Static Time Series Research Articles

Evaluation of soft-computing techniques for pan evaporation estimation

Estimation of pan evaporation (Epan) can be useful in judicious irrigation scheduling for enhancing agricultural water productivity. The aim of present study was to assess the efficacy of state-of-the-art LSTM and ANN for daily Epan estimation using meteorological data. Besides this, the effect of static time-series (Julian date) as additional input variable was investigated on performance of soft-computing techniques. For this purpose,the models were trained, tested and validated with eight meteorological variables of 37 years by using preceding 1-, 3- and 5- days’ information. Data were partitioned into three groups as training (60%), testing (20%), and validation (20%) components. It was observed that the models performed well (best) with preceding 5-days meteorological information followed by 3-days and 1-day. However, all LSTMs simulated peak value of Epan was more accurate as compared to lower values. Meteorological data with julian date improved the performance of LSTMs (0.75<NSE 1; PBias< 10; KGE 0.75). The ANN trained using only meteorological data (preceding 5-days information) had better performance error statistics among all other ANNs and LSTMs with minimum MAE (0.68 to 0.86), RMSE (0.93 to 1.22), PBias (-0.73 to 2.44) and maximum NSE (0.83 to 0.84) and KGE (0.89 to 0.92). Overall, it was inferred that the forecasting of meteorological parameters using a few days preceding information along with Julian date as the time series variables resulted in better estimation of Epan for the study region.

FUCOM-optimization based predictive maintenance strategy using expert elicitation and Artificial Neural Network

Necessity of predictive maintenance appears when production assets such as machines need either a repair or a regular check. When it comes to monitoring digital twins of objects, maintenance predictions in time play a significant role. This study targets an artificial intelligence-based prediction of time-series data integrated with fully consistent expert elicitation. It also allows the integration of expert knowledge into the construction and explanation of the predictor. Moreover, it is considered a hybrid approach using data and expert knowledge for time series prediction. Reliability of obtained prediction is examined through comparisons with basic dynamic and static forecasts. Decision-making analysis based on expert’s opinions and optimization science coincides to prepare the data set for ANN prediction. Thus, the maintenance process will become predictable over time. Moreover, this prediction avoids sudden overhaul cost. Therefore, the entire production system is kept controlled. In this research, a FUll COnsistently Method (FUCOM) of optimization together with Multi Criteria Decision Making (MCDM) is used for obtaining optimal weights. Then, dynamic and static time-series forecasts are compared with ANN prediction. The obtained results prove ANN reliability according to expert’s opinions and FUCOM optimization model. FUCOM-ANN prediction achieves nearly 10 times smaller RMSE (0.08) than the RMSE (0.83) of general ANN prediction on the test data set. Moreover, the 10-fold cross-validation strategy shows better RMSE (0.13) for FUCOM-ANN validation sets than the RMSE (0.78) of general ANN validation sets.

Development evaluation of China’s regional innovation during the deep integration process of industry-university research institute cooperation network-a perspective of change speed

ABSTRACT The dynamics of China’s regional innovation in the process of deep integration between industry, university, and research institutes is in a constant state of flux, exhibiting various characteristics such as fluctuations and shifts. This study aims to assess the development status of China’s regional industry-university research institute cooperation network from the perspective of change speed, utilising catastrophe series theory and methods such as butterfly catastrophe, dovetail catastrophe, and cusp catastrophe. Through a static time series panel evaluation, the study captures the static change speed value and trend value of the deep integration of China’s regional industry-university research institute cooperation network. Drawing on Newton’s second law of motion in physics as a reference, a comprehensive evaluation value is derived, reflecting the actual development status of China’s regional industry-university research institute cooperation network from the perspective of change speed.

