HAAR WAVELET ANALYSIS OF CLIMATIC TIME SERIES

2014 ◽  
Vol 12 (02) ◽  
pp. 1450020 ◽  
Author(s):  
ZHIHUA ZHANG ◽  
JOHN C. MOORE ◽  
ASLAK GRINSTED
Keyword(s):  
Time Series ◽  
Arctic Oscillation ◽  
Regime Shift ◽  
Power Spectra ◽  
Haar Wavelet ◽  
Analytic Formula ◽  
Fundamental Properties ◽  
Statistical Framework ◽  
The Difference ◽  
Climatic Time Series

In order to extract the intrinsic information of climatic time series from background red noise, in this paper, we will first give an analytic formula on the distribution of Haar wavelet power spectra of red noise in a rigorous statistical framework. After that, by comparing the difference of wavelet power spectra of real climatic time series and red noise, we can extract intrinsic features of climatic time series. Finally, we use our method to analyze Arctic Oscillation (AO) which is a key aspect of climate variability in the Northern Hemisphere, and discover a great change in fundamental properties of the AO, commonly called a regime shift or tripping point.

scholarly journals Reconstructive Reservoir Computing to Detect Anomaly in Time-series Signals

2022 ◽  
Author(s):  
Junya Kato ◽  
Gouhei Tanaka ◽  
Ryosho Nakane ◽  
Akira Hirose
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Large Range ◽  
Reconstruction Error ◽  
Time Shift ◽  
Reservoir Computing ◽  
Fundamental Properties ◽  
Echo State Networks ◽  
The Difference ◽  
Normal Input

We propose reconstructive reservoir computing (RRC) for anomaly detection working for time-series signals. This paper investigates its fundamental properties with experiments employing echo state networks (ESNs). The RRC model is a reconstructor to replicate a normal input time-series signal with no delay or a certain delay (delay ≥ 0). In its anomaly detection process, we evaluate instantaneous reconstruction error defined as the difference between input and output signals at each time. Experiments with a sound dataset from industrial machines demonstrate that the error is low for normal signals while it becomes higher for abnormal ones, showing successful anomaly detection. It is notable that the RRC models’ behavior is very different from that of conventional anomaly detection models, that is, those based on forecasting (delay < 0). The error of the proposed reconstructor is explicitly lower than that of a forecaster, resulting in superior distinction between normal and abnormal states. We show that the RRC model is effective over a large range of reservoir parameters. We also illustrate the distribution of the output weights optimized through a training to discuss their roles in the reconstruction. Then, we investigate the influence of the neuronal leaking rate and the delay time shift amount on the transient response and the reconstruction error, showing high effectiveness of the reconstructor in anomaly detection. The proposed RRC will play a significant role for anomaly detection in the present and future sensor network society

scholarly journals Reconstructive Reservoir Computing to Detect Anomaly in Time-series Signals

2022 ◽  
Author(s):  
Junya Kato ◽  
Gouhei Tanaka ◽  
Ryosho Nakane ◽  
Akira Hirose
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Large Range ◽  
Reconstruction Error ◽  
Time Shift ◽  
Reservoir Computing ◽  
Fundamental Properties ◽  
Echo State Networks ◽  
The Difference ◽  
Normal Input

We propose reconstructive reservoir computing (RRC) for anomaly detection working for time-series signals. This paper investigates its fundamental properties with experiments employing echo state networks (ESNs). The RRC model is a reconstructor to replicate a normal input time-series signal with no delay or a certain delay (delay ≥ 0). In its anomaly detection process, we evaluate instantaneous reconstruction error defined as the difference between input and output signals at each time. Experiments with a sound dataset from industrial machines demonstrate that the error is low for normal signals while it becomes higher for abnormal ones, showing successful anomaly detection. It is notable that the RRC models’ behavior is very different from that of conventional anomaly detection models, that is, those based on forecasting (delay < 0). The error of the proposed reconstructor is explicitly lower than that of a forecaster, resulting in superior distinction between normal and abnormal states. We show that the RRC model is effective over a large range of reservoir parameters. We also illustrate the distribution of the output weights optimized through a training to discuss their roles in the reconstruction. Then, we investigate the influence of the neuronal leaking rate and the delay time shift amount on the transient response and the reconstruction error, showing high effectiveness of the reconstructor in anomaly detection. The proposed RRC will play a significant role for anomaly detection in the present and future sensor network society

scholarly journals A comparison of two methods for detecting abrupt changes in the variance of climatic time series

