scholarly journals How to Identify Varying Lead–Lag Effects in Time Series Data: Implementation, Validation, and Application of the Generalized Causality Algorithm

Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 95 ◽  
Author(s):  
Johannes Stübinger ◽  
Katharina Adler
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Structural Breaks ◽  
Time Series Data ◽  
Consumer Price Index ◽  
Real Data ◽  
Linear Mapping ◽  
Series Data ◽  
Lag Effects ◽  
Silver Metal

This paper develops the generalized causality algorithm and applies it to a multitude of data from the fields of economics and finance. Specifically, our parameter-free algorithm efficiently determines the optimal non-linear mapping and identifies varying lead–lag effects between two given time series. This procedure allows an elastic adjustment of the time axis to find similar but phase-shifted sequences—structural breaks in their relationship are also captured. A large-scale simulation study validates the outperformance in the vast majority of parameter constellations in terms of efficiency, robustness, and feasibility. Finally, the presented methodology is applied to real data from the areas of macroeconomics, finance, and metal. Highest similarity show the pairs of gross domestic product and consumer price index (macroeconomics), S&P 500 index and Deutscher Aktienindex (finance), as well as gold and silver (metal). In addition, the algorithm takes full use of its flexibility and identifies both various structural breaks and regime patterns over time, which are (partly) well documented in the literature.

Random order autoregressive time series model with structural break

2020 ◽  
Vol 15 (3) ◽  
pp. 225-237
Author(s):  
Saurabh Kumar ◽  
Jitendra Kumar ◽  
Vikas Kumar Sharma ◽  
Varun Agiwal
Keyword(s):  
Time Series ◽  
Structural Breaks ◽  
Time Series Data ◽  
Structural Break ◽  
Random Order ◽  
Real Data ◽  
Series Data ◽  
Model Parameters ◽  
Multiple Time ◽  
Multiple Time Points

This paper deals with the problem of modelling time series data with structural breaks occur at multiple time points that may result in varying order of the model at every structural break. A flexible and generalized class of Autoregressive (AR) models with multiple structural breaks is proposed for modelling in such situations. Estimation of model parameters are discussed in both classical and Bayesian frameworks. Since the joint posterior of the parameters is not analytically tractable, we employ a Markov Chain Monte Carlo method, Gibbs sampling to simulate posterior sample. To verify the order change, a hypotheses test is constructed using posterior probability and compared with that of without breaks. The methodologies proposed here are illustrated by means of simulation study and a real data analysis.

CAUSAL RELATIONSHIP OF ENERGY CONSUMPTION AND ECONOMIC GROWTH – A CASE STUDY OF PAKISTAN

2021 ◽  
Vol 9 (1) ◽  
pp. 139-164
Author(s):  
Saddam Hussain ◽  
Chunjiao Yu
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Energy Consumption ◽  
Causal Relationship ◽  
Inflation Rate ◽  
Time Series Data ◽  
Consumer Price Index ◽  
Series Data ◽  
Relationship Of

This paper explores the causal relationship between energy consumption and economic growth in Pakistan, applying techniques of co-integration and Hsiao’s version of Granger causality, using time series data over the period 1965-2019. Time series data of macroeconomic determi-nants – i.e. energy growth, Foreign Direct Investment (FDI) growth and population growth shows a positive correlation with economic growth while there is no correlation founded be-tween economic growth and inflation rate or Consumer Price Index (CPI). The general conclu-sion of empirical results is that economic growth causes energy consumption.

scholarly journals Evidence Graphs: Supporting Transparent and FAIR Computation, with Defeasible Reasoning on Data, Methods and Results

2021 ◽  
Author(s):  
Sadnan Al Manir ◽  
Justin Niestroy ◽  
Maxwell Adam Levinson ◽  
Timothy Clark
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Time Series Data ◽  
Predictive Analytics ◽  
Defeasible Reasoning ◽  
Series Data ◽  
Inference Rules ◽  
Deep Networks ◽  
Evidence Graph ◽  
Over Time

Introduction: Transparency of computation is a requirement for assessing the validity of computed results and research claims based upon them; and it is essential for access to, assessment, and reuse of computational components. These components may be subject to methodological or other challenges over time. While reference to archived software and/or data is increasingly common in publications, a single machine-interpretable, integrative representation of how results were derived, that supports defeasible reasoning, has been absent. Methods: We developed the Evidence Graph Ontology, EVI, in OWL 2, with a set of inference rules, to provide deep representations of supporting and challenging evidence for computations, services, software, data, and results, across arbitrarily deep networks of computations, in connected or fully distinct processes. EVI integrates FAIR practices on data and software, with important concepts from provenance models, and argumentation theory. It extends PROV for additional expressiveness, with support for defeasible reasoning. EVI treats any com- putational result or component of evidence as a defeasible assertion, supported by a DAG of the computations, software, data, and agents that produced it. Results: We have successfully deployed EVI for very-large-scale predictive analytics on clinical time-series data. Every result may reference its own evidence graph as metadata, which can be extended when subsequent computations are executed. Discussion: Evidence graphs support transparency and defeasible reasoning on results. They are first-class computational objects, and reference the datasets and software from which they are derived. They support fully transparent computation, with challenge and support propagation. The EVI approach may be extended to include instruments, animal models, and critical experimental reagents.