Domain knowledge decomposition of building energy consumption and a hybrid data-driven model for 24-h ahead predictions

The task of building energy prediction (BEP) is essential to several emerging research domains, including energy management, control optimization and fault detection. An accuracy prediction model contributes significantly to the improvement of building energy efficiency and flexibility. However, most existing prediction models are either based on single time series model or static model, making them perform poorly for long-term predictions. To solve this problem, this paper proposes a hybrid prediction model, which combines the deep ensemble (DE) model and autoregressive (AR) model together. Its motivation comes from the domain knowledge analysis of building energy consumption, where the energy consumption is decomposed into a global part and a local part. The deep ensemble model is adopted to predict its global part, and the AR model is employed to predict its local part. Comprehensive data experiments are conducted based on 50 real buildings across five building types to validate the model performance. The prediction horizon is the 24-h ahead sliding window prediction for one-year energy consumption. The results indicate that the hybrid prediction model outperforms the LSTM, DE-only, and ARIMA-only model, where its relative improvements of CV-RMSE are 28.7%, 35.98% and 18.47%, respectively. The experimental results also reveal that the types of buildings, i.e., office, lodging, will affect the model performance, which is attributed to their different user-behaviors. Based on the experimental results, it is demonstrated that the integration of static model and time series model is robust to the varying of building types and prediction steps, which should be the preferred choice of BEP task.

Naturalization of an alien ancient fruit tree at a fine scale: Community structure and population dynamics of Cydonia oblonga in China.

Naturalized plants play pivotal roles in local plant biodiversity and ecological functions; however, the drivers of naturalization remain poorly understood at a fine scale. Thus, understanding the processes of the development and dominance of alien plants in local natural habitats is of paramount importance. In the present study, we report for the first time the naturalization of Cydonia oblonga in China based on community structure and population dynamics at a fine scale. We conducted a comprehensive survey of the species through field community investigations, interviews, and a literature review. Cydonia oblonga is an ancient fruit tree with a long introduction history of over 4500 years worldwide and a cultivation history of over 2500 years in China. We analyzed C.oblonga community structure using the spatiotemporal substitution method and quantitatively analyzed population dynamics using a static life table, survivorship curve, and time series model to explore the naturalization processes. The following results were obtained. (i) The community comprised 31 coexisting vascular plant species (16 woody and 15 herbaceous species) belonging to 28 genera in 20 families. Rosaceae and Asteraceae were the two most dominant families. (ii) All individuals in the shrub layer as well as the C.oblonga population exhibited a roughly inverted J-shaped basal diameter distribution. A complete age structure was noted, and the survival curve was classified as Deevey type II. According to time series analysis, the population is estimated to increase in the future, specifically of medium and large individuals. (iii) Religious exchange, potent resource competitiveness, and similarity with the native habitat may be the major drivers of the introduction and successful naturalization of C.oblonga. These results suggest that alien species closely related to native ones are more likely to invade, naturalize, and dominate communities in local habitats.

Structure and dynamics of co-dominant species in different succession stages of natural forests in Daxing'an Mountains, China

Affected by the disturbance of forest fire and logging, the primary forest in Daxing'an Mountains gradually degenerates into secondary forest. In this study, we established 16 plots in each of three typical forests, including natural Betula platyphylla pure forest (pioneer stage), natural B. platyphylla and Larix gmelinii mixed forest (transition stage) and natural L. gmelinii pure forest (top stage). The methods of population age and tree height structure, static life table, survival analysis, dynamic index and time series prediction were used to quantitatively analyze the dynamics of dominant species (B. platyphylla and L. gmelinii) and all the arbors, aiming to provide scientific basis for the restoration and development of natural L. gmelinii forest. The results showed that the abundance of young co-dominant species and total arbors in each stage was large, and that all population had strong self-renewal potential. With the progress of succession, the abundance of B. platyphylla in each age class gradually decreased, whereas that of L. gmelinii gradually increased. The mortality and disappearance rates of total arbors and B. platyphylla in the transition stage and L. gmelinii in the pioneer stage gradually increased with the increases of age class, and the survival curve was Deevey-Ⅰ type. The survival analysis results showed that the population was stable in the early stage, increased in the middle stage, and declined in the later stage. In other stages, the mortality rates fluctuated slightly, the survival curves were Deevey-Ⅱ type, and the population increased in the early stage, declined in the middle stage, and stable in the later stage. The co-dominant species and total arbors were growing in the three succession stages, among which B. platyphylla in the pioneer stage, L. gmelinii and total arbors in the top stage showed the lowest sensitivity to the environment. The results of time series prediction showed that the co-dominant species and total arbors in each stage would increase in the future. During forest succession, it was necessary to strengthen the protection of seedlings and young trees, thin the forest with large coverage, and take appropriate measures to ensure population renewal.

