The boosting technique using correlation coefficient to improve time series forecasting accuracy

2007 ◽  
Author(s):  
Luzia Vidal de Souza ◽  
Aurora T. R. Pozo ◽  
Joel M. C. da Rosa ◽  
Anselmo Chaves Neto
Keyword(s):  
Time Series ◽  
Correlation Coefficient ◽  
Time Series Forecasting ◽  
Forecasting Accuracy ◽  
Boosting Technique

Enhanced Forecasting Accuracy of Fuzzy Time Series Model Based on Combined Fuzzy C-Mean Clustering with Particle Swam Optimization

2020 ◽  
Vol 19 (02) ◽  
pp. 2050017
Author(s):  
Nghiem Van Tinh
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Pso Algorithm ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Forecasting Accuracy ◽  
Forecasting Models ◽  
Proposed Model ◽  
Fuzzy C Means Clustering ◽  
Universe Of Discourse

Over the past 25 years, numerous fuzzy time series forecasting models have been proposed to deal the complex and uncertain problems. The main factors that affect the forecasting results of these models are partition universe of discourse, creation of fuzzy relationship groups and defuzzification of forecasting output values. So, this study presents a hybrid fuzzy time series forecasting model combined particle swarm optimization (PSO) and fuzzy C-means clustering (FCM) for solving issues above. The FCM clustering is used to divide the historical data into initial intervals with unequal size. After generating interval, the historical data is fuzzified into fuzzy sets with the aim to serve for establishing fuzzy relationship groups according to chronological order. Then the information obtained from the fuzzy relationship groups can be used to calculate forecasted value based on a new defuzzification technique. In addition, in order to enhance forecasting accuracy, the PSO algorithm is used for finding optimum interval lengths in the universe of discourse. The proposed model is applied to forecast three well-known numerical datasets (enrolments data of the University of Alabama, the Taiwan futures exchange —TAIFEX data and yearly deaths in car road accidents in Belgium). These datasets are also examined by using some other forecasting models available in the literature. The forecasting results obtained from the proposed model are compared to those produced by the other models. It is observed that the proposed model achieves higher forecasting accuracy than its counterparts for both first—order and high—order fuzzy logical relationship.

scholarly journals Combining Qualitative and Time Series Forecasting to Increase the Forecasting Accuracy for Instant Noodle Sales in Thailand

2016 ◽  
Vol 3 (3) ◽  
pp. 1
Author(s):  
Teerada Khamphinit ◽  
Pornthipa Ongkunaruk
Keyword(s):  
Time Series ◽  
Demand Forecasting ◽  
Forecast Accuracy ◽  
Time Series Forecasting ◽  
Forecasting Accuracy ◽  
Sales Data ◽  
Average Percentage ◽  
Sales Forecast ◽  
Instant Noodle ◽  
Instant Noodles

<p>Demand forecasting is very important for the planning process. The forecast accuracy affects the efficiency of the procurement, production and delivery processes. Our research has the objective of increasing the sales forecasting accuracy of instant noodles for a case study company in Thailand. Many factors affect the sales of instant noodles, such as promotion, other commodities’ prices, national disaster and production capacity. Thus, we collected historical monthly sales data, analysed the data and their pattern and considered whether the data were irregular due to those factors. After obtaining the forecast data, data intervention by adjustment of the irregular effects was performed in accordance with our experience and judgement. Next, we used the predictor function in the Crystal Ball software to determine the best time series forecasting method for actual and adjusted sales data. Then, we verified the result with the actual sales data for one year. The result showed that the adjustment could increase the sales forecast accuracy by 46.14%, 22.53% and 56.42% for products A, B and C, respectively. In summary, the mean average percentage sales forecast error after adjustment was 6.48%–11.62%, which is better than the current method of forecasting based on experts.  </p><p><strong>Keywords</strong>: Instant Noodle; Intervention; Qualitative Forecasting; Sales Adjustment; Time Ser ies Forecasting </p>

scholarly journals Two Birds with One Stone: Series Saliency for Accurate and Interpretable Multivariate Time Series Forecasting

2021 ◽  
Author(s):  
Qingyi Pan ◽  
Wenbo Hu ◽  
Ning Chen
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Data Augmentation ◽  
Multivariate Time Series ◽  
Time Series Forecasting ◽  
Experimental Results ◽  
Learning Methods ◽  
Forecasting Accuracy ◽  
Sliding Windows ◽  
Scheme Of Series

