473 results about "Seasonality" patented technology

A hybrid manual/programmable thermostat for a furnace or air conditioner offers the simplicity of a manual thermostat while providing the convenience and versatility of a programmable one. Initially, the hybrid thermostat appears to function as an ordinary manual thermostat; however, it privately observes and learns a user's manual temperature setting habits and eventually programs itself accordingly. If users begin changing their preferred temperature settings due to seasonal changes or other reasons, the thermostat continues learning and will adapt to those changes as well. For ease of use, the thermostat does not require an onscreen menu as a user interface. In some embodiments, the thermostat can effectively program itself for temperature settings that are set to occur at particular times daily or just on weekends, yet the user is not required to enter the time of day or the day of the week.

The present invention provides a multitude of embodiments for landscape water conservation with automated water budgeting or seasonal adjustment. Water budget automation may be implemented within an irrigation controller, by means of an add-on or a plug-in to the controller, or broadcast from a central location. The environmental data used for automated water budgeting may be historical including ambient temperature, wind, solar radiation, relative humidity, soil moisture, soil temperature, or evapotranspiration, or combinations thereof, or with a combination of current sensor data. The automated water budgeting may be accomplished with a percentage accumulation method which adjusts watering intervals and schedules, or on a daily percentage basis. In addition, government based restricted watering schedules may be combined within all the above embodiments to provide additional flexibility for water conservation.

One embodiment practicing aspects of the present invention provides a computer-implemented method for adjusting a reference selling profile for a reference product, comprising retrieving one or more reference selling profiles corresponding to daily or more frequent historical data for one or more reference products, and adjusting the reference selling profiles to correct for one or more promotions which impacted the historical data. Other embodiments and aspects provide for determining location distribution shares, projecting sales, determining distribution quantities, comparing alternative markdown scenarios, etc.

Multi-step statistical modeling in one embodiment of the present disclosure enables anomaly detection, forecasting and/or root cause analysis of the energy consumption for a portfolio of buildings using multi-step statistical modeling. In one aspect, energy consumption data associated with a building, building characteristic data associated with the building, building operation and activities data associated with the building, and weather data are used to generate a variable based degree model. A base load factor, a heating coefficient and a cooling coefficient associated with the building and an error term are determined from the variable based degree model and used to generate a plurality of multivariate regression models. A time series model is generated for the error term to model seasonal factors which reflect monthly dependence on energy use and an auto-regressive integrated moving average model (ARIMA) which reflects temporal dependent patterns of the energy use.

An ET based irrigation system includes a stand alone irrigation controller with a seasonal adjust feature and a stand alone weather station including at least one environmental sensor. The ET based irrigation system further includes a stand alone ET unit operatively connected to the irrigation controller and the weather station. The ET unit includes programming configured to calculate an estimated ET value using a signal from the environmental sensor and to automatically modify a watering schedule of the irrigation controller through the seasonal adjust feature based on the estimated ET value to thereby conserve water while maintaining plant health.

Real-time fraud prevention software-as-a-service (SaaS) products include computer instruction sets to enable a network server to receive medical histories, enrollments, diagnosis, prescription, treatment, follow up, billings, and other data as they occur. The SaaS includes software instruction sets to combine, correlate, categorize, track, normalize, and compare the data sorted by patient, healthcare provider, institution, seasonal, and regional norms. Fraud reveals itself in the ways data points deviate from norms in nonsensical or inexplicable conduct. The individual behaviors of each healthcare provider are independently monitored, characterized, and followed by self-spawning smart agents that can adapt and change their rules as the healthcare providers evolve. Such smart agents will issue flags when their particular surveillance target is acting out of character, outside normal parameters for them. Fraud controls can therefore be much tighter than those that have to accommodate those of a diverse group.

Real-time fraud prevention software-as-a-service (SaaS) products include computer instruction sets to enable a network server to receive medical histories, enrollments, diagnosis, prescription, treatment, follow up, billings, and other data as they occur. The SaaS includes software instruction sets to combine, correlate, categorize, track, normalize, and compare the data sorted by patient, healthcare provider, institution, seasonal, and regional norms. Fraud reveals itself in the ways data points deviate from norms in nonsensical or inexplicable conduct. The individual behaviors of each healthcare provider are independently monitored, characterized, and followed by self-spawning smart agents that can adapt and change their rules as the healthcare providers evolve. Such smart agents will issue flags when their particular surveillance target is acting out of character, outside normal parameters for them. Fraud controls can therefore be much tighter than those that have to accommodate those of a diverse group.

A computer implemented system and methods for forecasting the future values of a time series with multiple seasonal cycles are described. The forecasting method of the present invention include 1) two multi-seasonal exponential smoothing methods, 2) the seasonal sub-series method splitting a time series into multiple sub-series, and 3) the aggregate-to-period forecasting method.

