Uncertain Time Series in Weather Prediction

Radzuan, Nabilah Filzah Mohd; Othman, Zalinda; Bakar, Azuraliza Abu

doi:10.1016/j.protcy.2013.12.228

Cited by 15 publications

(6 citation statements)

References 26 publications

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“…Uncertainty is a basic feature of automatic and semi-automatic data processes [8]. There are many solutions have been proposed in order to reduce uncertainty because of risk in losing information and misleading results [9]. The uncertain time series is also a non-negative and precisely different ways in some fields.…”

Section: Research Relatedmentioning

confidence: 99%

Approaches of Handling Uncertain Time Series Data towards Prediction

Nabilah¹,

Othman²,

Azuraliza³

2016

IJFCC

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Section: Research Relatedmentioning

confidence: 99%

Approaches of Handling Uncertain Time Series Data towards Prediction

Nabilah¹,

Othman²,

Azuraliza³

2016

IJFCC

Self Cite

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“…Researcher in manufacturing tries to survey any techniques that have been proposed specifically for modelling and processing TS for temporal data. The determination of knowledge from TS can be used in weather precipitation or prediction by the researcher for human being benefits [8]. Rainfall modeling is an important topic in hydrology research.…”

Section: Introductionmentioning

confidence: 99%

Time Series Analysis of Large Scale Rainfall Data Using Regression Automata Models

Manepalli¹,

Subramanian²

2018

IJIES

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Rainfall is considered as the most important phenomena of the climate system. Due to the lack of adequate irrigation facilities, agriculture becomes vulnerable, which is the backbone of a country's economy. The rainfall can be able to predict by using selective appropriate predictors. Though several models have been developed for forecasting and predicting in Time Series (TS), there is no ideal model to predict the rainfall. In recent years, Automata is useful for forecasting and prediction of hydrological TS because automata help to predict the rainfall from the uncertainty data. The motivation of this work is to design a reliable tool for predicting daily rainfall in advance using Regression Automata (RA) models. The proposed method uses three different RA models for predicting rainfall from the collected data for four stations in Queensland State. The results clearly show that the all the three RA models can predict the rainfall very efficiently in various terms such as error rate, coefficients and mean square error.

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“…The point of the Time Series Analysis (TSA) is to plan TS information in order to learn, fit low dimensional models, and make gauges [12]. An analyst in assembling attempts to review any strategies that have been proposed, particularly to model and handling TS of worldly information [13]. The point of the proposed work is to outline a powerful portrayal system for time-series with dimensionality reduction utilizing MapReduce and furthermore plan proficient Automata based framework for Modeling Time series.…”

Section: Introductionmentioning

confidence: 99%

Map reduce technique for parallel-automata analysis of large scale rainfall data

Manepalli

Subramanian

2018

IJET

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Vast scale rainfall information assumes an imperative part in farming field thus early expectation of rainfall is important for the better finan-cial development of a nation. Rainfall expectation is an expert among the most troublesome issue far and wide in a year back. This data is generally secured in the unstructured course of action. Along these, tremendous measure of data has been accumulated and archived. Thus, storage and handling of such tremendous information for accurate rainfall forecast are a major test. Big Data innovation like Hadoop have developed to fathom the difficulties and issues of huge information utilizing distributed computing. Till date few examinations have been accounted for on the preparing of vast scale rainfall information utilizing MapReduce. In this paper, the huge scale rainfall information is anticipated by utilizing MapReduce system which plays out the capacities which are required and diminishes the task to get proficient ar-rangements through taking the information and isolating into smaller tasks. At that point, the three Regression Automata (RA) algorithms such as Linear Regression automata, Support Vector Regression Automata and Logistic Regression Automata. are utilized to forecast the future esteem of large scale rainfall data. The proposed framework serves as a tool that takes in the rainfall information from diminished information as input and predicts the future rainfall. The outcomes obviously demonstrate that the all the three RA models can anticipate the rainfall productively in different terms, such as, error rate, coefficients and mean square error.

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Uncertain Time Series in Weather Prediction

Cited by 15 publications

References 26 publications

Approaches of Handling Uncertain Time Series Data towards Prediction

Approaches of Handling Uncertain Time Series Data towards Prediction

Time Series Analysis of Large Scale Rainfall Data Using Regression Automata Models

Map reduce technique for parallel-automata analysis of large scale rainfall data

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