scholarly journals Trend identification with the relative strength index (RSI) technical indicator –A conceptual study

2021 ◽  
Vol 8 (4) ◽  
pp. 159-169
Author(s):  
Ashok Kumar Panigrahi ◽  
Kushal Vachhani ◽  
Suman Kalyan Chaudhury
Keyword(s):  
Investment Strategy ◽  
Relative Strength ◽  
Medium Term ◽  
Short Term ◽  
Significance Level ◽  
Technical Indicator ◽  
Conceptual Study ◽  
Trend Identification ◽  
F Statistics

We all must agree that the word "trend" is now the buzzword of the stock market. As a part of investment strategy and analysis, it is always suggested that the investors should keep an eye on medium-term and short-term changes in addition to longer-term (secular) patterns. Traders and investors use the RSI as a momentum indicator. Overbought and oversold situations are indicated by RSI values between 70 and 30. Over the past two decades, several techniques have been developed to analyze NIFTY 50 data for investment purposes. In this paper, we have estimated the returns by looking at the two trends i.e., 50-50 and 60-40. In addition to this, how to trade and back-test our strategy is also explained. Applying these two RSI strategies to the NIFTY 50 chart revealed that 50-50 offers a higher long-term return, while 60-40 provides a superior short-term return. Finally, the strategies' returns F-statistics and P-values were calculated and analyzed to determine their significance level and acceptability.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

Sampling estuarine copepods at different scales and resolutions: a study in Rio de la Plata

2018 ◽  
Vol 99 (5) ◽  
pp. 1059-1064 ◽  
Author(s):  
Sourav Paul ◽  
Danilo Calliari
Keyword(s):  
Sampling Effort ◽  
Rio De La Plata ◽  
Population Densities ◽  
Medium Term ◽  
Short Term ◽  
Río De La Plata ◽  
La Plata ◽  
Resource Limitations ◽  
Paracalanus Parvus

AbstractIn the Rio de la Plata salinity, temperature, chlorophyll a (chl a), and densities (ind. m−3) of the copepods Acartia tonsa and Paracalanus parvus were measured from January to November in 2003 by following a nested weekly and monthly design. Such sampling yielded two separate datasets: (i) Yearly Dataset (YD) which consists of data of one sampling effort per month for 11 consecutive months, and (ii) Seasonal Weekly Datasets (SWD) which consists of data of one sampling effort per week of any four consecutive weeks within each season. YD was assumed as a medium-term low-resolution (MTLR) dataset, and SWD as a short-term high-resolution (STHR) dataset. The hypothesis was, the SWD would always capture (shorter scales generally captures more noise in data) more detail variability of copepod populations (quantified through the regression relationships between temporal changes of salinity, temperature, chl a and copepod densities) than the YD. Analysis of both YD and SWD found that A. tonsa density was neither affected by seasonal cycles, nor temporal variability of salinity, temperature and chl a. Thus, compared to STHR sampling, MTLR sampling did not yield any further information of the variability of population densities of the perennial copepod A. tonsa. Analysis of SWD found that during summer and autumn the population densities of P. parvus had a significant positive relationship to salinity but their density was limited by higher chl a concentration; analysis of YD could not yield such detailed ecological information. That hints the effectiveness of STHR sampling over MTLR sampling in capturing details of the variability of population densities of a seasonal copepod species. Considering the institutional resource limitations (e.g. lack of long-term funding, manpower and infrastructure) and the present hypothesis under consideration, the authors suggest that a STHR sampling may provide useful complementary information to interpret results of longer-term natural changes occurring in estuaries.

scholarly journals Towards the best future for humankind: ZIPAR whitepaper.

2018 ◽  
Author(s):  
Marko Kovic ◽  
Christian Caspar ◽  
Adrian Rauchfleisch
Keyword(s):  
Medium Term ◽  
Short Term ◽  
Profound Impact ◽  
Future Challenges ◽  
The Way

Humankind is facing major challenges in the short-term, medium-term, and long-term future. Those challenges will have a profound impact on humankind’s future progress and wellbeing. In this whitepaper, we outline our understanding of humankind’s future challenges, and we describe the way in which we work towards identifying as well as managing them. In doing so, we pursue the overall goal of ZIPAR: We want to make the best future for humankind (ever so slightly) more probable.

scholarly journals Intralymphatic immunotherapy for allergic rhinoconjunctivitis: a systematic review and meta-analysis

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
M.P. Hoang ◽  
K. Seresirikachorn ◽  
W. Chitsuthipakorn ◽  
K. Snidvongs
Keyword(s):  
Systematic Review ◽  
Meta Analysis ◽  
Specific Ige ◽  
Allergic Rhinoconjunctivitis ◽  
Medium Term ◽  
Short Term ◽  
Intralymphatic Immunotherapy ◽  
Specific Igg4 ◽  
Igg4 Level

BACKGROUND: Intralymphatic immunotherapy (ILIT) is a new route of allergen-specific immunotherapy. Data confirming its effect is restricted to a small number of studies. METHODOLOGY: A systematic review with meta-analysis was conducted. The short-term (less than 24 weeks), medium-term (24-52 weeks), and long-term (more than 52 weeks) effects of ILIT in patients with allergic rhinoconjunctivitis (ARC) were assessed. The outcomes were combined symptom and medication scores (CSMS), symptoms visual analog scale (VAS), disease-specific quality of life (QOL), specific IgG4 level, specific IgE level, and adverse events. RESULTS: Eleven randomized controlled trials and 2 cohorts (483 participants) were included. Compared with placebo, short term benefits of ILIT for seasonal ARC improved CSMS, improved VAS and increased specific IgG4 level but did not change QOL or specific IgE level. Medium-term effect improved VAS. Data on the long-term benefit of ILIT remain unavailable and require longer term follow-up studies. There were no clinical benefits of ILIT for perennial ARC. ILIT was safe and well-tolerated. CONCLUSION: ILIT showed short-term benefits for seasonal ARC. The sustained effects of ILIT were inconclusive. It was well tolerated.

