A Modified DBSCAN Algorithm for Anomaly Detection in Time-series Data with Seasonality

Anomaly detection concerns identifying anomalous observations or patterns that are a deviation from the dataset's expected behaviour. The detection of anomalies has significant and practical applications in several industrial domains such as public health, finance, Information Technology (IT), security, medical, energy, and climate studies. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm is a density-based clustering algorithm with the capability of identifying anomalous data. In this paper, a modified DBSCAN algorithm is proposed for anomaly detection in time-series data with seasonality. For experimental evaluation, a monthly temperature dataset was employed and the analysis set forth the advantages of the modified DBSCAN over the standard DBSCAN algorithm for the seasonal datasets. From the result analysis, we may conclude that DBSCAN is used for finding the anomalies in a dataset but fails to find local anomalies in seasonal data. The proposed Modified DBSCAN approach helps to find both the global and local anomalies from the seasonal data. Using normal DBSCAN we are able to get 19 (2.16%) anomaly points. While using the modified approach for DBSCAN we are able to get 42 (4.79%) anomaly points. In comparison we can say that we are able to get 2.11% more anomalies using the modified DBSCAN approach. Hence, the proposed Modified DBSCAN algorithm outperforms in comparison with the DBSCAN algorithm to find local anomalies.

STR: Seasonal-Trend Decomposition Using Regression

We propose a new method for decomposing seasonal data: a seasonal-trend decomposition using regression (STR). Unlike other decomposition methods, STR allows for multiple seasonal and cyclic components, covariates, seasonal patterns that may have noninteger periods, and seasonality with complex topology. It can be used for time series with any regular time index, including hourly, daily, weekly, monthly, or quarterly data. It is competitive with existing methods when they exist and tackles many more decomposition problems than other methods allow. STR is based on a regularized optimization and so is somewhat related to ridge regression. Because it is based on a statistical model, we can easily compute confidence intervals for components, something that is not possible with most existing decomposition methods (such as seasonal-trend decomposition using Loess, X-12-ARIMA, SEATS-TRAMO, etc.). Our model is implemented in the R package stR, so it can be applied by anyone to their own data.

Chaos analysis of pressure parameter over Ahmedabad

In the present study attempt has been made to obtain the dimensionality of atmosphere by using Grassberger and Proccacia's model of correlation dimension on pressure parameter for Ahmedabad station. Based on single variable time series, the dimension of pressure at tractor is evaluated to obtain a lower bound on the number of essential variables necessary to model atmospheric dynamics. A low dimensionality of the order of five to seven for the pressure variable was obtained if interannual and seasonal variabilities are excluded by using seasonal data.

Deciphering Temperature Seasonality in Earth's Ancient Oceans

Ongoing global warming due to anthropogenic climate change has long been recognized, yet uncertainties regarding how seasonal extremes will change in the future persist. Paleoseasonal proxy data from intervals when global climate differed from today can help constrain how and why the annual temperature cycle has varied through space and time. Records of past seasonal variation in marine temperatures are available in the oxygen isotope values of serially sampled accretionary organisms. The most useful data sets come from carefully designed and computationally robust studies that enable characterization of paleoseasonal parameters and seamless integration with mean annual temperature data sets and climate models. Seasonal data sharpen interpretations of—and quantify overlooked or unconstrained seasonal biases in—the more voluminous mean temperature data and aid in the evaluation of climate model performance. Methodologies to rigorously analyze seasonal data are now available, and the promise of paleoseasonal proxy data for the next generation of paleoclimate research is significant. Expected final online publication date for the Annual Review of Earth and Planetary Sciences, Volume 50 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Forecasting e-waste recovery scale driven by seasonal data characteristics: A decomposition-ensemble approach

