Weather Forecasting Using Sliding Window Algorithm

Kapoor, Piyush; Bedi, S. S.

doi:10.1155/2013/156540

Cited by 31 publications

(11 citation statements)

References 8 publications

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“…This network had to classify raw data in a sliding time window (Kapoor and Bedi 2013;Saeed and Václav 2014). The following methodology focused on setting the optimal values of model's parameters to offer the best possible prediction.…”

Section: Optimisation Methodologymentioning

confidence: 99%

“…To evaluate how well the model performed at different prediction intervals, data were reorganised by applying a day shift based on a time delay to the expected outputs (Martinerie et al 1998;Kapoor and Bedi 2013). Therefore, in the 0-day prediction interval, the targets correspond to the same day of the pattern, whereas in the 5-d prediction interval, the targets correspond to 5 d later in the pattern.…”

Section: Performance Analysismentioning

confidence: 99%

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Automated early detection of drops in commercial egg production using neural networks

Ramírez-Morales

Fernández-Blanco

Rivero

2017

British Poultry Science

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ABSTRACT1. The purpose of this work was to support decision-making in poultry farms by performing automatic early detection of anomalies in egg production. 2. Unprocessed data were collected from a commercial egg farm on a daily basis over 7 years. Records from a total of 24 flocks, each with approximately 20 000 laying hens, were studied. 3. Other similar works have required a prior feature extraction by a poultry expert, and this method is dependent on time and expert knowledge. 4. The present approach reduces the dependency on time and expert knowledge because of the automatic selection of relevant features and the use of artificial neural networks capable of costsensitive learning. 5. The optimum configuration of features and parameters in the proposed model was evaluated on unseen test data obtained by a repeated cross-validation technique. 6. The accuracy, sensitivity, specificity and positive predictive value are presented and discussed at 5 forecasting intervals. The accuracy of the proposed model was 0.9896 for the day before a problem occurs.

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Section: Optimisation Methodologymentioning

confidence: 99%

Section: Performance Analysismentioning

confidence: 99%

Automated early detection of drops in commercial egg production using neural networks

Ramírez-Morales

Fernández-Blanco

Rivero

2017

British Poultry Science

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“…First, the highest and lowest CPO prices in the year 2012 were determined: D min = 395.20, D max = 628.70. Hence, the universe of discourse U = [394, 629] was divided into certain lengths of interval, where the intervals were determined using a sliding window method which was adopted from [20].…”

Section: Empirical Analysismentioning

confidence: 99%

“…The SWM was introduced by [17], in which it is used for time series analysis, and it is appropriate for many applications [18]. The applications of SWM can be found in various disciplines, for example, medicine [19], weather forecasting [18,20], and database system [21]. In previous studies, limited class interval was used for FTS forecasting.…”

Section: Introductionmentioning

confidence: 99%

Type 2 Fuzzy Inference-Based Time Series Model

et al. 2019

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Fuzzy techniques have been suggested as useful method for forecasting performance. However, its dependency on experts’ knowledge causes difficulties in information extraction and data collection. Therefore, to overcome the difficulties, this research proposed a new type 2 fuzzy time series (T2FTS) forecasting model. The T2FTS model was used to exploit more information in time series forecasting. The concepts of sliding window method (SWM) and fuzzy rule-based systems (FRBS) were incorporated in the utilization of T2FTS to obtain forecasting values. A sliding window method was proposed to find a proper and systematic measurement for predicting the number of class intervals. Furthermore, the weighted subsethood-based algorithm was applied in developing fuzzy IF–THEN rules, where it was later used to perform forecasting. This approach provides inferences based on how people think and make judgments. In this research, the data sets from previous studies of crude palm oil prices were used to further analyze and validate the proposed model. With suitable class intervals and fuzzy rules generated, the forecasting values obtained were more precise and closer to the actual values. The findings of this paper proved that the proposed forecasting method could be used as an alternative for improved forecasting of sustainable crude palm oil prices.

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“…The goal of the SVR model is to search for a function f(x) that has ε deviation from actual y i for all training dataset and at the same time is as flat as possible [12,24,25]. Subsequently, suppose the function is defined as () f x wx b  , where , w X b R  .…”

Section: Support Vector Regression Modelmentioning

confidence: 99%

The strategy of investment in the stock market using modified support vector regression model

2017

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Abstract. Stock indices forecasting has become a popular research issue in recent years.Although many statistical time series models have been applied in stock indices forecasting, they are limited to certain assumptions. Accordingly, the traditional statistical time series models might not be suitable for forecasting real-life stock indices data. Hence, this paper proposes a novel forecasting model to assist investors in determining a strategy for investments in the stock market. The proposed model is called the modified support vector regression model, which is composed of the correlation coefficient method, the sliding window algorithm and the support vector regression model. The results show that the forecasting accuracy of the proposed model is more stable than the existing models in terms of average and standard deviation of the root mean square error (RMSE) and the mean absolute percentage error (MAPE). Accordingly, the proposed model would be used to assist investors in determining a strategy for investing in stocks.

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Weather Forecasting Using Sliding Window Algorithm

Cited by 31 publications

References 8 publications

Automated early detection of drops in commercial egg production using neural networks

Automated early detection of drops in commercial egg production using neural networks

Type 2 Fuzzy Inference-Based Time Series Model

The strategy of investment in the stock market using modified support vector regression model

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