Measure and analyze the impact of some macroeconomic variables on Iran's foreign trade for the period (1990-2019)

Foreign trade she one of the most important economic sectors in all countries of the world through its positive contribution to increasing economic growth rates and national income. Achieving structural transformations in many economies of different countries by providing consumer and production goods. As well as considering it as a measure by which one can know the degree of trade openness towards the outside world of any country in the world. The research relied on studying (public spending, gross domestic product, foreign direct investment and exchange rates) and analyzing them in order to estimate that relationship.Research amid to describe the effect of some macroeconomic variables on Iran's foreign trade in the long and short term through the use of modern economic measurement methods and estimation of that relationship during the research period (1990-2019). And after studying the static time series of the data of the variables used by using the co-integration method and relying on the time-delayed gaps regression model ARDL. The research concluded that there is a short-term relationship between the dependent variable and the independent variables, and the existence of a long-term relationship. Through results that appeared show that the sign is negative between foreign trade and variable public spending but not significance at the level of (5%). With the presence of a positive relationship between the domestic product and foreign trade and it is significant at the level of significance (5%). And that any increase in the domestic product the total by (1%) leads to an increase of (0.774%) in foreign trade. With the presence of a positive relationship between foreign trade and foreign direct investment, and that any increase in foreign direct investment by (1%) leads to an increase of (0.128%) in foreign trade. As well as the existence of the inverse relationship between exchange rates and foreign trade, which is not significant at the level of significance (5%). Recommended search paying attention to foreign trade, diversifying production and export sources, encouraging a policy of protection for national production through imposing customs taxes on imported goods, supporting local production sectors, adopting an exchange rate policy that helps increase foreign trade, the need to reform the banking system, including the industrial, agricultural and service sectors.

Economic measurement of the impact of some macroeconomic variables on the foreign trade of the Kingdom of Saudi Arabia for the period (1990-2019)

Foreign trade is one of the basic components of economic relations between the countries of the world, as it brings them together in one economic and international system, as it leads to the transfer of many economies of the countries of the world from one case to another. The global economy has worked to change it, and trade is affected by several internal and external factors, including macroeconomic variables. The research aims to: Identify some macro variables and their relationship to Saudi Arabia’s foreign trade for the long and short term using modern analytical and standard methods and their estimation for the period (1990-2019). The research reached its measurement aspect after studying the static time series and using the method of the co-integration methodology through the ARDL model. The research proved the existence of a short-term relationship between the independent variables and the dependent variable, and the results of the long-term relationship between foreign trade and macroeconomic variables in the Kingdom of Saudi Arabia showed that the signal is positive between public spending and foreign trade, but it is not significant at the level of morale (5%). And that the relationship is direct between the gross domestic product and foreign trade, and that an increase in the gross domestic product by (1%). Leads to an increase in foreign trade by (1.22%). As well as the existence of a direct relationship between foreign direct investment and foreign trade, and it was found that the increase in foreign direct investment by (1%). Leads to an increase in foreign trade by (0.047%). The research recommended paying attention to foreign trade and its tools that are needed by development, especially in oil-producing countries, as Saudi Arabia is an oil country, by taking advantage of oil revenues in the formation of its export structure. And focusing on increasing interest in studies who adopted the relationship between macroeconomic variables and foreign trade in order to know the impact of these variables and their positive and negative effects on trade. Since there is a long-term relationship between the foreign trade variable and the macro variables under discussion, it is necessary to focus on public spending and emphasize investment spending by taking advantage of the gross domestic product it has a direct relationship with foreign trade.

