An autocorrelation incremental fuzzy clustering framework based on dynamic conditional scoring model

In dynamic conditional score models, the innovation term of the dynamic specification is the score of the conditional distribution. These models are investigated for non-negative variables, using distributions from the generalized beta and generalized gamma families. The log-normal distribution is also considered. Applications to the daily range of stock market indices are reported and models are fitted to duration data.

Modelling stock returns volatility with dynamic conditional score models and random shifts

Motivated by the increasing abundance of data describing real-world networks that exhibit dynamical features, we propose an extension of the exponential random graph models (ERGMs) that accommodates the time variation of its parameters. Inspired by the fast-growing literature on dynamic conditional score models, each parameter evolves according to an updating rule driven by the score of the ERGM distribution. We demonstrate the flexibility of score-driven ERGMs (SD-ERGMs) as data-generating processes and filters and show the advantages of the dynamic version over the static one. We discuss two applications to temporal networks from financial and political systems. First, we consider the prediction of future links in the Italian interbank credit network. Second, we show that the SD-ERGM allows discriminating between static or time-varying parameters when used to model the U.S. Congress co-voting network dynamics.

In order to improve the accuracy of short-term load forecasting and solve the problems of time sequence, nonlinearity and multidimensionality of short-term power load data, this paper proposes a multi-step short-term power load forecasting method based on reinforcement learning Deep Q Network (DQN) and long-short-term memory network (LSTM). Firstly, the normalized historical data are sampled by time sliding window method to construct feature map as input. Then, LSTM is used as the main body to build a prediction model to capture long and short time series features. DQN is combined with LSTM network to form a skip layer to capture super-long time series features, so that DQN-LSTM can capture features of different time scales. Finally, the full-connection layer is introduced to fuse the multi-scale temporal features extracted from each layer, and the autoregressive model (AR) is used as the linear component to obtain the final prediction results. Experimental results verify that the MAPE and RMSE of the proposed prediction method are lower than those of other algorithms, and it can effectively capture the multi-scale time series features of power load data, thus improving the prediction accuracy.

The increased availability of time series datasets prompts the development of new tools and methods that allow machine learning classifiers to better cope with time series data. Time series data are usually characterized by a high space dimensionality and a very strong correlation among features. This special nature makes the development of effective time series classifiers a challenging task. This work proposes and analyzes methods combining spectral decomposition and feature selection for time series classification problems and compares them against methods that work with original time series and time-dependent features. Briefly, our approach first applies discrete cosine transform (DCT) or discrete wavelet transform (DWT) on time series data. Then, it performs supervised feature selection/reduction by selecting only the most discriminative set of coefficients to represent the data. Experimental evaluations, carried out on multiple datasets, demonstrate the benefits of our approach in learning efficient and accurate time series classifiers.

Quantifying relationships between components of a complex system is critical to understanding the rich network of interactions that characterize the behavior of the system. Traditional methods for detecting pairwise dependence of time series, such as Pearson correlation, Granger causality, and mutual information, are computed directly in the space of measured time-series values. However, for systems in which interactions are mediated by statistical properties of the time series (time-series features) over longer timescales, this approach can fail to capture the underlying dependence from limited and noisy time-series data and can be challenging to interpret. Addressing these issues, here we introduce an information-theoretic method for detecting dependence between time series mediated by time-series features that provides interpretable insights into the nature of the interactions. Our method extracts a candidate set of time-series features from sliding windows of the source time series and assesses their role in mediating a relationship to values of the target process. Across simulations of three different generative processes, we demonstrate that our feature-based approach can outperform a traditional inference approach based on raw time-series values, especially in challenging scenarios characterized by short time-series lengths, high noise levels, and long interaction timescales. Our work introduces a tool for inferring and interpreting feature-mediated interactions from time-series data, contributing to the quantitative research of complex systems more broadly, with potential applications in various domains including but not limited to neuroscience, finance, climate science, and engineering.

