Dimensionality reduction for multivariate time-series data mining

Wan, Xiaoji; Li, Hailin; Zhang, Liping; Wu, Yenchun Jim

doi:10.1007/s11227-021-04303-4

Cited by 8 publications

(2 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As reducing dimensional has become increasingly critical for efficient similarity searches in large time series databases, our focus is to obtain a reduced dimension representation. Results of experiments which includes classification, analysis of retained information and comparison of time consumption of CPU, concludes PCA method for dimensionality reduction is superior for multivariate time series data [25]. PCA method generally depends on covariance or correlation matrix of the data, for time series data relationships between variables can be nonlinear and change over time, hence the temporal dependencies between variables in time series data may get ignored.…”

Section: Feature Reductionmentioning

confidence: 99%

Deep learning approach to forecast air pollution based on novel hourly index

Narkhede,

Hiwale

2023

Phys. Scr.

View full text Add to dashboard Cite

Air pollution is a pressing concern that the entire world is striving to combat. Among air pollutants, particulate matter poses a significant threat to human health. The Sustainable Development Goals (SGD3, SGD7 and SGD11) include initiatives to address air pollution. Two innovative methods are proposed in this research to predict the PM2.5 concentration in advance. While multivariate time series prediction models typically employ multiple features as inputs, this research reduces the number of inputs, which makes the proposed combination of approaches simple and effective. The approaches involve the development of two new indexing methods, namely, the Hourly Relative Mean Index and the Hourly Weighted Index. This research offers innovative hybrid deep learning models that incorporate the newly developed indices, Long Short Term Memory (LSTM) models, and robust preprocessing techniques. Multivariate Isolation Forest Relative Index Bidirectional LSTM and Multivariate Isolation Forest Weighted Index LSTM methods are used to forecast PM2.5 concentration for an hourly time frame. Further, Multivariate Isolation Forest Relative Index LSTM and Multivariate Isolation Forest Weighted Index LSTM methods are used to forecast PM2.5 concentration 48 hours ahead. The study establishes that the proposed combination of approaches outperform traditional ways to achieve superior performance with reduced complexities requiring fewer inputs for predictions.

show abstract

Section: Feature Reductionmentioning

confidence: 99%

Deep learning approach to forecast air pollution based on novel hourly index

Narkhede,

Hiwale

2023

Phys. Scr.

View full text Add to dashboard Cite

show abstract

“…Dimensionality reduction for extracting the abstract and high-level features to feed the subsequent modules of the prediction models has been studied in many research works such as [39,46]. In [46], Zhang et al applied principal component analysis (PCA) to perform dimensionality reduction and extract the abstract and high-level features to feed the next module of their framework, an LSTM predicting the next trading day's close price.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian-based classification framework for financial time series trend prediction

et al. 2022

View full text Add to dashboard Cite

Financial time series have been extensively studied within the past decades; however, the advent of machine learning and deep neural networks opened new horizons to apply supercomputing techniques to extract more insights from the underlying patterns of price data. This paper presents a tri-state labeling approach to classify the underlying patterns in price data into up, down and no-action classes. The introduction of a no-action state in our novel approach alleviates the burden of denoising the dataset as a preprocessing task. The performance of our labeling algorithm is experimented with using machine learning and deep learning models. The framework is augmented by applying the Bayesian optimization technique for the selection of the best tuning values of the hyperparameters. The price trend prediction module generates the required trading signals. The results show that the average annualized Sharpe ratio as the trading performance metric is about 2.823, indicating the framework produces excellent cumulative returns.

show abstract