Process Data Mining Based on Rough-set Time Series Analysis

With the continuous development of financial information technology, traditional data mining technology cannot effectively deal with large-scale user data sets, nor is it suitable to actively discover various potential rules from a large number of data and predict future trends. Time series are the specific values of statistical indicators on different time scales. Data sequences arranged in chronological order exist in our lives and scientific research. Financial time series is a special kind of time series, which has the commonness of time series, chaos, non-stationary and non-linear characteristics. Financial time series analysis judges the future trend of change through the analysis of historical time series. Through in-depth analysis of massive financial data, mining its potential valuable information, it can be used for individual or financial institutions in various financial activities, such as investment decision-making, market forecasting, risk management, customer requirement analysis provides scientific evidence.

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Time series analysis of agro-meteorological through algorithms scalable data mining case: Chili river watershed, Arequipa

2015 Latin American Computing Conference (CLEI) ◽

10.1109/clei.2015.7359466 ◽

2015 ◽

Author(s):

Abarca Romero Melisa ◽

Karla Fernandez Fabian ◽

Jose Herrera Quispe

Keyword(s):

Data Mining ◽

Time Series ◽

Time Series Analysis ◽

River Watershed ◽

Series Analysis ◽

Scalable Data Mining

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Time series analysis of refining conditions and estimated pulp properties in a chemi-thermomechanical pulp process

BioResources ◽

10.15376/biores.14.3.5451-5466 ◽

2019 ◽

Vol 14 (3) ◽

pp. 5451-5466

Author(s):

Daniel Ekbåge ◽

Lars Nilsson ◽

Helena Håkansson

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Electricity Consumption ◽

Thermomechanical Pulp ◽

Process Data ◽

Hydraulic Residence Time ◽

Series Analysis ◽

Pulp Properties ◽

The Impact ◽

Specific Electricity Consumption

Frequently sampled process data from a conical disc refiner and infrequently sampled pulp data from a full scale chemi-thermomechanical pulp (CTMP) mill were evaluated to study autocovariance with aspects of potential dynamic modelling applicability. Two trial measurements with an online pulp analyzer at decreased sampling intervals were performed. For variability analysis, time-series containing up to one day of operational data were used. At the chip refiner, the clearest significant autocovariance was identified for the specific electricity consumption, based on the longer sequences. Most of the estimated pulp properties indicated low or non-significant autocovariance, limiting applicability of a specific dynamic model. A mill trial was conducted to investigate the impact from an increase in the conical disc gap on the specific electricity consumption and the resulting freeness. The response time from the gap change in the refiner to measured change in freeness was estimated at 19 min, which was approximately the hydraulic residence time in the latency chest. The relevance of this study lies in applicability of mill-data-driven modelling to capture the dynamics of a specific refining process. Through mill trials the sampling speed of pulp properties was more than doubled to gain insights into short term systematic variations by applying time-series-analysis.

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