It is important yet challenging to perform accurate and interpretable time series forecasting. Though deep learning methods can boost forecasting accuracy, they often sacrifice interpretability. In this paper, we present a new scheme of series saliency to boost both accuracy and interpretability. By extracting series images from sliding windows of the time series, we design series saliency as a mixup strategy with a learnable mask between the series images and their perturbed versions. Series saliency is model agnostic and performs as an adaptive data augmentation method for training deep models. Moreover, by slightly changing the objective, we optimize series saliency to find a mask for interpretable forecasting in both feature and time dimensions. Experimental results on several real datasets demonstrate that series saliency is effective to produce accurate time-series forecasting results as well as generate temporal interpretations.

scholarly journals Short-term Time Series Forecasting of Concrete Sewer Pipe Surface Temperature

2020 ◽  
Author(s):  
Karthick Thiyagarajan ◽  
sarath kodagoda ◽  
Nalika Ulapane
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Time Series Forecasting ◽  
Sensor Data ◽  
Alternative Measure ◽  
Sewer Pipe ◽  
Short Term ◽  
Pipe Surface ◽  
Forecasting Accuracy ◽  
Short Term Forecasting

Microbial corrosion is considered the main reason for multi-billion dollar sewer asset degradation. Sewer pipe surface temperature is a vital parameter for predicting the micro-biologically induced concrete corrosion. Due to this important measure, a surface temperature sensor suite was recently developed and tested in an aggressive sewer environment. The sensors can fail and they may also put offline during the period of scheduled maintenance. In such situations, time series forecasting of sensor data can be an alternative measure for the operators managing the sewer network. In this regard, this paper focuses on the short-term forecasting of sensor measurements. The evaluation was carried out by forecasting the sensor measurements for different time periods and evaluated with different forecasting models. The ETS model leads to high short-term forecasting accuracy and the ARIMA model leads to high long-term forecasting accuracy. The models were evaluated on real data captured in a Sydney sewer

scholarly journals Short-term Time Series Forecasting of Concrete Sewer Pipe Surface Temperature

2020 ◽  
Author(s):  
Karthick Thiyagarajan ◽  
sarath kodagoda ◽  
Nalika Ulapane
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Time Series Forecasting ◽  
Sensor Data ◽  
Alternative Measure ◽  
Sewer Pipe ◽  
Short Term ◽  
Pipe Surface ◽  
Forecasting Accuracy ◽  
Short Term Forecasting

Microbial corrosion is considered the main reason for multi-billion dollar sewer asset degradation. Sewer pipe surface temperature is a vital parameter for predicting the micro-biologically induced concrete corrosion. Due to this important measure, a surface temperature sensor suite was recently developed and tested in an aggressive sewer environment. The sensors can fail and they may also put offline during the period of scheduled maintenance. In such situations, time series forecasting of sensor data can be an alternative measure for the operators managing the sewer network. In this regard, this paper focuses on the short-term forecasting of sensor measurements. The evaluation was carried out by forecasting the sensor measurements for different time periods and evaluated with different forecasting models. The ETS model leads to high short-term forecasting accuracy and the ARIMA model leads to high long-term forecasting accuracy. The models were evaluated on real data captured in a Sydney sewer

scholarly journals A New Period-Sequential Index Forecasting Algorithm for Time Series Data

2019 ◽  
Vol 9 (20) ◽  
pp. 4386 ◽  
Author(s):  
Hongyan Jiang ◽  
Dianjun Fang ◽  
Klaus Spicher ◽  
Feng Cheng ◽  
Boxing Li
Keyword(s):  
Time Series ◽  
Correlation Coefficient ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
New Period ◽  
Psi Method

A period-sequential index algorithm with sigma-pi neural network technology, which is called the (SPNN-PSI) method, is proposed for the prediction of time series datasets. Using the SPNN-PSI method, the cumulative electricity output (CEO) dataset, Volkswagen sales (VS) dataset, and electric motors exports (EME) dataset are tested. The results show that, in contrast to the moving average (MA), exponential smoothing (ES), and autoregressive integrated moving average (ARIMA) methods, the proposed SPNN-PSI method shows satisfactory forecasting quality due to lower error, and is more suitable for the prediction of time series datasets. It is also concluded that: There is a trend that the higher the correlation coefficient value of the reference historical datasets, the higher the prediction quality of SPNN-PSI method, and a higher value (>0.4) of correlation coefficient for SPNN-PSI method can help to improve occurrence probability of higher forecasting accuracy, and produce more accurate forecasts for the big datasets.

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