Intelligent Monitoring and Forecasting Using Machine Learning Techniques

Stock market prediction is a crucial task and a prominent research area in the financial domain as investing in the stock market involves greater risk. Various events may affect public sentiments and emotions differently, which may affect the trend of stock market prices. Because of dependency on numerous factors, the stock prices are dynamic, and not static, highly noisy and nonlinear time series data. Due to its great learning ability, machine learning has been applied to this research area. The enormous stock market volatility emphasizes the need to effectively assess the role of external factors such as news in stock prediction. Machine learning algorithms have made a great impact in predicting stocks accurately. Methods based on learning for stock price prediction are found to be popular and a lot of strategies have been used to improve the performance of the learning-based predictors. However, performing successful predictions in the stock market is still a challenge. In this paper, we use machine learning algorithms and predict the stock market trade to examine if it increases or decreases. Compared with existing learning-based methods, the effectiveness of this new enhanced learning-based method is demonstrated by using a Naïve Bayes classifier which is found to be consistent and gives the maximum efficiency.

Efficient segmentation-based methods for anomaly detection in static and streaming time series under dynamic time warping

The problem of time series anomaly detection has attracted a lot of attention due to its usefulness in various application domains. However, most of the methods proposed so far used Euclidean distance to deal with this problem. Dynamic Time Warping (DTW) distance is more suitable than Euclidean distance because of its capability in shape-based similarity checking in many practical fields, for example those with multimedia data. In this paper, we propose two efficient anomaly detection methods, EP-Leader-DTW and SEP-Leader-DTW, for static and streaming time series under DTW, respectively. Our methods are based on time series segmentation, subsequence clustering, and anomaly scoring. For segmentation, the major extrema method is used to obtain subsequences. For clustering, we apply Leader algorithm to cluster the subsequences along with a lower bounding technique to accelerate DTW distance computation. Experimental results on several benchmark time series datasets reveal that our method for anomaly detection in static time series under DTW can perform very fast and accurately on large time series datasets. For streaming time series, our method can meet the instantaneous requirement with high accuracy. As a result, our anomaly detection methods are applicable to both static and streaming time series in practice.

Population structure and quantitative characteristics of Tetracentron sinense (Trochodendraceae) in Leigong Mountain Nature Reserve, China

Background: Knowledge of plant population structure facilitates conservation, management, and utilization of endangered plants.
 Research question: What is the current status of the natural populations of Tetracentron sinense in Leigong Mountain Nature Reserve (LMNR) and what future population development trends can be predicted?
 Studied species: Tetracentron sinense 
 Study site and period of research: T. sinense populations in LMNR in China in 2018.
 Methods: The population structure and quantity dynamics of four typical patches were studied using static life tables, survival curves, survival analyses, and time series analyses. 
 Results: The age structures of the T. sinense populations were spindle-shaped, with few seedlings and saplings, and Deevey type II and ? survival curves. The mortality rate ( q x ) of each patch increased quickly, and then plateaued, finally increasing again. Survival rate ( S x ) showed a contrary trend to q x . Trends in cumulative mortality rate ( F (i) ), killing power ( K x ), mortality density ( f (ti) ), and hazard rate ( ? (ti) ) with increasing age class were similar: increasing at a younger age, gradually stabilizing in middle age, and then increasing slightly in older age. The number of individuals in these T. sinense populations was predicted to decrease sharply in future, with younger individuals being seriously deficient. The results showed that the natural populations of T. sinense in the LMNR were relatively stable but were in an early stage of decline. 
 Conclusions: The lack of younger individuals might reflect a bottleneck for regeneration of T. sinense populations , leading to a decline in population size.

L-Match: A Lightweight and Effective Subsequence Matching Approach

Many IoT (Internet of Things) applications, like the industrial internet and the smart city, collect data continuously from massive sensors. It is crucial to exploit and analyze the time series data efficiently. Subsequence matching is a fundamental task in mining time series data. Most existing works develop the index and the matching approach for the static time series data. However, IoT applications need to continuous collect new data and deposit huge historical time series data, which pose a significant challenge for the static indexing approach. To address this challenge, we propose a lightweight index structure, L-index, and a matching approach, L-match, for the constraint normalized subsequence matching problem (cNSM). L-index is a two-layer structure and built on the simple series synopsis, the mean values of the disjoint windows. It is easy to build and update as data grows. Moreover, to further improve the efficiency for the variable query lengths, an optimization technique, named SD-pruning, is proposed. We conduct extensive experiments, and the results verify the effectiveness and efficiency of the proposed approach.