Forecasting the scale of e-waste recycling is the basis for the government to formulate the development plan of circular economy and relevant subsidy policies and enterprises to evaluate resource recovery and optimise production capacity. In this article, the CH-X12 /STL-X framework for e-waste recycling scale prediction is proposed based on the idea of ‘decomposition-integration’, considering that the seasonal data characteristics of quarterly e-waste recycling scale data may lead to large forecasting errors and inconsistent forecasting results of a traditional single model. First, the seasonal data characteristics of the time series of e-waste recovery scale are identified based on Canova–Hansen (CH) test, and then the time series suitable for seasonal decomposition is extracted with X12 or seasonal-trend decomposition procedure based on loess (STL) model for seasonal components. Then, the Holt–Winters model was used to predict the seasonal component, and the support vector regression (SVR) model was used to predict the other components. Finally, the linear sum of the prediction results of each component is used to obtain the final prediction result. The empirical results show that the proposed CH-X12/STL-X forecasting framework can better meet the modelling requirements for time-series forecasting driven by different seasonal data characteristics and has better and more stable forecasting performance than traditional single models (Holt–Winters model, seasonal autoregressive integrated moving average model and SVR model).

Forecasting the Agriculture Output Values in China Based on Grey Seasonal Model

The output values for agriculture, forestry, animal husbandry, and fishery are important indicators of agricultural economic development. Therefore, accurately predicting the output values for agriculture, forestry, animal husbandry, and fishery can capture the developmental trend and the optimize the structure. Agriculture, forestry, animal husbandry, and fishery are typical seasonal industries, and thus their output values vary greatly among different seasons. To accurately estimate the seasonal variations in the observed sequence and obtain better prediction results, the output values for agriculture, forestry, animal husbandry, and fishery in different quarters from 2018 to 2021 are predicted and analyzed by using the grey seasonal model (GSM). The results indicated that the prediction accuracy of GSM is relatively high. The output values for the agriculture, forestry, animal husbandry, and fishery as well as their total output value will increase gradually. It is an important achievement of structural reform under the new normal economic situation. In addition, the GSM provides a new method for predicting seasonal data.

Pengaruh Musim Beranak Terhadap Produksi Susu Sapi Friesian Holstein (FH) di BPTSP-HMT Lembang

This research was aimed to analyze the influence of breeding season on the production of Frisian Holstein (FH) cattle milk. This research used secondary data covering milk production data of 81 lactation FH cattle and seasonal data (rainfall, temperature, humidity, and THI). The data is tabulated to yield the total data of each variable. Data obtained from each observed variables were analyzed using a t-test using the SAS program. Based on t-test, the results showed no significant effect on average milk production at rainy dan dry season during lactation periods of L1, L2, and L3. Our experiment suggested that season of calving showed no differences on milk production of FH dairy cattle during lactation periods of L1, L2, and L3. Conclusions in this research show that seasonal differences do not affect the diversity of milk production. Keywords: dry season, Friesian Holstein, milk production, rainy

Analysis Prediction of Prambanan Temple Visitors with Fuzzy Time Series Chen Model and Seasonal Auto Regressive Integrated Moving Average (SARIMA) Model

The number of visitors in tourist attractions are almost always changes each time, even for tourist attractions that are already well-known among local and foreign people, usually will tend to increase at certain times, as in the Prambanan Temple. Based on data from TWC (Taman Wisata Candi) unit office, the number of visitors of Prambanan Temple during holidays at the end of 2018 increased by 8% from the previous year. Because of its increase, the manager of tourist attractions must always try to provide the best service. Therefore, the manager of Prambanan Temple needs to know the prediction of the number of visitors in the future so that they can prepare services and innovations to increase its attractiveness. The data of Prambanan Temple visitors number is seasonal, so the visitors number prediction at Prambanan Temple will be determined using the method for seasonal data. This research tries to compare the two methods, namely Fuzzy Time Series Chen Model and Seasonal Auto Regressive Integrated Moving Average (SARIMA) Model. The results of these methods are the visitors number prediction with different errors, so it can be seen which method is better between the two.