scholarly journals Fuzzy Transfer Learning in Time Series Forecasting for Stock Market Prices

2021 ◽  
Author(s):  
Shanoli Samui Pal ◽  
Samarjit Kar
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Prior Knowledge ◽  
Transfer Learning ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Series Data ◽  
Data Sets ◽  
Market Data

Abstract Transfer learning involves transferring prior knowledge of solving similar problems in order to achieve quick and efficient solution. The aim of fuzzy transfer learning is to transfer prior knowledge in an imprecise environment. Time series like stock market data are non-linear in nature and movement of stock is uncertain, so it is quite difficult following the stock market and in decision making. In this study, we propose a method to forecast stock market time series in the situation when we can use prior experience to make decisions. Fuzzy transfer learning (FuzzyTL) is based on knowledge transfer in that and adapting rules obtained domain. Three different stock market time series data sets are used for comparative study. It is observed that the effect of knowledge transferring works well together with smoothing of dependent attributes as the stock market data fluctuate with time. Finally, we give an empirical application in Shenzhen stock market with larger data sets to demonstrate the performance of the model. We have explored FuzzyTL in time series prediction to unerstand the essence of FuzzyTL. We were working on the question of the capability of FuzzyTL in improving prediction accuracy. From the comparisons, it can be said fuzzy transfer learning with smoothing improves prediction accuracy efficiently.

scholarly journals Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 876 ◽  
Author(s):  
Renzhuo Wan ◽  
Shuping Mei ◽  
Jun Wang ◽  
Min Liu ◽  
Fan Yang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Multivariate Time Series ◽  
Series Data ◽  
Asymmetric Structure ◽  
Convolutional Network

Multivariable time series prediction has been widely studied in power energy, aerology, meteorology, finance, transportation, etc. Traditional modeling methods have complex patterns and are inefficient to capture long-term multivariate dependencies of data for desired forecasting accuracy. To address such concerns, various deep learning models based on Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) methods are proposed. To improve the prediction accuracy and minimize the multivariate time series data dependence for aperiodic data, in this article, Beijing PM2.5 and ISO-NE Dataset are analyzed by a novel Multivariate Temporal Convolution Network (M-TCN) model. In this model, multi-variable time series prediction is constructed as a sequence-to-sequence scenario for non-periodic datasets. The multichannel residual blocks in parallel with asymmetric structure based on deep convolution neural network is proposed. The results are compared with rich competitive algorithms of long short term memory (LSTM), convolutional LSTM (ConvLSTM), Temporal Convolution Network (TCN) and Multivariate Attention LSTM-FCN (MALSTM-FCN), which indicate significant improvement of prediction accuracy, robust and generalization of our model.

scholarly journals Approaching Stock Market Trading with Echo State Networks

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Dev Patel ◽  
Krish Patel ◽  
Charles Dela Cuesta
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Prediction Models ◽  
Time Series Data ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Modern Society ◽  
Series Data ◽  
Echo State Networks ◽  
The Us

The US stock market is an integral part of modern society. Nearly 55% of Americans  own corporate shares in the US stock market (What Percentage of Americans Own Stock?, 2019), and as of June 30th, 2020, the total value of the US stock market was over 35 trillion USD (Total Market Value of U.S. Stock Market, 2020). The stock market is also extremely volatile, and many people have gone bankrupt from poor investments. To minimize the risk and capitalize off the massive amounts of data on corporations and share prices present in the world, algorithmic trading began to rise. Trading algorithms have the potential for huge returns, and while many algorithms employ strategies like Long-Short Equity, very few attempt to use machine learning due to the unpredictable nature of the stock market. Many time series prediction models like autoregressive integrated moving average (ARIMA), and even neural networks like long short term memory (LSTMs) often fail when predicting stock market data, because unlike other time series data, the stock market is almost never univariate, or follows seasonal trends. However, where other models come short, echo state networks (ESNs) excel, due to their reservoir like computing model, which allows them to perform better on messy, non traditional time series data. Using a combination of ESNs to predict prices, and clustering we created an algorithm model that can predict trends with over 95% confidence, but had mixed results accurately predicting returns.

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

scholarly journals Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests

2020 ◽  
Vol 12 (11) ◽  
pp. 1876 ◽  
Author(s):  
Katsuto Shimizu ◽  
Tetsuji Ota ◽  
Nobuya Mizoue ◽  
Hideki Saito
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Forest Monitoring ◽  
Series Data ◽  
Landsat 8 ◽  
Multi Temporal ◽  
Sentinel 2

Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.

scholarly journals Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems

2017 ◽  
Author(s):  
Anthony Szedlak ◽  
Spencer Sims ◽  
Nicholas Smith ◽  
Giovanni Paternostro ◽  
Carlo Piermarocchi
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Cell Cycle ◽  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Data Sets ◽  
Expression Data ◽  
Time Series Gene Expression ◽  
Human Cervical Cancer

