49 results about "Autoregressive integrated moving average" patented technology

The invention relates to the field of elastic computing of cloud computing, and discloses an agile elastic telescoping method in a cloud environment. The agile elastic telescoping method includes the specific steps: forecasting the load of a next time slice according to historical load data of a data center through an ARIMA (autoregressive integrated moving average) model and an ARMA (autoregressive moving average) model by taking the time slice as a cycle; performing saving operation and restoring operation on a virtual machine, saving the memory state of the virtual machine by the saving operation to hang up the virtual machine, and then restoring the memory state of the virtual machine by the restoring operation to restore use of the virtual machine; hanging up one or a plurality of application-ready virtual machines or rapidly placing the virtual machines into service through the forecasted load of the data center obtained by the load forecasting step and by the aid of the rapid supply step of the virtual machines to dynamically adjust resources of application clusters of the data center. The agile elastic telescoping method has the advantages that the sizes of the clusters are adjusted in real time according to current conditions of the application clusters, and energy consumption of the data center is reduced.

The present invention discloses a sharing bike attraction and generation prediction method based on an ARIMA (Autoregressive Integrated Moving Average Model). The method comprises the following stepsof: 1) collecting GPS positioning data of static parking positions of available bikes in an area, and continuously performing collection for assigned days; 2) obtaining geographic information data oftraffic zones in the area; 3) matching geographical location information of the sharing bikes to each traffic zone; 4) establishing a sharing bike trip total sample; 5) establishing a sharing bike available bike distribution spatial and temporal distribution thermodynamic chart, and a space thermodynamic chart of the number of times of attraction and generation of each zone; 6) establishing a timesequence of the number of travel times of each zone; 7) establishing an ARIMA prediction model after parameters are calibrated; and 8) predicting a sharing bike trip of each traffic zone in a next time aggregation interval. The demand prediction method employs position data of bikes to sense time-space features of the sharing bikes in a city and performs prediction of the time sequence so that adata support is provided for operation, management and scheduling of the sharing bikes.

The invention provides a method for predicting the key industrial electricity consumption based on an industrial condition index. The method comprises the following steps: (1) obtaining the key industrial condition index and historical electricity consumption data; (2) performing seasonal adjustment and a stationary test on the data; (3) judging whether the industrial condition index and the industrial electricity consumption have a causal relationship or not through a Granger causality test and determining an optimal lag period of the condition index; (4) creating a time sequence ARIMA (autoregressive integrated moving average) model of the key industrial electricity consumption, introducing the key industrial condition index into an original ARIMA model, and creating a regressive model; (5) on the basis of an AIC (Akaike information criterion), screening out an optimal model; (6) performing model popularization and application, and predicting the industrial electricity consumption in the future. The key industrial electricity consumption is taken as a study object, the electricity consumption and the influence of the industrial condition index on the electricity consumption are studied by introducing the industrial condition index, the key industrial electricity consumption is accurately predicted in combination with the time sequence model, and a basis is provided for development and planning of electricity industry in the future.

The invention discloses a wastewater treatment process adaptive generalized predictive control method. The method includes the following steps that: the design of an adaptive generalized predictive controller is realized through utilizing an idea of feedback linearization; and when the Lyapunov stability of the adaptive generalized predictive controller is proved, adaptive rules for correcting system controlled autoregressive integrated moving average (CARIMA) model parameters can be obtained, and the model parameters can be dynamically adjusted, so that a system tracking error can be minimum, and therefore, the steady-state control of dissolved oxygen concentration can be realized. With the wastewater treatment process adaptive generalized predictive control method of the invention adopted, the problem of incapability of ordinary generalized predictive control to realize stable control in response to large interference can be solved. As indicated by experimental results, the control algorithm can stably and fast control the dissolved oxygen concentration, has strong anti-interference ability, and is conducive to the stable and efficient operation of a wastewater treatment process.

