Ation in the following elements: ^ ^ ^ ^ oinum , Orule , TS, De f , Desc, (11)^ ^ exactly where TS is usually a set on the time series models. TS could be the dynamic model of numerical attribute num of control object. The model of formed with applying non-time series representation O ^ ^ ^ ^ ^ elements De f , Desc, as Extract(Orule ) ( De f , Desc). ^ ^ Elements of the time series model De f , Desc Ethyl Vanillate References defined by the following expression: y De f = ybase ytendbase , (12)where , would be the weight coefficients. The model makes use of only contextual details and doesn’t use the numerical time series values. The proposed contextual model with the time series with weights: ymodel = w De f y De f (1 – w De f ) y, (13)exactly where w De f is a contextual time series model weight. The result of yet another chosen time series model is y. Forecast values are weighted inside the very same way. The Decs model component validates modeling and forecasting final results: Errvalid =n i=1 isValid(desci ) , n | Desc|(14)exactly where the function isValid(desci ) features a variety [0, 1] and assists to check the constraints. 6. Forming a Context for Time Series Analysis and Forecasting Time series context modeling can use ontology as a understanding base about domain objects. The ontology can contain an object’s relations, restrictions, along with a set of properties.Mathematics 2021, 9,eight ofThe ontology aids to pick the top time series forecast system by way of using logical rules [35]. The set of rules is based on the time series properties, see (ten). Right here, an instance of ontology for context representation, not just for type 2 time series models, is described.M Om Onum TS De f Desc Interval Interval MhasName.StringhasName.StringhasMinValue.IntegerhasMinValue.Integer hasName.String hasTendency.Boolean hasSeason.Boolean hasSmooth.Boolean length.IntervalO De fmhasMaxValue.Integer hasName.String hasPeriod.BooleanhasMaxValue.IntegerhasTendency.Boolean hasPeriod.BooleanhasSeason.Boolean hasFuzzy.BooleanhasSmooth.Boolean hasFuzzy.Boolean hasProperty.Onum hasBase.Integerlength.Interval hasName.String hasName.StringhasName.String hasName.StringhasBase.IntegerOnum DeschasTendBase.IntegerhasTendBase.Integer hasDe f .De f hasBase.Integer hasBound.IntervalhasName.String hasTS.TS hasName.StringhasName.String hasName.StringhasDe f .De fhasBase.IntegerhasTendBase.Integer hasAccept.IntervalhasTendBase.Integer hasAccept.Interval hasDe f .De fhasTendDelta.Integer hasBound.IntervalTShasTendDelta.Integer hasName.StringhasName.StringhasDe f .De f ,where Interval is a concept representing an integer interval; C2 Ceramide In stock hasName is actually a functional function for “has a name” axiom; hasMinValue and hasMaxValue are functional roles for “has a minimal value” and “has a maximal value” axioms; String is usually a string data type; Integer is definitely an integer data variety; M is often a concept representing some process for analyzing or forecasting a time series; hasTendency is really a functional part for “has the ability to function with tendencies” axiom; hasPeriod is a functional role for “has the ability to function with periodicity” axiom; hasSeason is actually a functional part for “has the ability to perform with seasonality” axiom; hasSmooth can be a functional function for “has the capability to use smoothing” axiom; hasFuzzy is a functional function for “has the capability to use fuzzy values” axiom; length can be a functional part for “has an acceptable interval of the time series length” axiom; Boolean is a boolean information variety; Om is really a idea representing some handle object; hasDe f is usually a functional part for “has a time series.