2016 ◽  
Vol 2 (1) ◽  
pp. 63-78 ◽  
Author(s):  
Sergei N. Rodionov
Keyword(s):  
Time Series ◽  
Regime Shift ◽  
Change Points ◽  
The Arctic ◽  
Monte Carlo Experiments ◽  
Shift Analysis ◽  
Starting Point ◽  
Abrupt Shifts ◽  
Climatic Time Series ◽  
The Tropics

Abstract. Two methods for detecting abrupt shifts in the variance – Integrated Cumulative Sum of Squares (ICSS) and Sequential Regime Shift Detector (SRSD) – have been compared on both synthetic and observed time series. In Monte Carlo experiments, SRSD outperformed ICSS in the overwhelming majority of the modeled scenarios with different sequences of variance regimes. The SRSD advantage was particularly apparent in the case of outliers in the series. On the other hand, SRSD has more parameters to adjust than ICSS, which requires more experience from the user in order to select those parameters properly. Therefore, ICSS can serve as a good starting point of a regime shift analysis. When tested on climatic time series, in most cases both methods detected the same change points in the longer series (252–787 monthly values). The only exception was the Arctic Ocean sea surface temperature (SST) series, when ICSS found one extra change point that appeared to be spurious. As for the shorter time series (66–136 yearly values), ICSS failed to detect any change points even when the variance doubled or tripled from one regime to another. For these time series, SRSD is recommended. Interestingly, all the climatic time series tested, from the Arctic to the tropics, had one thing in common: the last shift detected in each of these series was toward a high-variance regime. This is consistent with other findings of increased climate variability in recent decades.

EVALUASI PROYEKSI DEMAND BARANG DAN PENUMPANG PELABUHAN DI PROVINSI MALUKU

2019 ◽  
Vol 19 (2) ◽  
pp. 101-110
Author(s):  
Adrian Firdaus ◽  
M. Dwi Yoga Sutanto ◽  
Rajin Sihombing ◽  
M. Weldy Hermawan
Keyword(s):  
Time Series ◽  
Projection Method ◽  
Development Plan ◽  
Master Plan ◽  
Port Development ◽  
Loading And Unloading ◽  
The Difference ◽  
Existing Data

Abstract Every port in Indonesia must have a Port Master Plan that contains an integrated port development plan. This study discusses one important aspect in the preparation of the Port Master Plan, namely the projected movement of goods and passengers, which can be used as a reference in determining the need for facilities at each stage of port development. The case study was conducted at a port located in a district in Maluku Province and aims to evaluate the analysis of projected demand for goods and passengers occurring at the port. The projection method used is time series and econometric projection. The projection results are then compared with the existing data in 2018. The results of this study show that the econometric projection gives adequate results in predicting loading and unloading activities as well as the number of passenger arrival and departure in 2018. This is indicated by the difference in the percentage of projection results towards the existing data, which is smaller than 10%. Whereas for loading and unloading activities, time series projections with logarithmic trends give better results than econometric projections. Keywords: port, port master plan, port development, unloading activities  Abstrak Setiap pelabuhan di Indonesia harus memiliki sebuah Rencana Induk Pelabuhan yang memuat rencana pengem-bangan pelabuhan secara terpadu. Studi ini membahas salah satu aspek penting dalam penyusunan Rencana Induk Pelabuhan, yaitu proyeksi pergerakan barang dan penumpang, yang dapat dipakai sebagai acuan dalam penentuan kebutuhan fasilitas di setiap tahap pengembangan pelabuhan. Studi kasus dilakukan pada sebuah pelabuhan yang terletak di sebuah kabupaten di Provinsi Maluku dan bertujuan untuk melakukan evaluasi ter-hadap analisis proyeksi demand barang dan penumpang yang terjadi di pelabuhan tersebut. Metode proyeksi yang dipakai adalah proyeksi deret waktu dan ekonometrik. Hasil proyeksi selanjutnya dibandingkan dengan data eksisting tahun 2018. Hasil studi ini menunjukkan bahwa proyeksi ekonometrik memberikan hasil yang cukup baik dalam memprediksi aktivitas bongkar barang serta jumlah penumpang naik dan turun di tahun 2018. Hal ini diindikasikan dengan selisih persentase hasil proyeksi terhadap data eksisting yang lebih kecil dari 10%. Sedangkan untuk aktivitas muat barang, proyeksi deret waktu dengan tren logaritmik memberikan hasil yang lebih baik daripada proyeksi ekonometrik. Kata-kata kunci: pelabuhan, rencana induk pelabuhan, pengembangan pelauhan, aktivitas bongkar barang