The invention discloses a method for predicting telephone traffic based on clustering and an autoregressive integrated moving average (ARIMA) model, which belongs to the field of mobile communication and is used for solving the problems of high subjectivity and incorrect partition caused by the way of partitioning traffic cells according to historical experience of experts during prediction of telephone traffic. The method comprises the following steps of: (1) classifying traffic cells into four types, namely main traffic lines, prosperous business districts, institutions of higher education and residential areas according to priori knowledge; (2) performing preprocessing to obtain the clustering characteristics of each traffic cell, wherein the clustering characteristics comprise a relevant coefficient, a variance, a maximum value, an intermediate value, an average value, a minimum value, a value with the highest occurrence frequency and a standard deviation; (3) performing clustering by a K-MEANS clustering algorithm according to the clustering characteristics of each traffic cell so as to form detailed traffic cell types; and (4) predicting the telephone traffic by using the ARIMA model, wherein the same modeling parameter is selected for the same type of detailed telephone traffic cells.

A method and a system of predicting an electric system load based on wavelet noise reduction and empirical mode decomposition-autoregressive integrated moving average (EMD-ARIMA) are provided. The method and the system belong to a field of electric system load prediction. The method includes the following steps. Raw load data of an electric system is obtained first. Next, noise reduction processing is performed on the load data through wavelet analysis. The noise-reduced load data is further processed through an EMD method to obtain different load components. Finally, ARIMA models corresponding to the different load components are built. Further, the ARIMA models are optimized through an Akaike information criterion (AIC) and a Bayesian information criterion (BIC). The load components obtained through predicting the different ARIMA models are reconstructed to obtain a final prediction result, and accuracy of load prediction is therefore effectively improved.

The invention discloses a method for predicting elastic cloud computing resources based on an SARIMA-WNN model. The method comprises the steps that complementary advantages are achieved by using a seasonal autoregressive integrated moving average (SARIMA) model combining with a wavelet neural network (WNN) prediction model to improve the prediction accuracy; according to the SARIMA, seasonal periodic factors are added on the basis of an ARIMA model, periodic cloud resource demand data of a past section is input to a SARIMA (q, d, q)(P, D, Q) s model to obtain d, p, q, D, P and Q respectively;prediction is conducted on tranquilized and sequenced codes by using the SARIMA model, and a prediction result is marked as and an L residual value is marked as rt, wherein the prediction result andthe residual value can be obtained through the prediction; a model which conforms to elastic cloud source prediction is obtained through conducting training on the WNN network by using training samples, prediction is conducted aiming at the residual sequence rt, and the prediction result is marked as ; finally the prediction result of the SARIMA-WNN combined model is obtained. By means of the method, the problems of inaccuracy of a single model, poor effects of other combined models and the like are solved.

A generalized autoregressive integrated moving average (ARIMA) model for use in predictive analytics of time series is based upon creating all possible ARIMA models (by knowing a priori the largest possible values of the p, d and q parameters forming the model), and utilizing the results of at least two different performance measures to ultimately choose the ARIMA(p,d,q) model that is most appropriate for the time series under study. The method of the present invention allows each parameter to range over all possible values, and then evaluates the complete universe of all possible ARIMA models based on these combinations of p, d and q to find the specific p, d and q parameters that yield the “best” (i.e., lowest value) performance measure results. This generalized ARIMA model is particularly useful in predicting future operating hours of power plants and scheduling maintenance events on the gas turbines at these plants.

The invention discloses a quick north-finding method based on an optical fiber gyroscope, which comprises the following steps of: firstly, adjusting a rotary table, and fixing the optical fiber gyroscope; then, collecting and storing the output sequences of the optical fiber gyroscope at all the positions, obtaining a reverse sequence by reverse storage, and modeling all the sequences by an autoregressive integrated moving average model (ARIMA) (2, 1, 1); establishing a Kalman-stable equation and a measurement equation, carrying out Kalman filtering on all the sequences, and confirming the output of the initial time and the finish time of measurement; and finally, calculating the initial azimuth angle, and realizing quick north-finding based on the optical fiber gyroscope. The quick north-finding method overcomes the influence of zero deviation change for the north-finding accuracy in the starting process of the optical fiber gyroscope, so that the optical fiber gyroscope can be used for finding north after being electrified, the north-finding time can be shortened, and the north-finding accuracy is simultaneously guaranteed.

