A prediction device includes: a store visit number prediction unit that acquires data regarding the number of past visits to a store, inputs the data to a trained store visit number prediction model, and predicts the number of visits to a store on a prediction target day by using an output from the store visit number prediction model; a rate prediction unit that acquires data regarding the number of past visits to a store, a past sales volume until a designated time of the prediction target day, and a sales time feature of the prediction target day, and predicts a sales rate of each product; and a sales volume prediction unit that predicts a sales volume of each product on the prediction target day by using the number of visits to a store predicted by the store visit number prediction unit and the sales rate of each product predicted by the rate prediction unit.

An example computer-implemented method for temporal data analysis and forecasting utilizes topological hierarchical decompositions to process historical and future time windows. The method receives sales data and purchase data for at least one item and generates multiple sets of historical time subsets with varying lengths, where information in shorter subsets is duplicated in longer ones. Future time windows are also generated in a similar manner. Future time windows are chronologically after a given initial time. The method creates past and future topological hierarchical decompositions and directed graph adjacency arrays. Customer attention matrices are generated for past and future windows, and matrix multiplications are performed to create self-attention arrays. These arrays are then multiplied together. The method culminates in providing a dashboard for forecasting after an initial time point, enabling comprehensive temporal data analysis and prediction.

Aspects of the present disclosure relate to a demand forecasting system. The demand forecasting system may include components for developing forecasting models, generating demand forecasts, and handling outputs of demand forecasting models. In some embodiments, the demand forecasting system may include a model training system and one or more components that can be used by the model training system to improve model performance.

Aspects of the present disclosure relate to a demand forecasting system. The demand forecasting system may include components for developing forecasting models, generating demand forecasts, and handling outputs of demand forecasting models. In some embodiments, the demand forecasting system may include a model training system and one or more components that can be used by the model training system to improve model performance.

Disclosed is a method of evaluating water saving policies comprising constructing an impact pathway of agricultural water-saving policy, establishing a farm household production decision-making sub-model, and combining agricultural water use estimation sub-model. Water-savings and grain output at the farm household level are aggregated to regional levels by using a scaling-up method, thereby achieving a link-approach evaluation from macro policy to micro farm household decision-making and then to macro policy effects. The system comprises a policy impact pathway construction module, a farm household decision-making simulation module, an agricultural water use estimation module, and a scaling-up module. The method can accurately characterize the impact mechanism of policies on farmer behavior, enhance the comprehensiveness and depth of policy effectiveness evaluation, and dynamically reflect the long-term effects of policies, thereby providing a basis for the formulation and optimization of agricultural water-saving policies.

An inference tool set having instructions to: gather data relating to value characteristics regarding a venture and a market from one or more data sources regarding the venture and the market; generate a plan circumplex, via the assessment tool circuit, visually representing plan strength scores of the plurality of value characteristics, respectively, and calculate a plan index of the plan circumplex; generate an actual circumplex, via the value creating engine circuits, visually representing actual strength scores of the value characteristics and calculate an actual index of the actual circumplex; align the actual circumplex with the plan circumplex, via an alignment assessment tool circuit, to identify value characteristics having large disparity between plan strength scores and actual strength scores; and forecast financial performance of the venture, via a forecast tool circuit, based on the plan strength scores, the actual strength scores, the plan index, and the actual index.

An information processing device includes a storage to store value data representing a crop value of a crop for each planting position, and a controller configured or programmed to execute selection processing of a harvest range based on the value data, in which the selection processing includes processing of calculating a harvestable area in which a work subject is allowed to execute harvesting in a predetermined period, and processing of allocating the harvestable area to the planting position where the crop value is equal to or more than a predetermined threshold.

The system may create marketing campaigns based on inventory data and/or sales data for a product. The system may compare the inventory data for the product or the sales data for the product to inventory threshold data or sales threshold data, respectively. The system may adjust a marketing campaign for the product based on a relationship between at least one of the inventory data and the inventory threshold data, or the sales data and the sales threshold data. The marketing campaigns may be further based on weather data, traffic data, event data, seasonal data, holiday data, calendar data, trend data and/or any other data.