A quantum approach for time series data based on graph and Schrödinger equations methods

2018 ◽  
Vol 33 (35) ◽  
pp. 1850208 ◽  
Author(s):  
Pritpal Singh ◽  
Gaurav Dhiman ◽  
Amandeep Kaur
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Error Rates ◽  
Series Data ◽  
Quantum Model ◽  
Effective Solution ◽  
Proposed Model ◽  
Quantum Approach ◽  
Speed Up ◽  
Competing Models

The supremacy of quantum approach is able to solve the problems which are not practically feasible on classical machines. It suggests a significant speed up of the simulations and decreases the chance of error rates. This paper introduces a new quantum model for time series data which depends on the appropriate length of intervals. To provide effective solution of this problem, this study suggests a new graph-based quantum approach. This technique is useful in discretization and representation of logical relationships. Then, we divide these logical relations into various groups to obtain efficient results. The proposed model is verified and validated with various approaches. Experimental results signify that the proposed model is more precise than existing competing models.

A hybrid fuzzy quantum time series and linear programming model: Special application on TAIEX index dataset

2019 ◽  
Vol 34 (25) ◽  
pp. 1950201 ◽  
Author(s):  
Pritpal Singh ◽  
Gaurav Dhiman ◽  
Sen Guo ◽  
Ritika Maini ◽  
Harsimran Kaur ◽  
...  
Keyword(s):  
Time Series ◽  
Linear Programming ◽  
Real Time ◽  
Time Series Data ◽  
Programming Model ◽  
Series Data ◽  
Quantum Model ◽  
Quantum Time ◽  
Degree Of Membership ◽  
Quantum Approach

The supremacy of quantum approach is able to provide the solutions which are not practically feasible on classical machines. This paper introduces a novel quantum model for time series data which depends on the appropriate length of intervals. In this study, the effects of these drawbacks are elaborately illustrated, and some significant measures to remove them are suggested, such as use of degree of membership along with mid-value of the interval. All these improvements signify the effective results in case of quantum time series, which are verified and validated with real-time datasets.

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.

Time Series Data Mining Implemented on Football Match

2010 ◽  
Vol 26-28 ◽  
pp. 98-103 ◽  
Author(s):  
Ben Cheng Chai
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Data Mining Technique ◽  
Time Series Data Mining ◽  
Proposed Model ◽  
Interesting Pattern ◽  
Football Match ◽  
The One

This study utilizes time series data mining to find the interesting pattern and cooperation custom. Meanwhile, data mining technique and some special football skills such as ball possession are employed to build a novel decision model in football match. The proposed model is expatiated through real football match. In short, on the one hand, the model provides a feasible route to guide the decision makers including football coach to establish effective mechanism in football match. On the other hand, it extends the application scope of time series data mining.

scholarly journals A new model for learning-based forecasting procedure by combining k-means clustering and time series forecasting algorithms

2021 ◽  
Vol 7 ◽  
pp. e534
Author(s):  
Kristoko Dwi Hartomo ◽  
Yessica Nataliani
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Time Series Forecasting ◽  
Series Data ◽  
Improvement Rate ◽  
New Model ◽  
Average Improvement ◽  
Proposed Model

This paper aims to propose a new model for time series forecasting that combines forecasting with clustering algorithm. It introduces a new scheme to improve the forecasting results by grouping the time series data using k-means clustering algorithm. It utilizes the clustering result to get the forecasting data. There are usually some user-defined parameters affecting the forecasting results, therefore, a learning-based procedure is proposed to estimate the parameters that will be used for forecasting. This parameter value is computed in the algorithm simultaneously. The result of the experiment compared to other forecasting algorithms demonstrates good results for the proposed model. It has the smallest mean squared error of 13,007.91 and the average improvement rate of 19.83%.

scholarly journals Forecasting of Tomato Yields Using Attention-Based LSTM Network and ARMA Model

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1576
Author(s):  
Wanhyun Cho ◽  
Sangkyuoon Kim ◽  
Myunghwan Na ◽  
Inseop Na
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Environmental Factors ◽  
Environmental Variables ◽  
Time Series Data ◽  
Arma Model ◽  
Series Data ◽  
Proposed Model ◽  
Lstm Network ◽  
Statistical Time

Nonlinear autoregressive exogenous (NARX), autoregressive integrated moving average (ARIMA) and multi-layer perceptron (MLP) networks have been widely used to predict the appearance value of future points for time series data. However, in recent years, new approaches to predict time series data based on various networks of deep learning have been proposed. In this paper, we tried to predict how various environmental factors with time series information affect the yields of tomatoes by combining a traditional statistical time series model and a deep learning model. In the first half of the proposed model, we used an encoding attention-based long short-term memory (LSTM) network to identify environmental variables that affect the time series data for tomatoes yields. In the second half of the proposed model, we used the ARMA model as a statistical time series analysis model to improve the difference between the actual yields and the predicted yields given by the attention-based LSTM network at the first half of the proposed model. Next, we predicted the yields of tomatoes in the future based on the measured values of environmental variables given during the observed period using a model built by integrating the two models. Finally, the proposed model was applied to determine which environmental factors affect tomato production, and at the same time, an experiment was conducted to investigate how well the yields of tomatoes could be predicted. From the results of the experiments, it was found that the proposed method predicts the response value using exogenous variables more efficiently and better than the existing models. In addition, we found that the environmental factors that greatly affect the yields of tomatoes are internal temperature, internal humidity, and CO2 level.