The invention discloses a method for predicting a traffic volume between the entrance and exit of a highway based on highway toll collection data, and the method comprises the steps: carrying out thecollection and processing of networking toll collection data of the highway in a preset time period according to a time granularity threshold value, and obtaining the collected networking toll collection data; converting the collected networking toll collection data into at least one OD traffic demand matrix according to a certain index rule; generating an OD traffic demand time sequence through at least one OD traffic demand matrix, and employing an ARIMA (Autoregressive Integrated Moving Average) model for predicting a OD traffic demand quantity in a future period. The method can help a manager timely obtain the development situation of the traffic demands, achieves the early warning against the sharp increase of traffic demands, achieves the pre-judgment of whether the capacity of the highway is overloaded or not, lays a data foundation for the efficient daily business of the highway, the reasonable formulating of policies and measures, the investment of a construction project, andprovides the decision-making reference data.

The invention discloses a LoRa minimum PingSlot number prediction method and aims at the problem that in a Class B mode of a LoRaWAN standard, battery power is wasted due to the fact that too many PingSlots are opened. According to the method, a plurality of downlink data time sequences of a server are obtained, wherein downlink data volume within 128s is taken as a unit of the downlink data timesequences, and the sequences are enabled to be smooth and steady through d times of difference; and an optimum ARIMA (Autoregressive Integrated Moving Average Model) (p, d, q) model is established according to processed smooth and steady time sequences, and the server predicts the data volume within future 128s according to the established ARIMA (p, d, q) downlink time sequence model, so the suitable PingSlot number and compensation uplink number are accurately selected. Both downlink efficiency and an energy consumption requirement of a terminal are taken into consideration.

The invention discloses a constrained generalized-predictive-control (GPC) optimal control method for a load frequency of a multi-zone interconnection power system. On the basis of a small-signal mechanism analysis modeling method and Z conversion, a controlled autoregressive integrated moving average model of a load frequency of a multi-zone interconnection power system is established; with constrained GPC as a control method, weighted values of a square value of an error between a system predicted output and a reference track, a square value of an output increment of a controller, and a square value of a control output are used as optimal objective functions; and a high-efficiency adaptive evolved optimal solver is designed to realize on-line rolling optimization of constrained GPC and all variables of the system are updated in real time to realize optimal control of the load frequency. With the method disclosed by the invention, the optimal control effect of the load frequency of the multi-zone interconnection power system can be realized. Compared with the prior art, the disclosed method has the improved steady-state performance and dynamic performance and high robustness.

The invention relates to a bridge structure constant load response time domain fusion analysis method, and belongs to the field of bridge structures. The method comprises the following steps: S1, extracting and analyzing bridge monitoring data at the same temperature, and eliminating the influence of temperature effects; S2, reducing the influence of random interference utilizing a time domain averaging technique; S3, extracting a bridge constant load response characteristic quantity utilizing an autoregressive integrated moving average model; and S4, performing data fusion on the acquired bridge structure constant load response information through a fuzzy support vector machine to obtain a final TDFA(Thulium Doped Fiber Amplifier) analysis result. According to the bridge structure constant load response time domain fusion analysis method, on the premise that the influence of temperature factors is eliminated and the influence of random factors is reduced, the characteristic quantity representing the changing condition of a structure constant load response is directly extracted from complex bridge safety operation monitoring signals, the slow evolution process of variables in a whole monitoring period is deeply analyzed, and scientific reference bases are provided to the technicians for management and maintenance of in-service bridges.

