Self-organized critical gating of ion channels: On the origin of long-term memory in dwell time series

2006 ◽  
Vol 16 (3) ◽  
pp. 033129 ◽  
Author(s):  
A. R. Brazhe ◽  
G. V. Maksimov
Keyword(s):  
Time Series ◽  
Ion Channels ◽  
Dwell Time ◽  
Long Term Memory ◽  
Term Memory ◽  
Self Organized

Introducing a chaotic map with a wide range of long-term memory as a model of patch-clamped ion channels current time series

2019 ◽  
Vol 126 ◽  
pp. 361-368 ◽  
Author(s):  
Alireza Bahramian ◽  
Ali Nouri ◽  
Golnaz Baghdadi ◽  
Shahriar Gharibzadeh ◽  
Farzad Towhidkhah ◽  
...  
Keyword(s):  
Time Series ◽  
Ion Channels ◽  
Chaotic Map ◽  
Long Term Memory ◽  
Current Time ◽  
Term Memory ◽  
Wide Range

ASSESSMENT persistent time series hryvnia to the dollar US

2019 ◽  
Author(s):  
Serhii Ternov ◽  
Vasyl Fortuna
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Efficient Market Hypothesis ◽  
Long Term Memory ◽  
Characteristic Time Scale ◽  
Term Memory ◽  
Dollar Exchange Rate ◽  
Study Process ◽  
Daily Changes

Contemporary literature suggests that the effective market hypothesis is not substantiated. Instead, it suggests the Fractal Market Hypothesis (FMH). Fractal markets are characterized by long-term memory. The main feature of the fractal market is that the frequency distribution of the indicator looks the same across diffe­ rent investment horizons. In such cases, it is said that for an appropriate indicator, the phenomenon of scale invariance is observed. All daily changes are correlated with all future daily changes, all weekly changes are correlated with all future weekly changes. There is no characteristic time scale, a key characteristic of the time series. The presence of memory in the time series can be characterized by the Hearst indicator. This paper analyzes the hryvnia to US dollar exchange rate for the period 04.06.14-04.01.15. Finding the Hearst index made it possible to conclude that there is or is not long-term memory in this series. The presence of long-term memory indi­ cates that the efficient market hypothesis is unjustified. The hypothesis was tested that the longer the averaging intervals are taken into account in the model, the Hearst's index decreases. The analysis does not have great predictive power, however, it allows to identify the presence or absence of long-term memory in the study process and thus to accept or reject the hypothesis of an effective market. That is, the series under study is persistent, thus demonstrating long-term me­ mory availability. Thus, since persistence is revealed, the hypothesis of an effective market for the exchange rate yield is not confirmed, but instead can be argued for the fractality of the hryvnia / dollar exchange rate yield. Therefore, the application of the proposed approach made it possible to find the Hearst rate for the hryvnia / dollar exchange rate. The value found indicates that the effective market hypothesis is not substantiated for at least such an exchange rate.

scholarly journals ERROR ASSESSMENT IN MODELING WITH FRACTAL BROWNIAN MOTIONS

Fractals ◽  
2013 ◽  
Vol 21 (03n04) ◽  
pp. 1350018 ◽  
Author(s):  
BINGQIANG QIAO ◽  
SIMING LIU
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
Hurst Exponent ◽  
Long Term Memory ◽  
Error Assessment ◽  
Data Sampling ◽  
Brownian Motions ◽  
Term Memory ◽  
Rescaled Range

To model a given time series F(t) with fractal Brownian motions (fBms), it is necessary to have appropriate error assessment for related quantities. Usually the fractal dimension D is derived from the Hurst exponent H via the relation D = 2-H, and the Hurst exponent can be evaluated by analyzing the dependence of the rescaled range 〈|F(t + τ) - F(t)|〉 on the time span τ. For fBms, the error of the rescaled range not only depends on data sampling but also varies with H due to the presence of long term memory. This error for a given time series then can not be assessed without knowing the fractal dimension. We carry out extensive numerical simulations to explore the error of rescaled range of fBms and find that for 0 < H < 0.5, |F(t + τ) - F(t)| can be treated as independent for time spans without overlap; for 0.5 < H < 1, the long term memory makes |F(t + τ) - F(t)| correlated and an approximate method is given to evaluate the error of 〈|F(t + τ) - F(t)|〉. The error and fractal dimension can then be determined self-consistently in the modeling of a time series with fBms.