scholarly journals A Study of Multi-Scale Relationship Between Investor Sentiment and Stock Index Fluctuation Based on the Analysis of BEMD Spillover Index

2021 ◽  
Vol 9 (4) ◽  
pp. 399-420
Author(s):  
Weiguo Chen ◽  
Shufen Zhou ◽  
Yin Zhang ◽  
Yi Sun
Keyword(s):  
Stock Price ◽  
Composite Index ◽  
Investor Sentiment ◽  
Stock Index ◽  
Financial Industry ◽  
Medium Term ◽  
Short Term ◽  
Frequency Components ◽  
Original Scale

Abstract According to behavioral finance theory, investor sentiment generally exists in investors’ trading activities and influences financial market. In order to investigate the interaction between investor sentiment and stock market as well as financial industry, this study decomposed investor sentiment, stock price index and SWS index of financial industry into IMF components at different scales by using BEMD algorithm. Moreover, the fluctuation characteristics of time series at different time scales were extracted, and the IMF components were reconstructed into short-term high-frequency components, medium-term important event low-frequency components and long-term trend components. The short-term interaction between investor sentiment and Shanghai Composite Index, Shenzhen Component Index and financial industries represented by SWS index was investigated based on the spillover index. The time difference correlation coefficient was employed to determine the medium-term and long-term correlation among variables. Results demonstrate that investor sentiment has a strong correlation with Shanghai Composite Index, Shenzhen Component Index and different financial industries represented by SWS index at the original scale, and the change of investor sentiment is mainly influenced by external market information. The interaction between most markets at the short-term scale is weaker than that at the original scale. Investor sentiment is more significantly correlated with SWS Bond, SWS Diversified Finance and Shanghai Composite Index at the long-term scale than that at the medium-term scale.

scholarly journals Novel FTLRNN with Gamma Memory for Short-Term and Long-Term Predictions of Chaotic Time Series

2009 ◽  
Vol 2009 ◽  
pp. 1-21
Author(s):  
Sanjay L. Badjate ◽  
Sanjay V. Dudul
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mean Squared Error ◽  
Chaotic Time Series ◽  
Nonstationary Time Series ◽  
Medium Term ◽  
Short Term ◽  
Squared Error ◽  
Wide Range

Multistep ahead prediction of a chaotic time series is a difficult task that has attracted increasing interest in the recent years. The interest in this work is the development of nonlinear neural network models for the purpose of building multistep chaotic time series prediction. In the literature there is a wide range of different approaches but their success depends on the predicting performance of the individual methods. Also the most popular neural models are based on the statistical and traditional feed forward neural networks. But it is seen that this kind of neural model may present some disadvantages when long-term prediction is required. In this paper focused time-lagged recurrent neural network (FTLRNN) model with gamma memory is developed for different prediction horizons. It is observed that this predictor performs remarkably well for short-term predictions as well as medium-term predictions. For coupled partial differential equations generated chaotic time series such as Mackey Glass and Duffing, FTLRNN-based predictor performs consistently well for different depths of predictions ranging from short term to long term, with only slight deterioration after k is increased beyond 50. For real-world highly complex and nonstationary time series like Sunspots and Laser, though the proposed predictor does perform reasonably for short term and medium-term predictions, its prediction ability drops for long term ahead prediction. However, still this is the best possible prediction results considering the facts that these are nonstationary time series. As a matter of fact, no other NN configuration can match the performance of FTLRNN model. The authors experimented the performance of this FTLRNN model on predicting the dynamic behavior of typical Chaotic Mackey-Glass time series, Duffing time series, and two real-time chaotic time series such as monthly sunspots and laser. Static multi layer perceptron (MLP) model is also attempted and compared against the proposed model on the performance measures like mean squared error (MSE), Normalized mean squared error (NMSE), and Correlation Coefficient (r). The standard back-propagation algorithm with momentum term has been used for both the models.

Probabilistic Computational Analysis of Transcatheter Aortic Valve Leaflet Design

2013 ◽  
Author(s):  
Kewei Li ◽  
Wei Sun
Keyword(s):  
Aortic Valve ◽  
Computational Analysis ◽  
Computational Simulation ◽  
Medium Term ◽  
Short Term ◽  
Geometry Design ◽  
Transcatheter Aortic Valve ◽  
Aortic Valve Leaflet ◽  
Improved Performance

Aortic stenosis (AS) is abnormal narrowing of the aortic valve which partially obstructs outflow of blood from the left ventricle to aorta. Symptomatic AS is associated with a high mortality rate, approximately 50% in the first 2 years, if left untreated [1, 2]. Transcatheter aortic valve (TAV) implantation has been recently developed as an effective endovascular treatment for high-risk AS patients, in which a stented bioprosthetic valve is deployed through a catheter within the diseased aortic valve. Since the first procedure in 2002 [3], there has been an explosive growth in TAV implantation. By the end of 2011, there were 10 TAV companies that had first-in-man implantation data [4]. More than 50,000 TAV implantations have been performed worldwide since 2007. Short-term and medium-term outcomes after TAV implantation are encouraging with significant reduction in rates of death. However, adverse events associated with TAV implantation were reported [5, 6]. Furthermore, long-term durability and safety of these devices are largely unknown and needed to be evaluated and studied carefully [7, 8]. It is widely accepted that valve designs that reduce leaflet stresses are likely to give improved performance in long-term applications. The objective of this study was to quantify the effect of 2D TAV leaflet geometry design on 3D valve stress distribution using probabilistic computational simulation.

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