scholarly journals SRPM–CNN: a combined model based on slide relative position matrix and CNN for time series classification

2021 ◽  
Author(s):  
Taoying Li ◽  
Yuqi Zhang ◽  
Ting Wang
Keyword(s):  
Time Series ◽  
Relative Position ◽  
Time Series Data ◽  
Series Data ◽  
Time Series Classification ◽  
Combined Model ◽  
Daily Work ◽  
Mining Areas ◽  
Model Based ◽  
Proposed Model

AbstractResearch on the time series classification is gaining an increased attention in the machine learning and data mining areas due to the existence of the time series data almost everywhere, especially in our daily work and life. Recent studies have shown that the convolutional neural networks (CNN) can extract good features from the images and texts, but it often encounters the problem of low accuracy, when it is directly employed to solve the problem of time series classification. In this pursuit, the present study envisaged a novel combined model based on the slide relative position matrix and CNN for time series. The proposed model first adopted the slide relative position for converting the time series data into 2D images during preprocessing, and then employed CNN to classify these images. This made the best of the temporal sequence characteristic of time series data, thereby utilizing the advantages of CNN in image recognition. Finally, 14 UCR time series datasets were chosen to evaluate the performance of the proposed model, whose results indicate that the accuracy of the proposed model was higher than others.

scholarly journals Simultaneous Forecasting of Meteorological Data Based on a Self-Organizing Incremental Neural Network

2018 ◽  
Vol 22 (6) ◽  
pp. 900-906 ◽  
Author(s):  
Wonjik Kim ◽  
Osamu Hasegawa ◽  
◽  
◽  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wind Speed ◽  
Atmospheric Pressure ◽  
Time Series Data ◽  
Meteorological Data ◽  
Series Data ◽  
Total Solar Radiation ◽  
Proposed Model ◽  
Self Organizing

In this study, we propose a simultaneous forecasting model for meteorological time-series data based on a self-organizing incremental neural network (SOINN). Meteorological parameters (i.e., temperature, wet bulb temperature, humidity, wind speed, atmospheric pressure, and total solar radiation on a horizontal surface) are considered as input data for the prediction of meteorological time-series information. Based on a SOINN within normalized-refined-meteorological data, proposed model succeeded forecasting temperature, humidity, wind speed and atmospheric pressure simultaneously. In addition, proposed model does not take more than 2 s in training half-year period and 15 s in testing half-year period. This paper also elucidates the SOINN and the algorithm of the learning process. The effectiveness of our model is established by comparison of our results with experimental results and with results obtained by another model. Three advantages of our model are also described. The obtained information can be effective in applications based on neural networks, and the proposed model for handling meteorological phenomena may be helpful for other studies worldwide including energy management system.

scholarly journals ENROLLMENT FORECASTING BASED ON LINGUISTIC TIME SERIES

2020 ◽  
Vol 36 (2) ◽  
pp. 119-137
Author(s):  
Nguyen Duy Hieu ◽  
Nguyen Cat Ho ◽  
Vu Nhu Lan
Keyword(s):  
Time Series ◽  
Fuzzy Sets ◽  
Time Series Data ◽  
Historical Data ◽  
Fuzzy Model ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Forecasting Model ◽  
Proposed Model

Dealing with the time series forecasting problem attracts much attention from the fuzzy community. Many models and methods have been proposed in the literature since the publication of the study by Song and Chissom in 1993, in which they proposed fuzzy time series together with its fuzzy forecasting model for time series data and the fuzzy formalism to handle their uncertainty. Unfortunately, the proposed method to calculate this fuzzy model was very complex. Then, in 1996, Chen proposed an efficient method to reduce the computational complexity of the mentioned formalism. Hwang et al. in 1998 proposed a new fuzzy time series forecasting model, which deals with the variations of historical data instead of these historical data themselves. Though fuzzy sets are concepts inspired by fuzzy linguistic information, there is no formal bridge to connect the fuzzy sets and the inherent quantitative semantics of linguistic words. This study proposes the so-called linguistic time series, in which words with their own semantics are used instead of fuzzy sets. By this, forecasting linguistic logical relationships can be established based on the time series variations and this is clearly useful for human users. The effect of the proposed model is justified by applying the proposed model to forecast student enrollment historical data.

scholarly journals Predicting failures in electronic water taps in rural sub-Saharan African communities: an LSTM-based approach

2020 ◽  
Vol 82 (12) ◽  
pp. 2776-2785
Author(s):  
N. M. Offiong ◽  
Y. Wu ◽  
F. A. Memon
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Short Term Memory ◽  
Service Providers ◽  
Sub Saharan Africa ◽  
Series Data ◽  
Proposed Model ◽  
Sub Saharan ◽  
Failure Predictions ◽  
Solar Powered

Abstract There is a growing need to sustain solar-powered water taps in most parts of the sub-Saharan Africa. The frequent failure of the water taps gives rise to intermittent water supply and poor service delivery by the water service providers. The challenge is to foresee and predict the failure of these water systems before they occur. This study develops a scalable machine-learning model for failure prediction in electronic water taps to ensure timely maintenance of the taps. Specifically, we develop a model based on long short-term memory (LSTM) to efficiently make failure predictions with noisy heterogeneous time-series data from rural water taps. Results from the experiment prove that the proposed model can effectively classify activities and patterns in various time-series datasets. With the proposed model, the failures of the solar-powered taps due to abnormal events can be successfully predicted well in advance, with an accuracy of 78.54%. Based on the data analyses, common causes of failures are presented.

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