279 WACSAW: A Statistical and Individually Adaptive Method to Determine Sleep and Wakefulness from Activity

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A111-A112
Author(s):  
Austin Vandegriffe ◽  
V A Samaranayake ◽  
Matthew Thimgan
Keyword(s):  
Time Series ◽  
Optimal Transport ◽  
Activity Patterns ◽  
Adaptive Method ◽  
Wasserstein Distance ◽  
Activity Data ◽  
Commercial Grade ◽  
Movement Data ◽  
The Difference ◽  
Levene Test

Abstract Introduction Technological innovations have broadened the type and amount of activity data that can be captured in the home and under normal living conditions. Yet, converting naturalistic activity patterns into sleep and wakefulness states has remained a challenge. Despite the successes of current algorithms, they do not fill all actigraphy needs. We have developed a novel statistical approach to determine sleep and wakefulness times, called the Wasserstein Algorithm for Classifying Sleep and Wakefulness (WACSAW), and validated the algorithm in a small cohort of healthy participants. Methods WACSAW functional routines: 1) Conversion of the triaxial movement data into a univariate time series; 2) Construction of a Wasserstein weighted sum (WSS) time series by measuring the Wasserstein distance between equidistant distributions of movement data before and after the time-point of interest; 3) Segmenting the time series by identifying changepoints based on the behavior of the WSS series; 4) Merging segments deemed similar by the Levene test; 5) Comparing segments by optimal transport methodology to determine the difference from a flat, invariant distribution at zero. The resulting histogram can be used to determine sleep and wakefulness parameters around a threshold determined for each individual based on histogram properties. To validate the algorithm, participants wore the GENEActiv and a commercial grade actigraphy watch for 48 hours. The accuracy of WACSAW was compared to a detailed activity log and benchmarked against the results of the output from commercial wrist actigraph. Results WACSAW performed with an average accuracy, sensitivity, and specificity of &gt;95% compared to detailed activity logs in 10 healthy-sleeping individuals of mixed sexes and ages. We then compared WACSAW’s performance against a common wrist-worn, commercial sleep monitor. WACSAW outperformed the commercial grade system in each participant compared to activity logs and the variability between subjects was cut substantially. Conclusion The performance of WACSAW demonstrates good results in a small test cohort. In addition, WACSAW is 1) open-source, 2) individually adaptive, 3) indicates individual reliability, 4) based on the activity data stream, and 5) requires little human intervention. WACSAW is worthy of validating against polysomnography and in patients with sleep disorders to determine its overall effectiveness. Support (if any):

Multiscale Mutual Mode Entropy Based Coupling Analysis of Alpha Rhythm Electroencephalogram

2014 ◽  
Vol 574 ◽  
pp. 718-722
Author(s):  
Ning Ji ◽  
Jun Tan ◽  
An Shan Pei ◽  
Jia Fei Dai ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Clinical Evaluation ◽  
Brain Function ◽  
Alpha Rhythm ◽  
Physiological Status ◽  
Noise Resistance ◽  
Middle Aged ◽  
Coupling Analysis ◽  
Scale Change ◽  
The Difference

This paper presents the Multiscale Mutual Mode Entropy algorithm to quantify the coupling degree between two alpha rhythm EEG time series which are simultaneously acquired. The results show that in the process of scale change, the young and middle-aged differ from each other in terms of the coupling degree of alpha rhythm EEG and the difference grow clear gradually. So the Multiscale Mutual Mode Entropy can be used to analyze the coupling information of time series under different physiological status, and it also has good noise resistance. Besides, as an indicator of measuring brain function, in the future it can also come to the aid of clinical evaluation of brain function.

scholarly journals An Efficient Prediction Model for Water Discharge in Schoharie Creek, NY

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Katerina G. Tsakiri ◽  
Antonios E. Marsellos ◽  
Igor G. Zurbenko
Keyword(s):  
New York ◽  
Time Series ◽  
Water Discharge ◽  
Winter Period ◽  
Statistical Methodology ◽  
Summer Period ◽  
Short Term ◽  
Efficient Prediction ◽  
Climatic Time Series

Flooding normally occurs during periods of excessive precipitation or thawing in the winter period (ice jam). Flooding is typically accompanied by an increase in river discharge. This paper presents a statistical model for the prediction and explanation of the water discharge time series using an example from the Schoharie Creek, New York (one of the principal tributaries of the Mohawk River). It is developed with a view to wider application in similar water basins. In this study a statistical methodology for the decomposition of the time series is used. The Kolmogorov-Zurbenko filter is used for the decomposition of the hydrological and climatic time series into the seasonal and the long and the short term component. We analyze the time series of the water discharge by using a summer and a winter model. The explanation of the water discharge has been improved up to 81%. The results show that as water discharge increases in the long term then the water table replenishes, and in the seasonal term it depletes. In the short term, the groundwater drops during the winter period, and it rises during the summer period. This methodology can be applied for the prediction of the water discharge at multiple sites.

