A Comparative Study of Seasonal-ARIMA and RNN (LSTM) on Time Series Temperature Data Forecasting

Pervasive Computing and Social Networking - Lecture Notes in Networks and Systems ◽

10.1007/978-981-16-5640-8_25 ◽

2022 ◽

pp. 315-326

Author(s):

Sumanta Banerjee ◽

Shyamapada Mukherjee

Keyword(s):

Time Series ◽

Comparative Study ◽

Temperature Data

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Methods for Processing and Summarizing Time-Series Temperature Data Collected as Part of the National Water-Quality Assessment Program Studies on the Effects of Urbanization on Stream Ecosystems

Data Series ◽

10.3133/ds330 ◽

2008 ◽

Author(s):

Thomas F. Cuffney ◽

Robin A. Brightbill

Keyword(s):

Water Quality ◽

Time Series ◽

Quality Assessment ◽

Water Quality Assessment ◽

Temperature Data ◽

Stream Ecosystems ◽

Assessment Program ◽

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Comparative Study on Performance of Time Series Fitting and Prediction of SF6 Decomposition Gas Under Over-heated Condition by GM(1,1) and OBGM(1,N) Grey System

2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE) ◽

10.1109/ichve49031.2020.9279818 ◽

2020 ◽

Author(s):

Zhang Shiling

Keyword(s):

Time Series ◽

Comparative Study ◽

Grey System ◽

Sf6 Decomposition ◽

Heated Condition

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Business Days Time Series Weekly Trend and Seasonality

Engineering Proceedings ◽

10.3390/engproc2021005026 ◽

2021 ◽

Vol 5 (1) ◽

pp. 26

Author(s):

Karlis Gutans

Keyword(s):

Time Series ◽

Global Warming ◽

Exchange Rates ◽

Temperature Data ◽

Adjustment Method ◽

Currency Exchange ◽

Currency Exchange Rates ◽

Time Series Decomposition ◽

The world changes at incredible speed. Global warming and enormous money printing are two examples, which do not affect every one of us equally. “Where and when to spend the vacation?”; “In what currency to store the money?” are just a few questions that might get asked more frequently. Knowledge gained from freely available temperature data and currency exchange rates can provide better advice. Classical time series decomposition discovers trend and seasonality patterns in data. I propose to visualize trend and seasonality data in one chart. Furthermore, I developed a calendar adjustment method to obtain weekly trend and seasonality data and display them in the chart.

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Sensitivity of six typical spatiotemporal fusion methods to different influential factors: A comparative study for a normalized difference vegetation index time series reconstruction

Remote Sensing of Environment ◽

10.1016/j.rse.2020.112130 ◽

2021 ◽

Vol 252 ◽

pp. 112130

Author(s):

Junxiong Zhou ◽

Jin Chen ◽

Xuehong Chen ◽

Xiaolin Zhu ◽

Yuean Qiu ◽

...

Keyword(s):

Time Series ◽

Comparative Study ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Influential Factors ◽

Fusion Methods ◽

Index Time Series

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A comparative study of methods to reconstruct a periodic time series from an environmental proxy record

Earth-Science Reviews ◽

10.1016/j.earscirev.2009.04.002 ◽

2009 ◽

Vol 95 (3-4) ◽

pp. 97-118 ◽

Author(s):

Anouk de Brauwere ◽

Fjo De Ridder ◽

Rik Pintelon ◽

Johan Schoukens ◽

Frank Dehairs

Keyword(s):

Time Series ◽

Comparative Study ◽

Proxy Record ◽

Periodic Time Series ◽

Environmental Proxy

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A comparative study of inverse methods for estimating climatic history from borehole temperature data

Global and Planetary Change ◽

10.1016/0921-8181(92)90030-e ◽

1992 ◽

Vol 6 (2-4) ◽

pp. 113-127 ◽

Author(s):

P.Y. Shen ◽

K. Wang ◽

H. Beltrami ◽

J.-C. Mareschal

Keyword(s):

Comparative Study ◽

Temperature Data ◽

Inverse Methods ◽

Climatic History ◽

Borehole Temperature

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Empirical Mode Decomposition, Extreme Learning Machine and Long Short-Term Memory for Time Series Prediction: A Comparative Study

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) ◽

10.1109/bracis.2018.00091 ◽

2018 ◽

Author(s):

Elivelto Ebermam ◽

Gabriel G. De Angelo ◽

Helder Knidel ◽

Renato A. Krohling

Keyword(s):

Time Series ◽

Comparative Study ◽

Extreme Learning Machine ◽

Short Term Memory ◽

Time Series Prediction ◽

Term Memory ◽

Mode Decomposition ◽

Long Short Term Memory ◽

Learning Machine

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A Comparative Study of End-To-End Discriminative Deep Learning Models for Knee Joint Kinematic Time Series Classification

2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) ◽

10.1109/spmb47826.2019.9037831 ◽

2019 ◽

Author(s):

M. Abid ◽

A. Mitiche ◽

Y. Ouakrim ◽

P. A. Vendittoli ◽

A. Fuentes ◽

...

Keyword(s):

Time Series ◽

Deep Learning ◽

Comparative Study ◽

Time Series Classification ◽

Learning Models ◽

Joint Kinematic ◽

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COMPARATIVE STUDY OF TIME SERIES AND MULTIPLE REGRESSION FOR MODELING DEPENDENCE OF CATTLE BODY TEMPERATURE ON ENVIRONMENTAL VARIABLES DURING HEAT STRESS

Conference on Applied Statistics in Agriculture ◽

10.4148/2475-7772.1078 ◽

2009 ◽

Author(s):

M. Pathak ◽

A. M. Parkhurst ◽

R. A. Arias ◽

T. L. Mader

Keyword(s):

Time Series ◽

Heat Stress ◽

Body Temperature ◽

Comparative Study ◽

Multiple Regression ◽

Environmental Variables

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Comparative Study on Performance Analysis of Time Series Predictive Models

International Journal of Grid and Distributed Computing ◽

10.14257/ijgdc.2017.10.8.04 ◽

2017 ◽

Vol 10 (8) ◽

pp. 37-48 ◽

Author(s):

Syed Muzamil Basha ◽

Yang Zhenning ◽

Dharmendra Singh Rajput ◽

Ronnie D. Caytiles ◽

N. Ch. S.N Iyengar

Keyword(s):

Time Series ◽

Performance Analysis ◽

Comparative Study ◽

Predictive Models ◽

Analysis Of Time Series

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