Survey of Time Series Data Generation in IoT

Nowadays, with the rapid growth of the internet of things (IoT), massive amounts of time series data are being generated. Time series data play an important role in scientific and technological research for conducting experiments and studies to obtain solid and convincing results. However, due to privacy restrictions, limited access to time series data is always an obstacle. Moreover, the limited available open source data are often not suitable because of a small quantity and insufficient dimensionality and complexity. Therefore, time series data generation has become an imperative and promising solution. In this paper, we provide an overview of classical and state-of-the-art time series data generation methods in IoT. We classify the time series data generation methods into four major categories: rule-based methods, simulation-model-based methods, traditional machine-learning-based methods, and deep-learning-based methods. For each category, we first illustrate its characteristics and then describe the principles and mechanisms of the methods. Finally, we summarize the challenges and future directions of time series data generation in IoT. The systematic classification and evaluation will be a valuable reference for researchers in the time series data generation field.

[1]  Francisco Javier Castilla Pascual,et al.  Studying the impacts of test condition and nonoptimal positioning of the sensors on the accuracy of the in-situ U-value measurement , 2023, Heliyon.

[2]  Francisco Javier Castilla Pascual,et al.  In situ U-value measurement of building envelopes through continuous low-cost monitoring , 2023, Case Studies in Thermal Engineering.

[3]  Chunrong Feng,et al.  Periodic measures and Wasserstein distance for analysing periodicity of time series datasets , 2023, Commun. Nonlinear Sci. Numer. Simul..

[4]  Hyoungshick Kim,et al.  A Comparative Study of Time Series Anomaly Detection Models for Industrial Control Systems , 2023, Sensors.

[5]  N. Xiong,et al.  Disentangled Dynamic Deviation Transformer Networks for Multivariate Time Series Anomaly Detection , 2023, Sensors.

[6]  Won-Ju Lee,et al.  Anomaly Detection Based on Time Series Data of Hydraulic Accumulator , 2022, Sensors.

[7]  Tao Qin,et al.  Towards Generating Real-World Time Series Data , 2021, 2021 IEEE International Conference on Data Mining (ICDM).

[8]  Xing-yuan Wang,et al.  Recognition of the scale-free interval for calculating the correlation dimension using machine learning from chaotic time series , 2021, Physica A: Statistical Mechanics and its Applications.

[9]  T. Wright,et al.  Improving the Resolving Power of InSAR for Earthquakes Using Time Series: A Case Study in Iran , 2021, Geophysical Research Letters.

[10]  Ebrahim Ghaderpour,et al.  A Survey on Change Detection and Time Series Analysis with Applications , 2021, Applied Sciences.

[11]  Xiaoyong Du,et al.  TS-Benchmark: A Benchmark for Time Series Databases , 2021, 2021 IEEE 37th International Conference on Data Engineering (ICDE).

[12]  Peng Wang,et al.  Apache IoTDB , 2020, Proc. VLDB Endow..

[13]  Furkan Koltuk,et al.  A Novel Method for the Synthetic Generation of Non-I.I.D Workloads for Cloud Data Centers , 2020, 2020 IEEE Symposium on Computers and Communications (ISCC).

[14]  Tianlin Xu,et al.  COT-GAN: Generating Sequential Data via Causal Optimal Transport , 2020, NeurIPS.

[15]  Muhannad Quwaider,et al.  IoT Privacy and Security: Challenges and Solutions , 2020, Applied Sciences.

[16]  Zhen-Yu Huang,et al.  Air pollution and temperature are associated with increased COVID-19 incidence: A time series study , 2020, International Journal of Infectious Diseases.

[17]  J. Freer,et al.  CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain , 2020, Earth System Science Data.

[18]  D. Mandelli,et al.  Correlated synthetic time series generation for energy system simulations using Fourier and ARMA signal processing , 2020, International Journal of Energy Research.

[19]  Ahmet Murat Ozbayoglu,et al.  Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 , 2019, Appl. Soft Comput..

[20]  G. Fanti,et al.  Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions , 2019, Internet Measurement Conference.

[21]  Matthieu Boussard,et al.  A Fully Automated Periodicity Detection in Time Series , 2019, AALTD@PKDD/ECML.

[22]  Nadhir Al-Ansari,et al.  A Comparison Between Reconstruction Methods for Generation of Synthetic Time Series Applied to Wind Speed Simulation , 2019, IEEE Access.

[23]  Magnus Wiese,et al.  Quant GANs: deep generation of financial time series , 2019, Quantitative Finance.

[24]  Antonio Pepe,et al.  The Parallel SBAS Approach for Sentinel-1 Interferometric Wide Swath Deformation Time-Series Generation: Algorithm Description and Products Quality Assessment , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Elizabeth Ann Maharaj,et al.  Time Series Clustering and Classification , 2019 .

[26]  Feng Li,et al.  GRATIS: GeneRAting TIme Series with diverse and controllable characteristics , 2019, Stat. Anal. Data Min..

[27]  Manfred Mudelsee,et al.  Trend analysis of climate time series: A review of methods , 2019, Earth-Science Reviews.

[28]  Pavlos Protopapas,et al.  T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling , 2018, ArXiv.

[29]  Viktor K. Prasanna,et al.  Generative Adversarial Network for Synthetic Time Series Data Generation in Smart Grids , 2018, 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm).

[30]  Germain Forestier,et al.  Deep learning for time series classification: a review , 2018, Data Mining and Knowledge Discovery.

[31]  Seiichi Uchida,et al.  Biosignal Data Augmentation Based on Generative Adversarial Networks , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[32]  Stephan Mandt,et al.  Disentangled Sequential Autoencoder , 2018, ICML.

