Research on prediction method of api based on the enhanced moving average method

Zhang, Zhenghai; Jiang, Zhifang; Meng, Xing; Cheng, Shenghui; Sun, Wei

doi:10.1109/icsai.2012.6223534

Cited by 7 publications

(5 citation statements)

References 1 publication

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“…In the moving average method [ 26 ], a set of recently sampled data are used to predict the trend of future data. This algorithm is most commonly used in data smoothing methods.…”

Section: Improvement In Algorithm Of Positioning Point Shift and Simu...mentioning

confidence: 99%

An Efficient IAKF Approach for Indoor Positioning Drift Correction

Lin

Chien

Wang

et al. 2022

Sensors

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In this study, an indoor positioning shift correction architecture was developed with an improved adaptive Kalman filter (IAKF) algorithm for the people interference condition. Indoor positioning systems (IPSs) use ultra-wideband (UWB) communication technology. Triangulation positioning algorithms are generally employed for determining the position of a target. However, environmental communication factors and different network topologies produce localization drift errors in IPSs. Therefore, the drift error of real-time positioning points under various environmental factors and the correction of the localization drift error are discussed. For localization drift error, four algorithms were simulated and analyzed: movement average (MA), least square (LS), Kalman filter (KF), and IAKF. Finally, the IAKF algorithm was implemented and verified on the UWB indoor positioning system. The measurement results showed that the drift errors improved by 60% and 74.15% in environments with and without surrounding crowds, respectively. Thus, the coordinates of real-time positioning points are closer to those of actual targets.

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“…In the moving average method [ 26 ], a set of recently sampled data are used to predict the trend of future data. This algorithm is most commonly used in data smoothing methods.…”

Section: Improvement In Algorithm Of Positioning Point Shift and Simu...mentioning

confidence: 99%

An Efficient IAKF Approach for Indoor Positioning Drift Correction

Lin

Chien

Wang

et al. 2022

Sensors

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“…The main purpose of this process is to forecast the stock of procurement products at the Dhanty Store. To maximize forecasting results, the author will apply 2 moving averages, 4-week moving average and 5-week moving average [14], [15]. The results of the implementation of the method will be evaluated and tested to determine the best forecasting results.…”

Section: Modellingmentioning

confidence: 99%

Forecasting With Weighted Moving Average Method for Product Procurement Stock

Pranoto

2022

SISTEK

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Dhanty Store is a family start-up located in East Jakarta. It was initiated in 2018, engaged in retail with the main product in the form of women's clothing and accessories. One of the important processes in Dhanty Store operations is the product procurement process. Currently, Dhanty Store request products according to their wishes without looking at their sales data. This causes their product stock is not well controlled. When there is a lot of demand, sometimes Dhanty Shops run out of stock so their customers will move to other stores. In addition, the process of requesting and procuring products to suppliers also takes a long time so that it can further disrupt the operations of Dhanty Store. This study develops a forecasting application prototype with the Weighted Moving Average method to assist Dhanty Store in the process of requesting and procuring their products. Forecasting results in the period (t) of the 1st week of January were 275 products. In addition, this study predicts product stock with a 4-week moving average and the MAD tracking signal value is ranged from -1.51 to 3.86 and the MAPE value is 35.4%. As for the reliability and level of user acceptance of the prototype model in this study, tested using the System Usability Scale (SUS) method and it is known that the average value given by respondents was 82 with details 0% considered inappropriate, 40% considered neutral and 60% rated it according to need. Keywords: data mining, forecasting, weighted moving average, MAD, MAPE, SUS

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“…Specifically designed for time series data analysis, time series models are employed to forecast events based on historical data. Commonly used time series models for forecasting air pollutants concentration include moving average (MA) [32] and its variant autoregressive integrated moving average (ARIMA) [33][34][35]. PM 2.5 time series data's nature of high complexity, randomness, nonlinearity, and non-stationarity potentially create obstacles for times series models solely achieving satisfactory prediction outcomes [36].…”

Section: Introductionmentioning

confidence: 99%

Prediction of PM2.5 Concentration Using Spatiotemporal Data with Machine Learning Models

Ma,

Chen,

et al. 2023

Atmosphere

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Among the critical global crises curbing world development and sustainability, air quality degradation has been a long-lasting and increasingly urgent one and it has been sufficiently proven to pose severe threats to human health and social welfare. A higher level of model prediction accuracy can play a fundamental role in air quality assessment and enhancing human well-being. In this paper, four types of machine learning models—random forest model, ridge regression model, support vector machine model, extremely randomized trees model—were adopted to predict PM2.5 concentration in ten cities in the Jing-Jin-Ji region of north China based on multi-sources spatiotemporal data including air quality and meteorological data in time series. Data were fed into the model by using the rolling prediction method which is proven to improve prediction accuracy in our experiments. Lastly, the comparative experiments show that at the city level, RF and ExtraTrees models have better predictive results with lower mean absolute error (MAE), root mean square error (RMSE), and higher index of agreement (IA) compared to other selected models. For seasonality, level four models all have the best prediction performances in winter time and the worst in summer time, and RF models have the best prediction performance with the IA ranging from 0.93 to 0.98 with an MAE of 5.91 to 11.68 μg/m3. Consequently, the demonstration of how each model performs differently in each city and each season is expected to shed light on environmental policy implications.

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Research on prediction method of api based on the enhanced moving average method

Cited by 7 publications

References 1 publication

An Efficient IAKF Approach for Indoor Positioning Drift Correction

An Efficient IAKF Approach for Indoor Positioning Drift Correction

Forecasting With Weighted Moving Average Method for Product Procurement Stock

Prediction of PM2.5 Concentration Using Spatiotemporal Data with Machine Learning Models

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