Sentence examples for obtain a forecast from inspiring English sources

The model used HMM to identify data patterns and then used fuzzy logic to generate appropriate fuzzy rules and obtain a forecast value for next day stock price.

The simplest forecast method is to select a possible tsunami scenario from a precomputed database based on the estimated seismic magnitude with its hypocenter location (Tatehata 1997; Kamigaichi 2009; Lauterjung et al. 2010; Abe and Imamura 2010; Igarashi et al. 2015), requiring only minutes to obtain a forecast result after the earthquake occurrence.

In the next section, the occurrence time of this event of undefined magnitude (B10) is used as a data point to obtain a forecast of occurrence time; however, this event is not used as a data point when forecasting the magnitude.

Possible drawbacks of the presented approach are the lack of guarantee of obtaining a forecast (because in some cases the number of past similar examples may be insufficient), or the computational costs due to pattern tracing when scanning large databases.

According to this model, to obtain a winter forecast, four preceding forecasts were required, taking into account the difference of order ν = 1.

According to these models, to obtain a summer forecast, three preceding forecasts were required, taking into account the difference of order ν = 1.

In particular, daily temperature forecasts collected from five daily initialization runs were averaged out to obtain a weekly forecast for each model grid point related to the three Italian macro-areas; afterwards, each temperature forecast of the model grid point was once again averaged in order to obtain a single temperature forecast value for each week (12 in total for each area).

The forecast prices are shown in a dashed line in Fig. 3. Similarly, historical furnace oil prices are compared to past oil prices and the difference between the two minimized to obtain a price forecast.

It extracts and identifies the mean trend and the fluctuation component from an original time series, expecting to obtain a superior forecast result with a synthesis of these two components.

Two of them (models G and H) rely on geodynamo simulation based on a model of core dynamics and assimilation of magnetic field models in SH in order to obtain a SV forecast for the upcoming 5 years.

The latter team for the first time in the history of IGRF used assimilation of a retrospective field models including (CHAOS-2s) into a geodynamo simulation based on an approximation of core dynamics in order to obtain a SV forecast for the upcoming 5 years.