Environmental characteristics of habitable environments (e.g., hotel or motel rooms, spas, resorts, cruise boat cabins, offices, hospitals and/or homes, apartments or residences) are controlled to eliminate, reduce or ameliorate adverse or harmful aspects and introduce, increase or enhance beneficial aspects in order to improve a “wellness” or sense of “wellbeing” provided via the environments. Control of intensity and wavelength distribution of passive and active Illumination addresses various issues, symptoms or syndromes, for instance to maintain a circadian rhythm or cycle, adjust for “jet lag” or season affective disorder, etc. Air quality and attributes are controlled. Scent(s) may be dispersed. Noise is reduced and sounds (e.g., masking, music, natural) may be provided. Environmental and biometric feedback is provided. Experimentation and machine learning are used to improve health outcomes and wellness standards.

Robust forecasting techniques are relatively immune from anomalies or outliers in observed data, such as a stream of data values reflective of the operation or use of a computer system. One robust technique provides a relatively accurate forecast of seasonal behavior even in the presence of an anomaly in corresponding historical data. Another robust forecasting technique provides a relatively accurate forecast even in the presence of an anomaly that spans multiple recent observations. In one embodiment, both techniques are used in combination to automatically detect anomalies in the operation and/or use of a multi-user computer system.

A methodology for automatic a priori data pattern analysis is provided. Described methods allow consistent and objective determination of outliers; trend; seasonality; and level shifts; and the production of better models and more accurate forecasts. In addition, a two-step way to automatically determine seasonality and locate possible events in the data set is described. Decomposition of data into seasonal, trend and level components; detection of outliers and level-shift events in the time series based on statistical analysis of the time series; detection of seasonality based on statistical analysis of clusters of data, known as cluster-based seasonality analysis, or CBSA; evaluation of the goodness of fit of a model to data, using the existing goodness of fit indicator, R²; and seasonality analysis, using a sequence of cluster-based seasonality analysis (CBSA) and Fourier analysis are described.

An online system presents remote users with static incentives, dynamic incentives and the ability to create and submit customizable incentives related to goods or services of interest to the user. By using the incentive delivery system, the user can research goods or services of interest and in an interactive fashion create an incentive package most desirable to the user and submit same to a central location. The systems performs decisioning routines on submitted incentive requests and makes such requests available for review. Ultimately, the submitted requests may be accepted or rejected or a counter-incentive may be issued to the user for consideration. A manufacturer, such as an automobile manufacturer, can offer incentives direct to its consumers and enter into an electronic dialogue with its customers to help drive profits and sales in the most efficient manner. For instance, where certain models of vehicles are in over-supply in a certain geographic region a manufacturer may in real-time tailor its incentive offerings to help account for seasonal or other demand events. Further, the manufacturer can in real-time see the response to its incentives offerings and make appropriate changes to insure the desired result of its marketing efforts is obtained. A graphical user interface enables the remote user to search products or services of interest and to selectively build desired incentives and submit same for acceptance.

A method for in-season macro and micronutrient application based on predicted yield potential, coefficient of variation, and a nutrient response index. The inventive method includes the steps of: determining a nutrient response index for a field; determining the normalized difference vegetation index (NDVI) of an area to fertilize; determining the coefficient of variation of NDVI over a plot; determining a predicted crop yield for the area without additional nutrient; determining an attainable crop yield for the area with additional nutrient; determining the nutrient requirement for the area as the difference between the nutrient removal at the attainable yield minus the nutrient removal at the predicted yield, adjusted by the efficiency of nutrient utilization in the particular crop as indicated by the coefficient of variation.

A business tuning system collects streams of informational reports from remote renewable energy systems. A database holds the information and associates it with a particular renewable energy business operation. A pre-processor semi-structures the information into failure analyses, sales histories, performance results, regional trends, seasonal trends, and demographics. A data mining processor provides for knowledge discovery and prediction from semi-structured information related to said particular renewable energy business operation.

A method and system are provided for presenting rates for travel services using dynamic pricing bands. The dynamic pricing bands represent approximate rates for travel services relative to available rates during or close to the proposed dates of travel, or at or near the proposed travel destination. Each dynamic pricing band is keyed to a particular color, intensity, pattern, sound, or other graphical and/or audio characteristic, thereby providing the consumer a sense of the seasonal, regional, day of week, or other variability of rates for travel services without having to compare actual numbers. The dynamic pricing bands are applied to an interactive presentation of rates for travel services to allow the consumer to explore possible rate variations for particular travel services in a manner that provides a birds-eye view, is intuitive and user-friendly.