AbstractModern time series gene expression and other omics data sets have enabled unprecedented resolution of the dynamics of cellular processes such as cell cycle and response to pharmaceutical compounds. In anticipation of the proliferation of time series data sets in the near future, we use the Hopfield model, a recurrent neural network based on spin glasses, to model the dynamics of cell cycle in HeLa (human cervical cancer) and S. cerevisiae cells. We study some of the rich dynamical properties of these cyclic Hopfield systems, including the ability of populations of simulated cells to recreate experimental expression data and the effects of noise on the dynamics. Next, we use a genetic algorithm to identify sets of genes which, when selectively inhibited by local external fields representing gene silencing compounds such as kinase inhibitors, disrupt the encoded cell cycle. We find, for example, that inhibiting the set of four kinases BRD4, MAPK1, NEK7, and YES1 in HeLa cells causes simulated cells to accumulate in the M phase. Finally, we suggest possible improvements and extensions to our model.Author SummaryCell cycle – the process in which a parent cell replicates its DNA and divides into two daughter cells – is an upregulated process in many forms of cancer. Identifying gene inhibition targets to regulate cell cycle is important to the development of effective therapies. Although modern high throughput techniques offer unprecedented resolution of the molecular details of biological processes like cell cycle, analyzing the vast quantities of the resulting experimental data and extracting actionable information remains a formidable task. Here, we create a dynamical model of the process of cell cycle using the Hopfield model (a type of recurrent neural network) and gene expression data from human cervical cancer cells and yeast cells. We find that the model recreates the oscillations observed in experimental data. Tuning the level of noise (representing the inherent randomness in gene expression and regulation) to the “edge of chaos” is crucial for the proper behavior of the system. We then use this model to identify potential gene targets for disrupting the process of cell cycle. This method could be applied to other time series data sets and used to predict the effects of untested targeted perturbations.

scholarly journals A Predictive Meta Model for Forecasting Stock Price using Time Series Data

2021 ◽  
Vol 10 (1) ◽  
pp. 345-356
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Price ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Meta Model ◽  
Price Range ◽  
Stock Traders ◽  
The Right

Stock market prediction through time series is a challenging as well as an interesting research areafor the finance domain, through which stock traders and investors can find the right time to buy/sell stocks. However, various algorithms have been developed based on the statistical approach to forecast the time series for stock data, but due to the volatile nature and different price ranges of the stock price one particular algorithm is not enough to visualize the prediction. This study aims to propose a model that will choose the preeminent algorithm for that particular company’s stock that can forecastthe time series with minimal error. This model can assist a trader/investor with or without expertise in the stock market to achieve profitable investments. We have used the Stock data from Stock Exchange Bangladesh, which covers 300+ companies to train and test our system. We have classified those companies based on the stock price range and then applied our model to identify which algorithm suites most for a particular range of stock price. Comparative forecasting results of all algorithms in diverse price ranges have been presented to show the usefulness of this Predictive Meta Model

Intra-domain and cross-domain transfer learning for time series

2021 ◽  
Author(s):  
Erik Otović ◽  
Marko Njirjak ◽  
Dario Jozinović ◽  
Goran Mauša ◽  
Alberto Michelini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Transfer Learning ◽  
Time Series Data ◽  
The Other ◽  
Series Data ◽  
Sound Recognition ◽  
Transfer Of Knowledge ◽  
Learning Models ◽  
Machine Learning Models

<p>In this study, we compared the performance of machine learning models trained using transfer learning and those that were trained from scratch - on time series data. Four machine learning models were used for the experiment. Two models were taken from the field of seismology, and the other two are general-purpose models for working with time series data. The accuracy of selected models was systematically observed and analyzed when switching within the same domain of application (seismology), as well as between mutually different domains of application (seismology, speech, medicine, finance). In seismology, we used two databases of local earthquakes (one in counts, and the other with the instrument response removed) and a database of global earthquakes for predicting earthquake magnitude; other datasets targeted classifying spoken words (speech), predicting stock prices (finance) and classifying muscle movement from EMG signals (medicine).<br>In practice, it is very demanding and sometimes impossible to collect datasets of tagged data large enough to successfully train a machine learning model. Therefore, in our experiment, we use reduced data sets of 1,500 and 9,000 data instances to mimic such conditions. Using the same scaled-down datasets, we trained two sets of machine learning models: those that used transfer learning for training and those that were trained from scratch. We compared the performances between pairs of models in order to draw conclusions about the utility of transfer learning. In order to confirm the validity of the obtained results, we repeated the experiments several times and applied statistical tests to confirm the significance of the results. The study shows when, within the set experimental framework, the transfer of knowledge brought improvements in terms of model accuracy and in terms of model convergence rate.<br><br>Our results show that it is possible to achieve better performance and faster convergence by transferring knowledge from the domain of global earthquakes to the domain of local earthquakes; sometimes also vice versa. However, improvements in seismology can sometimes also be achieved by transferring knowledge from medical and audio domains. The results show that the transfer of knowledge between other domains brought even more significant improvements, compared to those within the field of seismology. For example, it has been shown that models in the field of sound recognition have achieved much better performance compared to classical models and that the domain of sound recognition is very compatible with knowledge from other domains. We came to similar conclusions for the domains of medicine and finance. Ultimately, the paper offers suggestions when transfer learning is useful, and the explanations offered can provide a good starting point for knowledge transfer using time series data.</p>

An Efficient Method for Forecasting Using Fuzzy Time Series

2017 ◽  
pp. 287-304 ◽  
Author(s):  
Pritpal Singh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Weather Forecasting ◽  
Small Error ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Data Sets ◽  
Proposed Model ◽  
Temperature Forecasting ◽  
The University

Forecasting using fuzzy time series has been applied in several areas including forecasting university enrollments, sales, road accidents, financial forecasting, weather forecasting, etc. Recently, many researchers have paid attention to apply fuzzy time series in time series forecasting problems. In this paper, we present a new model to forecast the enrollments in the University of Alabama and the daily average temperature in Taipei, based on one-factor fuzzy time series. In this model, a new frequency based clustering technique is employed for partitioning the time series data sets into different intervals. For defuzzification function, two new principles are also incorporated in this model. In case of enrollments as well daily temperature forecasting, proposed model exhibits very small error rate.

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