A refined method of forecasting based on high-order intuitionistic fuzzy time series data

A Novel Fuzzy Time Series Forecasting Model Based on Multiple Linear Regression and Time Series Clustering

“…Moreover, since most of the IFS-based [16,18] and HFSbased [17,19] models are experimented on the dataset of State Bank of India (SBI) at BSE, we compared our model with other models by using the market prices of SBI share at BSE. As the experiment in [19], we took one sample per month as testing sample, and others as training samples.…”

“…Also, the proposed model shows a better robustness to the missing values than other two models. [4] Average -based lengths 220 80 60 110 112 79 54 148 167 149 117.9 Distribution-based lengths 270 79 60 105 132 79 52 149 159 159 124.4 Weighted model [4] Average [13] 187.26 8.26 0.8839 0.7813 Huarng's model [15] 164.04 6.29 0.9110 0.8314 Pathak's model [14] 205.96 8.95 0.8685 0.7544 Kumar's model [16] 134.28 6.30 0.9446 0.8924 Bisht's model [17] 179.03 7.86 0.9001 0.8101 Abhishekh's model [18] 153.77 8.53 0.8442 0.7319 Kumar's model [19] 192.98 7.86 0.9001 0.8101 Proposed model 105.62 5.78 0.8651 0.7542…”

“…Compute Δw and Δb for all weights and offsets (17) Update network weights and offsets (18) End for (19) End for (20) Until RMSE is small enough or fallen into a local minimum ALGORITHM 1: Learning algorithm of the proposed model.…”

“…However, considering the instability of the data sources and the unreliability of data collecting process, most of the collecting data contain incomplete, imprecise, and ambiguous records, which makes preprocessing an indispensable procedure for machine learning. us, forecasting methods based on fuzzy time series have been proposed to cope with uncertainties caused by vagueness, ambiguity, and other nonprobabilistic reasons and widely applied in finance domain, such as Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) forecasting [3][4][5][6][7][8][9][10][11], the NTD/USD exchange rates forecasting [6,8], and the market price of shares of the State Bank of India (SBI) at the Bombay Stock Exchange (BSE) forecasting [12][13][14][15][16][17][18][19].…”

Prescriptive analytics for impulsive behaviour prevention using real-time biometrics

“…Deep Neural Networks are known for their high accuracy in retrieving hidden patterns in data and trends over time series data and thus have proven to have nearly 80% efficiency in prediction and classification depending on the type of biometrics and the class of related disease [ 15 ]. Studies have been attempted to achieve prediction of pathology by processing medical data as fuzzy time series data [ 2 ]…”