarity and variance changes present challenges. Our approach combines two trend estimation methods: the Hodrick Prescott (HP) filter and the l1 filter. A major challenge in these methods is selecting the tuning parameter lambda, which we address by introducing two selection techniques. For the HP based change point detection, we propose a probability-based threshold to select lambda under the assumption of an exponential distribution. For the l1 based method, we suggest a selection strategy assuming normality. Additionally, we introduce a technique to estimate the maximum number of change points in time segments using the l1 based method. We validate our methods by comparing them to similar techniques, such as PELT, using simulated data. We also demonstrate the practical application of our approach to real-world SNP stock data, showcasing its effectiveness in detecting change points.
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