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This study proposes a hybrid method that combines the advantages of the linear interpolation method and the LSTM estimation-based compensation method.
Nov 4, 2021 ， In order to estimate for missing values of time series data measured from smart meters, a total of four methods were experimented and the performance ...
Missing: Long Memory
In this paper, a stage-wise missing value treatment approach involving particle swarm optimization (PSO) comprising six stages has been proposed
In this research, we study a hybrid method that combines the advantages of the linear interpolation method and those of the LSTM estimation-based compensation ...
Method of estimation of missing data in AMI system | Semantic Scholar
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In order to estimate for missing values of time series data measured from smart meters, a total of four methods were experimented and the performance ...
People also ask
When dealing with missing data, what does the term imputation refer to?
Imputation in statistics refers to the procedure of using alternative values in place of missing data. It is referred to as "unit imputation" when replacing a data point and as "item imputation" when replacing a constituent of a data point.
How to handle mnar data?
If the data is MNAR: You must model the missingness explicitly; jointly modeling the response and missingness. In some specific cases (e.g. survival analysis), MNAR data (e.g. censored data) is handled appropriately.
What techniques can be used to handle missing data?
Removing Data. When dealing with missing data, data scientists can use two primary methods to solve the error: imputation or data removal. The imputation method substitutes reasonable guesses for missing data. It's most useful when the percentage of missing data is low.
When to use imputation for missing data?
If there are significant missingness on the baseline variable of a continuous variable, a complete case analysis may provide biased results [4]. Therefore, in all events, a single variable imputation (with or without auxiliary variables included as appropriate) is conducted if only the baseline variable is missing.
Jul 2, 2021 ， By using LSTM networks in the autoencoder, it allows the autoencoder to specialize in analyzing sequential data like timeseries. 5.2 Datasets.
Section 3 discusses data exploration, the missing values imputation approach, the data class balancing method, feature engineering, and the theoretical ...
Jun 22, 2021 ， We develop a novel technique to perform spatiotemporal missing data imputation. Using the power grid topology and timeseries data obtained from the metering ...
Dec 1, 2023 ， This paper investigates the handling of missing values in demand data, and a new approach is developed for improving the performance of demand analytics.
Mar 9, 2020 ， In [23], an enhanced DAE and long short-term memory (LSTM) based missing reconstruction framework was proposed and verified with phasor ...