[33]  Christoph Bergmeir,et al.  Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach , 2017, Expert Syst. Appl..

[34]  Konstantinos Fokianos,et al.  An Updated Literature Review of Distance Correlation and Its Applications to Time Series , 2017, International Statistical Review.

[35]  Torben Bach Pedersen,et al.  Time Series Management Systems: A Survey , 2017, IEEE Transactions on Knowledge and Data Engineering.

[36]  Houshang Darabi,et al.  LSTM Fully Convolutional Networks for Time Series Classification , 2017, IEEE Access.

[37]  Gunnar Rätsch,et al.  Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs , 2017, ArXiv.

[38]  Mani B. Srivastava,et al.  SenseGen: A deep learning architecture for synthetic sensor data generation , 2017, 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).

[39]  Olof Mogren,et al.  C-RNN-GAN: Continuous recurrent neural networks with adversarial training , 2016, ArXiv.

[40]  Tim Oates,et al.  Time series classification from scratch with deep neural networks: A strong baseline , 2016, 2017 International Joint Conference on Neural Networks (IJCNN).

[41]  Weinan Zhang,et al.  SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient , 2016, AAAI.

[42]  Neil W. Bergmann,et al.  IoT Privacy and Security Challenges for Smart Home Environments , 2016, Inf..

[43]  Carl Doersch,et al.  Tutorial on Variational Autoencoders , 2016, ArXiv.

[44]  Søren Kaae Sønderby,et al.  Sequential Neural Models with Stochastic Layers , 2016, NIPS.

[45]  Keiron O'Shea,et al.  An Introduction to Convolutional Neural Networks , 2015, ArXiv.

[46]  Yoshua Bengio,et al.  A Recurrent Latent Variable Model for Sequential Data , 2015, NIPS.

[47]  Christopher J. Smith,et al.  Stochastic generation of synthetic minutely irradiance time series derived from mean hourly weather observation data , 2015 .

[48]  Lida Xu,et al.  The internet of things: a survey , 2014, Information Systems Frontiers.

[49]  Manish Marwah,et al.  IoTAbench: an Internet of Things Analytics Benchmark , 2015, ICPE.

[50]  Demetris Koutsoyiannis,et al.  A multivariate stochastic model for the generation of synthetic time series at multiple time scales reproducing long-term persistence , 2014, Environ. Model. Softw..

[51]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[52]  Aaron C. Courville,et al.  Generative adversarial networks , 2014, Commun. ACM.

[53]  A. G. Bakirtzis,et al.  Application of time series and artificial neural network models in short-term forecasting of PV power generation , 2013, 2013 48th International Universities' Power Engineering Conference (UPEC).

[54]  Michele Manunta,et al.  Long-term ERS/ENVISAT deformation time-series generation at full spatial resolution via the extended SBAS technique , 2012 .

[55]  Jing Shi,et al.  Evaluation of hybrid forecasting approaches for wind speed and power generation time series , 2012 .

[56]  Simon M. Lucas,et al.  A Survey of Monte Carlo Tree Search Methods , 2012, IEEE Transactions on Computational Intelligence and AI in Games.

[57]  Birgitte Bak-Jensen,et al.  ARIMA-Based Time Series Model of Stochastic Wind Power Generation , 2010, IEEE Transactions on Power Systems.

[58]  Joanne C. White,et al.  Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model. , 2009 .

[59]  Horst Rinne,et al.  The Weibull Distribution: A Handbook , 2008 .

[60]  C. Villani.  Optimal Transport: Old and New , 2008 .

[61]  E. Chuvieco,et al.  Generation of long time series of burn area maps of the boreal forest from NOAA–AVHRR composite data , 2008 .

[62]  Rico Wind,et al.  Simple and realistic data generation , 2006, VLDB.

[63]  Surajit Chaudhuri,et al.  Flexible Database Generators , 2005, VLDB.

[64]  A. Shamshad,et al.  First and second order Markov chain models for synthetic generation of wind speed time series , 2005 .

[65]  Wei Zhang,et al.  EM algorithms of Gaussian mixture model and hidden Markov model , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[66]  V. Smakhtin,et al.  Generation of natural daily flow time-series in regulated rivers using a non-linear spatial interpolation technique , 1999 .

[67]  B. Nelson,et al.  Statistical methodology: V. Time series analysis using autoregressive integrated moving average (ARIMA) models. , 1998, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[68]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[69]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[70]  Lucien Duckstein,et al.  Practical generation of synthetic rainfall event time series in a semi-arid climatic zone , 1988 .

[71]  G. Reinsel,et al.  Prediction of multivariate time series by autoregressive model fitting , 1985 .

[72]  Rangasami L. Kashyap,et al.  Optimal Choice of AR and MA Parts in Autoregressive Moving Average Models , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[73]  J. Durbin.  EFFICIENT ESTIMATION OF PARAMETERS IN MOVING-AVERAGE MODELS , 1959 .

[74]  Tao Niu,et al.  GMM-HMM-Based Medium- and Long-Term Multi-Wind Farm Correlated Power Output Time Series Generation Method , 2021, IEEE Access.

[75]  Zhenguo Zhang,et al.  Few-Shot Learning for Time Series Data Generation Based on Distribution Calibration , 2021, WISA.

[76]  James D. Feyrer.  Trade and Income—Exploiting Time Series in Geography , 2019, American Economic Journal: Applied Economics.

[77]  Mihaela van der Schaar,et al.  Time-series Generative Adversarial Networks , 2019, NeurIPS.

[78]  W. Li,et al.  On a mixture autoregressive model , 2000 .

[79]  L. Rabiner,et al.  An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.

[80]  D. Vere-Jones.  Markov Chains , 1972, Nature.