scholarly journals Differentially Private Autocorrelation Time-Series Data Publishing Based on Sliding Window

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jing Zhao ◽  
Shubo Liu ◽  
Xingxing Xiong ◽  
Zhaohui Cai
Keyword(s):  
Time Series ◽  
Privacy Protection ◽  
Large Scale ◽  
Differential Privacy ◽  
Time Series Data ◽  
Sliding Window ◽  
Data Publishing ◽  
Series Data ◽  
Data Publication ◽  
Autocorrelation Time

Privacy protection is one of the major obstacles for data sharing. Time-series data have the characteristics of autocorrelation, continuity, and large scale. Current research on time-series data publication mainly ignores the correlation of time-series data and the lack of privacy protection. In this paper, we study the problem of correlated time-series data publication and propose a sliding window-based autocorrelation time-series data publication algorithm, called SW-ATS. Instead of using global sensitivity in the traditional differential privacy mechanisms, we proposed periodic sensitivity to provide a stronger degree of privacy guarantee. SW-ATS introduces a sliding window mechanism, with the correlation between the noise-adding sequence and the original time-series data guaranteed by sequence indistinguishability, to protect the privacy of the latest data. We prove that SW-ATS satisfies ε-differential privacy. Compared with the state-of-the-art algorithm, SW-ATS is superior in reducing the error rate of MAE which is about 25%, improving the utility of data, and providing stronger privacy protection.

scholarly journals AstroCatR: a mechanism and tool for efficient time series reconstruction of large-scale astronomical catalogues

2020 ◽  
Vol 496 (1) ◽  
pp. 629-637
Author(s):  
Ce Yu ◽  
Kun Li ◽  
Shanjiang Tang ◽  
Chao Sun ◽  
Bin Ma ◽  
...  
Keyword(s):  
Time Series ◽  
High Performance ◽  
Large Scale ◽  
Extrasolar Planets ◽  
Time Series Data ◽  
Series Data ◽  
Data Sets ◽  
Observation Data ◽  
Data Volume ◽  
And Performance

ABSTRACT Time series data of celestial objects are commonly used to study valuable and unexpected objects such as extrasolar planets and supernova in time domain astronomy. Due to the rapid growth of data volume, traditional manual methods are becoming extremely hard and infeasible for continuously analysing accumulated observation data. To meet such demands, we designed and implemented a special tool named AstroCatR that can efficiently and flexibly reconstruct time series data from large-scale astronomical catalogues. AstroCatR can load original catalogue data from Flexible Image Transport System (FITS) files or data bases, match each item to determine which object it belongs to, and finally produce time series data sets. To support the high-performance parallel processing of large-scale data sets, AstroCatR uses the extract-transform-load (ETL) pre-processing module to create sky zone files and balance the workload. The matching module uses the overlapped indexing method and an in-memory reference table to improve accuracy and performance. The output of AstroCatR can be stored in CSV files or be transformed other into formats as needed. Simultaneously, the module-based software architecture ensures the flexibility and scalability of AstroCatR. We evaluated AstroCatR with actual observation data from The three Antarctic Survey Telescopes (AST3). The experiments demonstrate that AstroCatR can efficiently and flexibly reconstruct all time series data by setting relevant parameters and configuration files. Furthermore, the tool is approximately 3× faster than methods using relational data base management systems at matching massive catalogues.

scholarly journals Exploring large scale time-series data using nested timelines

2013 ◽  
Author(s):  
Zaixian Xie ◽  
Matthew O. Ward ◽  
Elke A. Rundensteiner
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Time Series Data ◽  
Series Data

scholarly journals Evaluation of a new visualization and analytics solution for slow control data for large scale experiments

2020 ◽  
Vol 245 ◽  
pp. 07001
Author(s):  
Laura Sargsyan ◽  
Filipe Martins
Keyword(s):  
Time Series ◽  
Control System ◽  
Distribution System ◽  
Large Scale ◽  
High Energy Physics ◽  
Time Series Data ◽  
High Energy ◽  
Series Data ◽  
Test Version ◽  
Long Time

Large experiments in high energy physics require efficient and scalable monitoring solutions to digest data of the detector control system. Plotting multiple graphs in the slow control system and extracting historical data for long time periods are resource intensive tasks. The proposed solution leverages the new virtualization, data analytics and visualization technologies such as InfluxDB time-series database for faster access large scale data, Grafana to visualize time-series data and an OpenShift container platform to automate build, deployment, and management of application. The monitoring service runs separately from the control system thus reduces a workload on the control system computing resources. As an example, a test version of the new monitoring was applied to the ATLAS Tile Calorimeter using the CERN Cloud Process as a Service platform. Many dashboards in Grafana have been created to monitor and analyse behaviour of the High Voltage distribution system. They visualize not only values measured by the control system, but also run information and analytics data (difference, deviation, etc.). The new monitoring with a feature-rich visualization, filtering possibilities and analytics tools allows to extend detector control and monitoring capabilities and can help experts working on large scale experiments.

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