Forecasting the wind direction by using time series models with long-term memory (case study: Nayer region)

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Paria Soleimani ◽  
Bahareh Emami ◽  
Meysam Rafei ◽  
Hooman Shahrasbi
Keyword(s):  
Time Series ◽  
Wind Energy ◽  
Wind Direction ◽  
Fossil Fuels ◽  
Moving Average ◽  
Maximum Energy ◽  
Long Term Memory ◽  
Term Memory ◽  
Content Type

Purpose Today, because of the increasing need for the energy resources and the reduction of fossil fuels, renewable energy, especially wind energy, has attracted special attention. The precise forecasting of such energy will be the main factor in designing and investing in this field. On the other hand, the wind energy forecast provides the possibility of optimal use of available resources. In addition, the produce maximum energy would be possible by identifying wind direction and putting wind turbines in the best position. Design/methodology/approach Time series forecasting methods with long-term memory in this research have been used. Findings Eventually, the autoregressive fractionally integrated moving average (3,0,0)-FIGARCH (1,0,1) long-term memory model has more acceptable performance. The obtained error is based on the RMSE (0.2889) and the TIC (0.2605) values. Practical implications In this paper, the forecast wind direction belongs to Ardebil province and Nayer city in Iran. Originality/value The speed and direction of wind are variables that constantly change; hence, it will be difficult to predict the exact wind energy. In recent years, some studies have been conducted on wind speed forecasting, whereas wind direction forecasting has been done in a fewer number of studies. Most studies are related to low-lying areas. As the height of the wind turbine is directly related to the energy generation, 78 m height has been considered in this study.

scholarly journals Extreme event return times in long-term memory processes near 1/<i>f</i>

2008 ◽  
Vol 15 (4) ◽  
pp. 557-565 ◽  
Author(s):  
R. Blender ◽  
K. Fraedrich ◽  
F. Sienz
Keyword(s):  
Time Series ◽  
Weibull Distribution ◽  
Power Law ◽  
Extreme Event ◽  
Return Time ◽  
Specific Humidity ◽  
Long Term Memory ◽  
Term Memory ◽  
Return Times

Abstract. The distribution of extreme event return times and their correlations are analyzed in observed and simulated long-term memory (LTM) time series with 1/f power spectra. The analysis is based on tropical temperature and mixing ratio (specific humidity) time series from TOGA COARE with 1 min resolution and an approximate 1/f power spectrum. Extreme events are determined by Peak-Over-Threshold (POT) crossing. The Weibull distribution represents a reasonable fit to the return time distributions while the power-law predicted by the stretched exponential for 1/f deviates considerably. For a comparison and an analysis of the return time predictability, a very long simulated time series with an approximate 1/f spectrum is produced by a fractionally differenced (FD) process. This simulated data confirms the Weibull distribution (a power law can be excluded). The return time sequences show distinctly weaker long-term correlations than the original time series (correlation exponent γ≈0.56).

Conceptual short-term memory (CSTM) supports core claims of Christiansen and Chater

2016 ◽  
Vol 39 ◽  
Author(s):  
Mary C. Potter
Keyword(s):  
Working Memory ◽  
Rapid Serial Visual Presentation ◽  
Language Comprehension ◽  
Short Term Memory ◽  
Visual Presentation ◽  
Long Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Use Of Knowledge

AbstractRapid serial visual presentation (RSVP) of words or pictured scenes provides evidence for a large-capacity conceptual short-term memory (CSTM) that momentarily provides rich associated material from long-term memory, permitting rapid chunking (Potter 1993; 2009; 2012). In perception of scenes as well as language comprehension, we make use of knowledge that briefly exceeds the supposed limits of working memory.

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