The invention provides a time forecasting method and device and relates to the filed of dynamic traffic information service. The time forecasting method and device can improve accuracy of travel time forecasting on the unusual traffic condition. The time forecasting method includes dividing a path from the departing place to the destination into N links; dividing the N links into X link sets according to the relevance of historical travel time of the N links; acquiring link matrixes corresponding to the X link sets respectively, and making the time in the preset time interval before the forecasting time to be the historical time; performing Autoregressive Integrated Moving Average (ARIMA) training on the link matrixes corresponding to X link sets respectively to obtain the travel time of the X link sets at the forecasting time; acquiring the sum of the travel time of the X link sets at the forecasting time to serve as the travel forecasting time of the forecasting time from the departing place to the destination. The time forecasting method and device are used for travel time forecasting.

The invention discloses a power generation fuel supply prediction method, comprising the following steps of implementing stationary processing; implementing model recognition and order determination, i.e. establishing a prediction model according to an autocorrelogram and a partial autocorrelogram to determine model parameters; evaluating the model parameters, i.e. determining the related parameters of the model according to the autocorrelogram, the partial autocorrelogram and stationarity; testing model adaptability, i.e. implementing a residue independence test or a heteroskedasticity test, and modifying the prediction model till a residue sequence is a white noise sequence, and all useful information is extracted. According to the power generation fuel supply prediction method based on a multiplicative seasonal model provided by the invention, the prediction model is a combination of a random seasonal model and an ARIMA (Autoregressive Integrated Moving Average) model; under the premise of considering historical data and influencing factors, the seasonal factors of power generation fuel supply are reflected better, so that the power generation fuel prediction precision is improved.

The invention provides a short-term prediction method and device for transmission line icing and a storage medium. The short-term prediction method comprises the steps of obtaining meteorological information data and wire information data of a transmission line, wherein the meteorological information data comprises the temperature, humidity, precipitation and wind speed, and the wire information data comprises the current icing thickness and wire temperature of the wire; generating icing thickness prediction information of the transmission line by using an ARIMA (Autoregressive Integrated Moving Average) model and a neural network model according to the meteorological information data and the wire information data. According to the embodiment of the invention, whether the icing thickness of the transmission line exceeds the line load or not can be judged, and influences accumulated to an icing prediction result by errors existing in various measured microclimate factors of the existingicing prediction model are effectively reduced, thereby being conducive to arranging the duty, line inspection, ice melting or de-icing work, and improving the efficiency and quality of the de-icingwork.

The invention relates to an optimum weighted composite prediction method for shipment amount of the manufacturing industry. The method comprises the following steps: respectively establishing an autoregressive integrated moving average (ARIMA) model, a vector autoregression (VAR) model and a state space model (SSM) for all the shipment amounts of the manufacturing industry; establishing an optimum weighted composite prediction model based on ARIMA-VAR-SSM; automatically solving related parameters of the optimum weighted composite predication model by utilizing an artificial bee colony algorithm; substituting a test sample into the ARIMA-VAR-SSM-based optimum weighted composite prediction model with parameters determined, so as to obtain a prediction result; and carrying out prediction error evaluation and analysis. The optimum weighted composite prediction method provided by the invention has the advantages that functions of inputting a time series and automatically acquiring the prediction result are realized, physical concept and thinking are clear, calculation is easy, dynamic characteristic of all the shipment amounts of the manufacturing industry can be directly predicted and reflected, prediction precision and accuracy are high, and practicability is strong.

The invention relates to a public bike usage amount prediction method based on various factors. The method comprises the steps of establishing a training data set; carrying out cleaning and statistics on all data according to original data set information; determining a rainfall influencing coefficient; carrying out linear fitting according to rainfall and the bike rent and return number and adjusting the data set; searching nearby stations; calculating the nearest station through utilization of a K nearest neighbor (KNN) algorithm and determining the influencing coefficient of surrounding stations on the station; carrying out preliminary forecasting on setting parameters; inputting the setting parameters into the data set and establishing an autoregressive integrated moving average (ARIMA), thereby obtaining a preliminary forecasting result; adjusting the forecasting result and outputting a final result; and adjusting the result according to the rainfall coefficient and the station influencing coefficient and outputting the result. The method has the beneficial effect that according to a public bike system, the bikes of the stations can be regulated and controlled well through combination of real-time monitoring and prediction methods.

