Time Series Missing Value Prediction: Algorithms and Applications

Background: Due to enormous data in web sites, recommending users for every item is impossible. For this problem Recommender Systems (RS) are introduced. RS is categorized into content-based (CB), collaborative Filtering (CF) and Hybrid RS. Based on these techniques recommendations are done to user. In this, CF is the recent technique used in RS in which tagging features also provided. Objective: Three main issues occur in RS are scalability problem which occurs when there is a huge data, sparsity problem occurs when rating data is missing and cols start user or item problem occurs when new user or new item enters in the system. To avoid these issues here we have proposed Tag and Time weight model with GA in Collaborative Tagging. Method: Here we have proposed a method Collaborative Tagging (CT) with Tag and Time weight model with real value genetic algorithm which enhances the recommendation quality by removing the issues of sparsity and cold start user problems with the help of missing value prediction. Here in this the sparsity problem can be removed using missing value prediction and cold start problems are removed using tag and time weight model using GA. Results: Here we have compared the results of Collaborative Filtering with cosine similarity (CF-CS), Collaborative Filtering with Diffusion Similarity (CF-DS), Tag and Time weight model with Diffusion similarity (TAW-TIW-DS) and Tag and Time weight model using Diffusion similarity and Genetic algorithm (TAW-TIW-DS-GA). Conclusion: Here we have compare the proposed approach with the baseline approaches and the metrics are used MAE, prediction percentage, Hit-rate and Hit-rank. Based on these metrics for every split TAW-TIW-DS-GA shown best results as compared to existing approach.

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Recent sea level rise on Ireland's east coast based on multiple tide gauge analysis

10.5194/os-2020-81 ◽

2020 ◽

Author(s):

Amin Shoari Nejad ◽

Andrew C. Parnell ◽

Alice Greene ◽

Brian P. Kelleher ◽

Gerard McCarthy

Keyword(s):

Time Series ◽

Sea Level Rise ◽

Sea Level ◽

Continuous Time ◽

East Coast ◽

Tide Gauge ◽

Tide Gauges ◽

Missing Value ◽

Global Average ◽

Good Agreement

Abstract. We analysed multiple tide gauges from the east coast of Ireland over the period 1938–2018. We validated the different time series against each other and performed a missing value imputation exercise, which enabled us to produce a homogenised record. The recordings of all tide gauges were found to be in good agreement between 2003–2015, though this was markedly less so from 2016 to the present. We estimate the sea level rise in Dublin port for this period at 10 mm yr−1. The rate over the longer period of 1938–2015 was 1.67 mm yr−1 which is in good agreement with the global average. We found that the rate of sea level rise in the longer term record is cyclic with some extreme upward and downward trends. However, starting around 1980, Dublin has seen significantly higher rates that have been always positive since 1996, and this is mirrored in the surrounding gauges. Furthermore, our analysis indicates an increase in sea level variability since 1980. Both decadal rates and continuous time rates are calculated and provided with uncertainties in this paper.

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