Geological and hydrological visualization models for Digital Earth representation

This paper presents techniques and interactive models for multi-dimensional analyses and geospatial visualization in virtual globes based on three application examples: (1) earthquakes around the world, (2) groundwater well levels in Texas, and (3) geothermal subsurface heat indexes in Texas.While studies are known that represent multi-dimensional geospatial data points, we develop and suggest multi-dimensional models for virtual globes using KML and KMZ (compressed KML files) with a complete and static time series data set. The benefit of this approach for the user is the ability to view and analyze time-based correlations interactively over the entire time span in one instance, which is not possible with animated (dynamic) models.The methods embedded in our models include: (a) depth layered cueing within subsurface Earth visualization for a better orientation when maneuvering below the ground, (b) a technique with Ternary Visual Shape Logic (TVSL) as a quick indicator of change over time, and (c) different visual representations of multiple dimensions for the addressed case study examples. The models can be applied to a variety of problems in different disciplines, especially to support decision-making processes.

Similarity search for numerous patterns over multiple time series streams under dynamic time warping which supports data normalization

A huge challenge in nowadays' data mining is similarity search in streaming time series under Dynamic Time Warping (DTW). In the similarity search, data normalization is a must to obtain accurate results. However, data normalization on the fly and the DTW calculation cost a great deal of computational time and memory space. In the paper, we present two methods, SUCR-DTW and ESUCR-DTW, which conduct similarity search for numerous prespecified patterns over multiple time-series streams under DTW supporting data normalization. These two methods utilize a combination of techniques to mitigate the aforementioned costs. The efficient methods inherit the cascading lower bounds introduced in UCR-DTW, a state-of-the-art method of similarity search in the static time series, to admissibly prune off unpromising subsequences. To be adaptive in the streaming setting, SUCR-DTW performs incremental updates on the envelopes of new-coming time-series subsequences and incremental data normalization on time-series data. However, like UCR-DTW, SUCR-DTW retrieves only similar subsequences that have the same length as the patterns. ESUCR-DTW, an extension of SUCR-DTW, can find similar subsequences whose lengths are different from those of the patterns. Furthermore, our proposed methods exploit multi-threading to have a fast response to high-speed time-series streams. The experimental results show that SUCR-DTW obtains the same precision as UCR-DTW and has lower wall clock time. Besides, the experimental results of SUCR-DTW and ESUCR-DTW reveal that the extended method has higher accuracy in spite of longer wall clock time. Also, the paper evaluates the influence of incremental z-score normalization and incremental min---max normalization on the obtained results.

Evaluating an animated and static time series map of District Six: A visual and cognitive approach

Visualization of spatial information is an important aspect in the representation of map displays. Maps today are visually adapted to a variety of mediums in displaying spatial information temporally and as time series phenomena. GIS technology has incorporated tools for analysing these spatio-temporal trends. However map users are overwhelmed by the amount of information in these map displays and therefore experience cognitive overload. In this study we find that static and animated maps have their respective advantages in the visualization of the map reader by placing participants through a structured set of questions. All these facets exist in the visual and cognitive realm of the map reader. District Six is a unique area that has experienced significant spatial change in the last century, mainly attributed to its political history. This has been depicted in a conventional static and animated time series map representation which has been designed to facilitate the understanding of the spatial change that occurred during this unique period of history. In this paper a methodology has been investigated and implemented in the design of the map, by enhancing the map reader’s experience in visualising time series spatial data. We conclude further that visual intention and attention are cognitive facets that collectively strengthen the map reader’s ability in learning spatial information.

