Fuzzy Long Term Forecasting through Machine Learning and Symbolic Representations of Time Series

2006 ◽  
pp. 109-123
Author(s):  
Bernard Hugueney ◽  
Bernadette Bouchon-Meunier ◽  
Georges Hébrail
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Symbolic Representations ◽  
Long Term Forecasting

Long term forecasting using machine learning methods

2018 ◽  
Author(s):  
Hossein Sangrody ◽  
Ning Zhou ◽  
Salih Tutun ◽  
Benyamin Khorramdel ◽  
Mahdi Motalleb ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Long Term Forecasting

Short, medium and long term forecasting of time series using the L-Co-R algorithm

2014 ◽  
Vol 128 ◽  
pp. 433-446 ◽  
Author(s):  
E. Parras-Gutierrez ◽  
V.M. Rivas ◽  
M. Garcia-Arenas ◽  
M.J. del Jesus
Keyword(s):  
Time Series ◽  
Long Term Forecasting

scholarly journals Comparison of the long-term forecasting method of RSSI by machine learning

2020 ◽  
Vol 9 (11) ◽  
pp. 553-558
Author(s):  
Tatsuya Nagao ◽  
Takahiro Hayashi ◽  
Yoshiaki Amano
Keyword(s):  
Machine Learning ◽  
Forecasting Method ◽  
Long Term Forecasting

An integrated deterministic–stochastic approach for forecasting the long-term trajectories of COVID-19

2021 ◽  
pp. 2141001
Author(s):  
Indrajit Ghosh ◽  
Tanujit Chakraborty
Keyword(s):  
Time Series ◽  
Stochastic Approach ◽  
World Population ◽  
Data Sets ◽  
Univariate Time Series ◽  
Reproduction Numbers ◽  
The World ◽  
Long Term Forecasting ◽  
Ar Models

The ongoing coronavirus disease 2019 (COVID-19) pandemic is one of the major health emergencies in decades that affected almost every country in the world. As of June 30, 2020, it has caused an outbreak with more than 10 million confirmed infections, and more than 500,000 reported deaths globally. Due to the unavailability of an effective treatment (or vaccine) and insufficient evidence regarding the transmission mechanism of the epidemic, the world population is currently in a vulnerable position. The daily cases data sets of COVID-19 for profoundly affected countries represent a stochastic process comprised of deterministic and stochastic components. This study proposes an integrated deterministic–stochastic approach to forecast the long-term trajectories of the COVID-19 cases for Italy and Spain. The deterministic component of the daily-cases univariate time series is assessed by an extended version of the SIR [Susceptible–Infected–Recovered–Protected–Isolated (SIRCX)] model, whereas its stochastic component is modeled using an autoregressive (AR) time series model. The proposed integrated SIRCX-AR (ISA) approach based on two operationally distinct modeling paradigms utilizes the superiority of both the deterministic SIRCX and stochastic AR models to find the long-term trajectories of the epidemic curves. Experimental analysis based on the proposed ISA model shows significant improvement in the long-term forecasting of COVID-19 cases for Italy and Spain in comparison to the ODE-based SIRCX model. The estimated Basic reproduction numbers for Italy and Spain based on SIRCX model are found to be [Formula: see text] and [Formula: see text], respectively. ISA model-based results reveal that the number of cases in Italy and Spain between 11 May, 2020–9 June, 2020 will be 10,982 (6383–15,582) and 13,731 (3395–29,013), respectively. Additionally, the expected number of daily cases on 9 July, 2020 for Italy and Spain is estimated to be 30 (0–183) and 92 (0–602), respectively.

scholarly journals Precipitation isotope time series predictions from machine learning applied in Europe

2021 ◽  
Vol 118 (26) ◽  
pp. e2024107118
Author(s):  
Daniel B. Nelson ◽  
David Basler ◽  
Ansgar Kahmen
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Climate Data ◽  
Modeling Framework ◽  
History Of ◽  
Oxygen Isotopic ◽  
Input Variables ◽  
Hydrogen And Oxygen Isotope ◽  
High Level

Hydrogen and oxygen isotope values of precipitation are critically important quantities for applications in Earth, environmental, and biological sciences. However, direct measurements are not available at every location and time, and existing precipitation isotope models are often not sufficiently accurate for examining features such as long-term trends or interannual variability. This can limit applications that seek to use these values to identify the source history of water or to understand the hydrological or meteorological processes that determine these values. We developed a framework using machine learning to calculate isotope time series at monthly resolution using available climate and location data in order to improve precipitation isotope model predictions. Predictions from this model are currently available for any location in Europe for the past 70 y (1950–2019), which is the period for which all climate data used as predictor variables are available. This approach facilitates simple, user-friendly predictions of precipitation isotope time series that can be generated on demand and are accurate enough to be used for exploration of interannual and long-term variability in both hydrogen and oxygen isotopic systems. These predictions provide important isotope input variables for ecological and hydrological applications, as well as powerful targets for paleoclimate proxy calibration, and they can serve as resources for probing historic patterns in the isotopic composition of precipitation with a high level of meteorological accuracy. Predictions from our modeling framework, Piso.AI, are available at https://isotope.bot.unibas.ch/PisoAI/.

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