The invention discloses a method for predicating a power load and evaluating a predicated result based on an ARIMA (Autoregressive Integrated Moving Average) model. The method comprises the following steps: obtaining a time sequence of original power load data from a JAVA environment, and calling a statistical function in an R environment to process the time sequence, wherein the statistical function in the R environment is used for processing the time sequence based on the ARIMA model; receiving a processing result returned by the R environment from the JAVA environment and carrying out logic judgment on the result to obtain a corresponding predicated result; and evaluating the predicated result by using a pre-set accuracy evaluation index and a similarity evaluation index in the JAVA environment. With the adoption of the scheme, the functions of inputting the time sequence and automatically obtaining the predicated result are realized; and meanwhile, the predicated result is evaluated so that the accuracy of the predicated result is judged.

The invention provides a packet sending method capable of predicting the arrival time of user business packet accurately so as to effectively resolve the packet collision in a communication channel. The packet sending method is as follows: in order to resolve the collision better in the packet sending process, establishing a mathematical model described by time for an arrival time sequence of the user business packet by user business modeling, facilitating the prediction on the arrival time sequence of the user business packet in future, and when a plurality of packets need to be sent and generate collision, and the packets can be decomposed according to the predicted result. The key points in the invention are as follows: an assignment interval for packet transmission in the current time slot is determined by predicting the arrival time of next user business packet, and decides the packet which is sent in the current time slot and arrives in the time duration of the assignment interval. In the invention, the adopted fractal autoregressive integrated moving average (FARIMA) model can be well matched with the current wireless network environment, and by combination with the whole packet transmission method, the packet time delay can be effectively reduced and the network throughput rate can be improved.

The invention discloses a method for estimating the on-orbit service life of a lithium ion battery for spaceflight, which belongs to the technical field of spacecraft power supplies. The method comprises the following steps of firstly, measuring the median voltage of the lithium ion battery in ground and space environments, calibrating the ground and space performance difference of the lithium ion battery, then proposing a life estimation process based on an autoregressive integral moving average (ARIMA) model, and by adopting the estimation process, taking on-orbit capacitance cycle data of the lithium ion battery as a basis, conducting lithium ion battery cycle life prediction, and finally, according to the on-orbit operation rule of the spacecraft, converting the cycle life of the lithium ion battery obtained through estimation into the on-orbit working life. According to the method, the problem that the result is inaccurate when dynamic multi-step prediction is carried out based on a traditional ARIMA model is solved, and accurate prediction and dynamic evaluation of the on-orbit service life of the aerospace lithium ion battery under the condition that the actual value of a lag dependent variable cannot be obtained are achieved.

The invention discloses a method for predicting and diagnosing a fault of a gearbox of a wind turbine generator, which comprises the following steps of: revealing certain significant relevance among data by establishing an ARIMA (Autoregressive Integrated Moving Average)-based time sequence model for continuously changing state monitoring data, and carrying out continuation on historical data according to the time sequence model. Therefore, the operation condition of a complex mechanical system is obtained by predicting the state trend, an ARIMA model is established for historical sample data of the outlet pressure of the gearbox, the main change trend of the short-term outlet pressure state can be predicted, the prediction precision is satisfactory, and a certain reference value is provided for fault state analysis of the gearbox, due to the fact that prediction deviation of local sampling points can be large due to the change degree of single state monitoring data and the model parameter suitability degree, relevant parameters need to be corrected, and the accuracy and reliability of fault trend prediction need to be further improved together with other continuously-changing index data.