Prototype selection based on multi-objective optimisation and partition strategy

Prototype selection aims at reducing the storage of datasets and execution time, and improving prediction accuracy and operation efficiency by removing noisy or redundant samples, so the prediction classification accuracy is maximised and the reduction ratio is minimised simultaneously. To achieve this purpose, a two objective optimisation model is set up for prototype selection problem in the paper. To make the model be solved easier, it is transformed to a single objective optimisation model by the division of the two objectives, and a new two–layer genetic algorithm is proposed by using a divide–and–conquer partition strategy. The divide–and–conquer partition can divide the whole dataset into some random sub–datasets to be handled, respectively. The simulations are conducted and the proposed algorithm is compared with several existing algorithms. The results obtained on UCI static datasets and time series datasets indicate that the proposed algorithm is an expedient method in design nearest neighbour classifiers.

A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments

Cloud computing environments allow customers to dynamically scale their applications. The key problem is how to lease the right amount of resources, on a pay-as-you-go basis. Application re-dimensioning can be implemented effortlessly, adapting the resources assigned to the application to the incoming user demand. However, the identification of the right amount of resources to lease in order to meet the required Service Level Agreement, while keeping the overall cost low, is not an easy task. Many techniques have been proposed for automating application scaling. We propose a classification of these techniques into five main categories: static threshold-based rules, control theory, reinforcement learning, queuing theory and time series analysis. Then we use this classification to carry out a literature review of proposals for auto-scaling in the cloud.

Efficient Similarity Join over Multiple Stream Time Series

Similarity join (SJ) in time-series databases has a wide spectrum of applications such as data cleaning and mining. Specifically, an SJ query retrieves all pairs of (sub)sequences from two time-series databases that \varepsilon-match with each other, where \varepsilon is the matching threshold. Previous work on this problem usually considers static time-series databases, where queries are performed either on disk-based multidimensional indexes built on static data or by nested loop join (NLJ) without indexes. SJ over multiple stream time series, which continuously outputs pairs of similar subsequences from stream time series, strongly requires low memory consumption, low processing cost, and query procedures that are themselves adaptive to time-varying stream data. These requirements invalidate the existing approaches in static databases. In this paper, we propose an efficient and effective approach to perform SJ among multiple stream time series incrementally. In particular, we present a novel method, Adaptive Radius-based Search (ARES), which can answer the similarity search without false dismissals and is seamlessly integrated into SJ processing. Most importantly, we provide a formal cost model for ARES, based on which ARES can be adaptive to data characteristics, achieving the minimum number of refined candidate pairs, and thus, suitable for stream processing. Furthermore, in light of the cost model, we utilize space-efficient synopses that are constructed for stream time series to further reduce the candidate set. Extensive experiments demonstrate the efficiency and effectiveness of our proposed approach.

Warping the time on data streams

Continuously monitoring through time the correlation/distance of multiple data streams is of interest in a variety of applications, including financial analysis, video surveillance, and mining of biological data. However, distance measures commonly adopted for comparing time series, such as Euclidean and Dynamic Time Warping ( DTW), either are known to be inaccurate or are too time-consuming to be applied in a streaming environment. In this paper we propose a novel DTW-like distance measure, called Stream-DTW ( SDTW), which unlike DTW can be efficiently updated at each time step. We formally and experimentally demonstrate that SDTW speeds up the monitoring process by a factor that grows linearly with the size of the window sliding over the streams. For instance, with a sliding window of 512 samples, SDTW is about 600 times faster than DTW. We also show that SDTW is a tight approximation of DTW, errors never exceeding 10%, and that it consistently outperforms approximations developed for the case of static time series.

Online summarization of dynamic time series data

Managing large-scale time series databases has attracted significant attention in the database community recently. Related fundamental problems such as dimensionality reduction, transformation, pattern mining, and similarity search have been studied extensively. Although the time series data are dynamic by nature, as in data streams, current solutions to these fundamental problems have been mostly for the static time series databases. In this paper, we first propose a framework to online summary generation for large-scale and dynamic time series data, such as data streams. Then, we propose online transform-based summarization techniques over data streams that can be updated in constant time and space. We present both the exact and approximate versions of the proposed techniques and provide error bounds for the approximate case. One of our main contributions in this paper is the extensive performance analysis. Our experiments carefully evaluate the quality of the online summaries for point, range, and k–nn queries using real-life dynamic data sets of substantial size.