Link prediction in dynamic networks using time-aware network embedding and time series forecasting

Events can be defined as an action or a series of actions that have a determined theme, time, and place. Event analysis tasks for knowledge extraction from news and social media have been explored in recent years. However, there are still few studies that aim to enrich predictive models using event data. In particular, agribusiness events have multiple components to be considered for a successful prediction model. For example, price trend predictions for commodities can be performed through time series analysis of prices, but we can also consider events that represent knowledge about external factors during the training step of predictive models. In this paper, we present a method for integrating events into trend prediction tasks. First, we propose to model events and time-series information through heterogeneous information networks (HIN) that allow multiple components to be directly modeled through multi-type nodes and edges. Second, we learn features from HIN through network embedding methods, i.e., network nodes are mapped to a dense vector of features. In particular, we propose a network embedding method that propagates the semantic of the pre-trained neural language models to a heterogeneous information network and evaluates its performance in a trend link prediction. We show that the use of our proposed model language-based embedding propagation is competitive with state-of-art network embeddings algorithms. Moreover, our proposal performs network embedding incrementally, thereby allowing new events to be inserted in the same semantic space without rebuilding the entire network embedding.

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ARTIFICIAL INTELLIGENT BASED TIME SERIES FORECASTING OF STOCK PRICES USING DIGITAL FILTERS

FUZZY ECONOMIC REVIEW ◽

10.25102/fer.2002.01.02 ◽

2002 ◽

Vol 07 (01) ◽

Cited By ~ 1

Author(s):

A. Sfetsos ◽

C. Siriopoulos

Keyword(s):

Time Series ◽

Stock Prices ◽

Digital Filters ◽

Time Series Forecasting ◽

Artificial Intelligent

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Deep Learning for text in limted data settings

10.36227/techrxiv.12100692 ◽

2020 ◽

Author(s):

Pathikkumar Patel ◽

Bhargav Lad ◽

Jinan Fiaidhi

Keyword(s):

Machine Learning ◽

Time Series ◽

Deep Learning ◽

Sentiment Analysis ◽

Transfer Learning ◽

Text Classification ◽

State Of The Art ◽

Time Series Forecasting ◽

Text Data ◽

Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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Evolving Cascade-Correlation Networks for Time-Series Forecasting.

10.21236/ada289197 ◽

1994 ◽

Author(s):

J. R. McDonnell ◽

D. E. Waagen

Keyword(s):

Time Series ◽

Time Series Forecasting ◽

Correlation Networks ◽

Cascade Correlation

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A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation

Entropy ◽

10.3390/e21050455 ◽

2019 ◽

Vol 21 (5) ◽

pp. 455 ◽

Cited By ~ 5

Author(s):

Hongjun Guan ◽

Zongli Dai ◽

Shuang Guan ◽

Aiwu Zhao

Keyword(s):

Time Series ◽

Information Entropy ◽

Stock Exchange ◽

Time Series Forecasting ◽

High Order ◽

Stock Index ◽

Neutrosophic Set ◽

Forecasting Model ◽

Forecasting Models ◽

Proposed Model

In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data. Then, the upward trend of each of fluctuation data is mapped to the truth-membership of a neutrosophic set, while a falsity-membership is used for the downward trend. Information entropy of high-order fluctuation time series is introduced to describe the inconsistency of historical fluctuations and is mapped to the indeterminacy-membership of the neutrosophic set. Finally, an existing similarity measurement method for the neutrosophic set is introduced to find similar states during the forecasting stage. Then, a weighted arithmetic averaging (WAA) aggregation operator is introduced to obtain the forecasting result according to the corresponding similarity. Compared to existing forecasting models, the neutrosophic forecasting model based on information entropy (NFM-IE) can represent both fluctuation trend and fluctuation consistency information. In order to test its performance, we used the proposed model to forecast some realistic time series, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the Shanghai Stock Exchange Composite Index (SHSECI), and the Hang Seng Index (HSI). The experimental results show that the proposed model can stably predict for different datasets. Simultaneously, comparing the prediction error to other approaches proves that the model has outstanding prediction accuracy and universality.

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