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A Smart Methodology for Handling Missing Data in Time Series Datasets

Thesis Info

Access Option

External Link

Author

Muhammad Rehan Naeem

Institute

Virtual University of Pakistan

Institute Type

Public

City

Lahore

Province

Punjab

Country

Pakistan

Thesis Completing Year

2018

Thesis Completion Status

Completed

Subject

Software Engineering

Language

English

Link

http://vspace.vu.edu.pk/detail.aspx?id=255

Added

2021-02-17 19:49:13

Modified

2024-03-24 20:25:49

ARI ID

1676721006838

Similar


Missing information or missing data occurs when no data value is stored for any variable in the dataset. It is usually occurred and can have a substantial effect on the decisions. These days the treatment of the missing data is a real issue. In data quality, a severe threat is missing data which have a direct impact on the certainty of what is being presented to the end user. Imputation of missing values from predictive strategies created the idea that the influences accredited have some association with actual data. If these values did not have an association with actual data, it could produce uncertain results. There are different factual strategies of machine learning techniques like neural systems; genetic programming, and data mining strategies, which are utilized to impute such missing perceptions. In data analytics, predictive methodologies are exceptionally attractive because it can prompt better results. If there are missing data values in datasets certain machine algorithms cannot be applied to predict and forecast data. We can use different time series datasets of different organizations listed in the Pakistan Stock Exchange (PSX). The missing values can be discovered in these selected datasets. We have developed algorithms that can fill the missing dates in the datasets because some of the forecasting algorithms did not initially predict with missing values. The datasets with none missing values can be used for the training of some machine learning algorithms. The results based on different datasets of PSX can be used to forecast the next day value of the stock.
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مانا کہ زمانہ ستم ایجاد بہت ہے

مانا کہ زمانہ ستم ایجاد بہت ہے
مجھ کو شہہِ کونینؐ کی امداد بہت ہے

آیا تھا تصور میں بس اک پل کو مدینہ
دل ہے کہ اُسی لمحے سے آباد بہت ہے

بیکار ہے افکار کی دُنیا میں بھٹکنا
مجھ کو تو حضورؐ آپؐ کا ارشاد بہت ہے

کچھ اِس کے سنورنے کا بھی سامان ہو آقا ؐ
بستی جو زمیں پر ہے وُہ برباد بہت ہے

ایمان کی شمعیں ہوں فروزاں دل و جاں میں
اِس دورِ سیہ رنگ میں الحاد بہت ہے

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