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Influenza time series prediction models in a megacity from 2010 to 2019: Based on seasonal autoregressive integrated moving average and deep learning hybrid prediction model. [PDF]
Chin Med J (Engl)Yang J +10 more
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Investigating the Effects of Full-Spectrum LED Lighting on Strawberry Traits Using Correlation Analysis and Time-Series Prediction. [PDF]
Plants (Basel)Lu Y, Gong M, Li J, Ma J.
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A New Auto-Regressive Multi-Variable Modified Auto-Encoder for Multivariate Time-Series Prediction: A Case Study with Application to COVID-19 Pandemics. [PDF]
Int J Environ Res Public Healthde Oliveira EV +2 more
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Coordinated Decision Control of Lane-Change and Car-Following for Intelligent Vehicle Based on Time Series Prediction and Deep Reinforcement Learning. [PDF]
Sensors (Basel)Zhang K, Pu T, Zhang Q, Nie Z.
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2021
Over the past decades, the rapid development of big cities is raising the demands of underground space utilization. One of the favorable options for urban development is to build underground tunnels. Notably, a lot of tunnels are located at a low depth in soil or soft rock zones under densely populated areas, and thus the excavation works of shallow ...
Limao Zhang +3 more
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Over the past decades, the rapid development of big cities is raising the demands of underground space utilization. One of the favorable options for urban development is to build underground tunnels. Notably, a lot of tunnels are located at a low depth in soil or soft rock zones under densely populated areas, and thus the excavation works of shallow ...
Limao Zhang +3 more
openaire +1 more source
1993
A classic statement of the problem of predicting stationary time series x(t) is as follows [6.1, 6.2]. Suppose that a stationary random time series x(t) is defined on time axis t ∈ [∞,+∞]. To simplify the discussion, let us assume that the mean value of the process is zero: $${\rm{E}}\,x\left( t \right) = 0.$$
J. Santana, R. Vilela Mendes
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A classic statement of the problem of predicting stationary time series x(t) is as follows [6.1, 6.2]. Suppose that a stationary random time series x(t) is defined on time axis t ∈ [∞,+∞]. To simplify the discussion, let us assume that the mean value of the process is zero: $${\rm{E}}\,x\left( t \right) = 0.$$
J. Santana, R. Vilela Mendes
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Physics Letters A, 1995
Abstract We introduce a technique to characterize and measure predictability in time series. The technique allows one to formulate precisely a notion of the predictable component of given time series. We illustrate our method for both numerical and experimental time series data.
Liming W. Salvino +3 more
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Abstract We introduce a technique to characterize and measure predictability in time series. The technique allows one to formulate precisely a notion of the predictable component of given time series. We illustrate our method for both numerical and experimental time series data.
Liming W. Salvino +3 more
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Time series — information and prediction
Biological Cybernetics, 1990A time series \(Y_ t\) can be transformed into another time series \(V_ t\) by means of a linear transformation. Should the matrix of that transformation have an inverse, the pair \((Y_ t,V_ t)\) is called invertible. Based on the decomposition procedure for stationary time series it is shown that a sufficient condition for the invertibility of the ...
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