Data Fusion of Total Solar Irradiance Composite Time Series Using 40 years of Satellite Measurements: First Results 

2021 ◽  
Author(s):  
Jean-Philippe Montillet ◽  
Wolfgang Finsterle ◽  
Werner Schmutz ◽  
Margit Haberreiter ◽  
Rok Sikonja
Keyword(s):  
Time Series ◽  
Data Fusion ◽  
Solar Irradiance ◽  
Total Solar Irradiance ◽  
Maunder Minimum ◽  
The Sun ◽  
Climate Reconstructions ◽  
The Earth ◽  
First Results ◽  
The Difference

&lt;p&gt;&lt;span&gt;Since the late 70&amp;#8217;s, successive satellite missions have been monitoring the sun&amp;#8217;s activity, recording total solar irradiance observations. These measurements are important to estimate the Earth&amp;#8217;s energy imbalance, &lt;/span&gt;&lt;span&gt;i.e. the difference of energy absorbed and emitted by our planet. Climate modelers need the solar forcing time series in their models in order to study the influence of the Sun on the Earth&amp;#8217;s climate. With this amount of TSI data, solar irradiance reconstruction models &amp;#160;can be better validated which can also improve studies looking at past climate reconstructions (e.g., Maunder minimum). V&lt;/span&gt;&lt;span&gt;arious algorithms have been proposed in the last decade to merge the various TSI measurements over the 40 years of recording period. We have developed a new statistical algorithm based on data fusion.&amp;#160;&amp;#160;The stochastic noise processes of the measurements are modeled via a dual kernel including white and coloured noise.&amp;#160;&amp;#160;We show our first results and compare it with previous releases (PMOD,ACRIM, ... ).&amp;#160;&lt;/span&gt;&lt;/p&gt;

An Experimental Study of the Forced Response of Pre- and Post-Critical Plates

1995 ◽  
Author(s):  
Kevin D. Murphy ◽  
Lawrence N. Virgin ◽  
Stephen A. Rizzi
Keyword(s):  
Experimental Study ◽  
Time Series ◽  
Lyapunov Exponent ◽  
Small Amplitude ◽  
Narrow Band ◽  
Power Spectra ◽  
Forced Response ◽  
Acoustic Excitation ◽  
Rectangular Plates ◽  
Auto Correlation

Abstract Experimental results are presented which characterize the dynamic response of homogeneous, fully clamped, rectangular plates to narrow band acoustic excitation and uniform thermal loads. Using time series, pseudo-phase projections, power spectra and auto-correlation functions, small amplitude vibrations are considered about both the pre- and post-critical states. These techniques are then employed to investigate the snap-through response. The results for snap-through suggest that the motion is temporally complex and a Lyapunov exponent calculation confirms that the motion is chaotic. Finally, a snap-through boundary is mapped in the (ω, SPL) parameter space separating the regions of snap-through and no snap-through.

scholarly journals Revisiting Climate Region Definitions via Clustering

2009 ◽  
Vol 22 (7) ◽  
pp. 1787-1800 ◽  
Author(s):  
Robert Lund ◽  
Bo Li
Keyword(s):  
Time Series ◽  
Clustering Methods ◽  
Distance Metric ◽  
Climatic Zones ◽  
Time Series Clustering ◽  
Climate Region ◽  
Covariate Effects ◽  
Weather Stations ◽  
Climatic Time Series ◽  
Natural Way

Abstract This paper introduces a new distance metric that enables the clustering of general climatic time series. Clustering methods have been frequently used to partition a domain of interest into distinct climatic zones. However, previous techniques have neglected the time series (autocorrelation) component and have also handled seasonal features in a suboptimal way. The distance proposed here incorporates the seasonal mean and autocorrelation structures of the series in a natural way; moreover, trends and covariate effects can be considered. As an important by-product, the methods can be used to statistically assess whether two stations can serve as reference stations for one another. The methods are illustrated by partitioning 292 weather stations within the state of Colorado into six different zones.

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