Weather Forecasting Using Memory-Based Reasoning

Mohri, Takao; Nakamura, Masaaki; Tanaka, Hidehiko

doi:10.1016/b978-0-444-81837-9.50019-1

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…In recent years, theoretical physicists have applied statistical mechanics to various problems in information science and technology [10], [11], such as image restoration. Some researchers [12]- [14] have constructed time-series prediction for stock markets based on the statistical mechanics of Ising model.…”

Section: Introductionmentioning

confidence: 99%

Forecast of Meteorological Data Utilizing State-Space Model Utilizing Metric-Multi Dimensional Scaling

.¹

2014

IJRET

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Based on Bayesian statistics using the expected a posterior (EAP) estimation, we forecast time evolution of meteorological data, such as the temperature, at a target point via information on a set of time-series of the temperatures at sampling points selected by the metric multi-dimensional scaling (metric-MDS), without using information on that of the target point. Using numerical calculations with respect to the climate statistics in Kanto district, we clarify that the metric-MDS can select a set of sampling points whose data are similar to that of the target. Then, we clarify that the EAP estimation succeeds in predicting time evolution on the temperature in Maebashi using the set of time-series of the temperatures at the selected sampling points around Maebashi. Also, we find that the EAP estimation predicts the time evolution of the temperature more accurately than the conventional autoregressive model.

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Section: Introductionmentioning

confidence: 99%

Forecast of Meteorological Data Utilizing State-Space Model Utilizing Metric-Multi Dimensional Scaling

.¹

2014

IJRET

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“…Such extensions include a hybrid automaton 1),2) and a timed automaton 3) . Many researchers utilize computational models that can deal with real values to solve various problems including weather forecasting 4) , motion recognition 5), 6) , and time-sequential image analysis 7) . Therefore, we believe that the establishment of the theory of automata that can deal with real values is very important.…”

Section: Introductionmentioning

confidence: 99%

State and Threshold Sequence Minimization Algorithm of Linear Separation Automata

Numai

Udagawa

Kobayashi

2010

IPSJ Online Transactions

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In this paper, we present a minimization algorithm of the number of states of a linear separation automaton (LSA). An LSA is an extended model of a finite automaton. It accepts a sequence of real vectors, and has a weight and a threshold sequence at every state, which determine the transition from the current state to the next at each step. In our previous paper, we characterized an LSA and the minimum state LSA. The minimum state version for a given LSA M is obtained by the algorithm presented in this paper. Its time complexity is O((K + k) n 2 ), where K is the maximum number of threshold values assigned to each weight, k is the maximum number of edges going out from a state of M , and n is the number of states in M . Moreover, we discuss the minimization of a threshold sequence at each state.

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“…Recent advances in information technology and its related fields reveal the importance of computational models which can deal with time series of real valued data. Many researchers utilize computational tools based on these models to solve various problems including weather forecasting 7) , motion recognition 4), 5) , and time-sequential image analysis 13) .…”

Section: Introductionmentioning

confidence: 99%

Theory of Minimizing Linear Separation Automata

Numai

Udagawa

Kobayashi

2010

IPSJ Online Transactions

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In this paper, we theoretically analyze a certain extension of a finite automaton, called a linear separation automaton (LSA). An LSA accepts a sequence of real vectors, and has a weight function and a threshold sequence at every state, which determine the transition from some state to another at each step. Transitions of LSAs are just corresponding to the behavior of perceptrons. We develop the theory of minimizing LSAs by using Myhill-Nerode theorem for LSAs. Its proof is performed as in the proof of the theorem for finite automata. Therefore we find that the extension to an LSA from the original finite automaton is theoretically natural.

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Weather Forecasting Using Memory-Based Reasoning

Cited by 5 publications

References 3 publications

Forecast of Meteorological Data Utilizing State-Space Model Utilizing Metric-Multi Dimensional Scaling

Forecast of Meteorological Data Utilizing State-Space Model Utilizing Metric-Multi Dimensional Scaling

State and Threshold Sequence Minimization Algorithm of Linear Separation Automata

Theory of Minimizing Linear Separation Automata

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