Robust Prediction for Stationary Processes. 2D Enriched Version.

1987 ◽  
Author(s):  
P. Papantoni-Kazakos ◽  
Kailash Birmiwal
Keyword(s):  
Stationary Processes ◽  
Robust Prediction

Robust prediction and interpolation for vector stationary processes

1986 ◽  
Vol 72 (4) ◽  
pp. 589-602 ◽  
Author(s):  
Haralampos Tsaknakis ◽  
Dimitri Kazakos ◽  
P. Papantoni-Kazakos
Keyword(s):  
Stationary Processes ◽  
Robust Prediction

Robust Prediction and Interpolation for Vector Stationary Processes. Part 3.

1984 ◽  
Author(s):  
H. Tsaknakis ◽  
D. Kazakos ◽  
P. Papantoni-Kazakos
Keyword(s):  
Stationary Processes ◽  
Robust Prediction

Robust Prediction and Interpolation for Vector Stationary Processes.

1982 ◽  
Author(s):  
Haralampos Tsaknakis ◽  
Dimitri Kazakos ◽  
P. Papantoni-Kazakos
Keyword(s):  
Stationary Processes ◽  
Robust Prediction

CAPABILITY MEASURES FOR m -DEPENDENT STATIONARY PROCESSES

Statistics ◽  
2003 ◽  
Vol 37 (1) ◽  
pp. 1-24 ◽  
Author(s):  
SY-MIEN CHEN ◽  
YU-SHENG HSU ◽  
W. L. PEARN
Keyword(s):  
Stationary Processes

Stock Market Prediction using Data Mining Techniques

2020 ◽  
pp. 310-313
Author(s):  
Karunesh Makker ◽  
Prince Patel ◽  
Hrishikesh Roy ◽  
Sonali Borse
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Prediction Model ◽  
Efficient Market Hypothesis ◽  
Deterministic System ◽  
Vast Number ◽  
Factors Influencing ◽  
Number Of Factors ◽  
Using Data ◽  
Robust Prediction

Stock market is a very volatile in-deterministic system with vast number of factors influencing the direction of trend on varying scales and multiple layers. Efficient Market Hypothesis (EMH) states that the market is unbeatable. This makes predicting the uptrend or downtrend a very challenging task. This research aims to combine multiple existing techniques into a much more robust prediction model which can handle various scenarios in which investment can be beneficial. Existing techniques like sentiment analysis or neural network techniques can be too narrow in their approach and can lead to erroneous outcomes for varying scenarios. By combing both techniques, this prediction model can provide more accurate and flexible recommendations. Embedding Technical indicators will guide the investor to minimize the risk and reap better returns.

scholarly journals A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

2021 ◽  
Vol 13 (8) ◽  
pp. 1495
Author(s):  
Jehyeok Rew ◽  
Yongjang Cho ◽  
Eenjun Hwang
Keyword(s):  
Neural Networks ◽  
Species Distribution ◽  
Best Practice ◽  
Deep Neural Networks ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Environmental Data ◽  
Distribution Model ◽  
Distribution Models ◽  
Robust Prediction

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.

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