One of the main challenging subjects of data mining is fuzzy-clustering time series in real-world applications. Its reason can be time-series data characteristics that include high dimensional, large volume and existence of temporal ordering in data. So far, many studies have performed about issues such as addressing time-series data high dimension and applying a different effect of each dimension in clustering results. In many of the recent published resources, feature-weighting process in time-series data has employed as one effective way to overcome mentioned problems. Appropriate weight's assignment can discuss as one challenge in mentioned resources. Hence, aim of this paper is optimization of the feature-weighting process in time series clustering task. For this aim, a technique is proposed based on the combination of two fundamental concepts, including features effect-based weighting and optimization in order to optimize weight's assignment in time series clustering. Proposed technique is made possibility use of the merits of two concepts in order to improving performance fuzzy-clustering task in time-series data. Fuzzy Particle Swarm Optimization (FPSO) is used as a population-based technique for optimization of feature-weighting process and enhancing performance of clustering. Proposed technique is evaluated based on three indexes validity that includes Partition Coefficient (PC), Partition Entropy (PE) and Davios-Bouldin (DB). Experimental results are indicated that our proposed technique is efficient and can represent encouraging results. Also, clustering results by population-based a proposed technique is demonstrated the improvement of performance of fuzzy-clustering time series than other conventional techniques.

Short-term electrical load forecasting plays an important role in the safety, stability, and sustainability of the power production and scheduling process. An accurate prediction of power load can provide a reliable decision for power system management. To solve the limitation of the existing load forecasting methods in dealing with time-series data, causing the poor stability and non-ideal forecasting accuracy, this paper proposed an attention-based encoder-decoder network with Bayesian optimization to do the accurate short-term power load forecasting. Proposed model is based on an encoder-decoder architecture with a gated recurrent units (GRU) recurrent neural network with high robustness on time-series data modeling. The temporal attention layer focuses on the key features of input data that play a vital role in promoting the prediction accuracy for load forecasting. Finally, the Bayesian optimization method is used to confirm the model’s hyperparameters to achieve optimal predictions. The verification experiments of 24 h load forecasting with real power load data from American Electric Power (AEP) show that the proposed model outperforms other models in terms of prediction accuracy and algorithm stability, providing an effective approach for migrating time-serial power load prediction by deep-learning technology.

[1] A recent article by Rosenlof and Reid [2008] (hereinafter referred to as RR08) examines the relationship in the tropics between long-term cooling in the stratosphere and warming of the sea surface, with special emphasis in the western Pacific. In the stratosphere, radiosonde observations seem to show that most of the cooling took place since the mid-1990s, apparently coincident to a considerable extent with an increase in the rate of warming of sea surface temperatures (SSTs). A link was proposed by RR08 whereby the increase in SSTs leads to enhanced convection, which is responsible, dynamically, for cooling in the stratosphere. [2] The stratospheric analyses of RR08 rely considerably on tropical radiosonde observations, particularly those from several island stations in the western tropical Pacific. There is extensive evidence that the long-term variation in worldwide radiosonde temperatures is affected critically by inhomogeneities introduced through changes in instruments and measurement practices [e.g., Gaffen, 1994; Gaffen et al., 2000; Lanzante et al . , 2003a, 2003b; Climate Change Science Program (CCSP), 2006], a fact which is acknowledged by RR08. However, they minimize the importance of any such inhomogeneities in the six stations located in the ‘‘warm pool’’ region which they employ [RR08, paragraph 10]: ‘‘However, for our analysis, and in particular for changes noted post-1990, these discontinuities are not sufficient to preclude the use of these stations.’’ The purpose of this comment is to demonstrate that in fact such discontinuities are present, post-1990, and critically affect the evaluation of stratospheric temperatures made by RR08. [3] In their Figures 2, 3, and 5, RR08 display temperature time series for several levels in the lower stratosphere for the four stations located in the heart of the western Pacific warm pool. They note that while most of the earlier part of the records show little long-term trend, a pronounced cooling is seen for about the last 10 years of the record ( 1995–2005). Furthermore, they note an abrupt rise in temperature in early 1999 that lasts for about 2 years. As further evidence to bolster their findings, they note [RR08, paragraph 12] the similarity of the time series from the different stations: ‘‘. . . the overall similarity of the time series in Figure 2 shows that the assumption of homogeneity over the warm pool region is reasonable.’’ Unfortunately, there is considerable evidence to dispute the veracity of these claims. [4] The apparent substantial cooling during the final 10 years of the record can be explained largely by a change in radiosonde instrument type, specifically the transition from Viz to Vaisala, hereafter referred to as the VVT. Abrupt cooling upon introduction of Vaisala radiosondes was noted by Parker et al. [1997] for stations in Australia and New Zealand in the mid to late 1980s, and for U.S. controlled tropical Pacific stations beginning in 1995 [Stendel et al., 2000]. More generally, there has been a systematic worldwide change to Vaisala radiosondes since the 1980s with an expected artificial cooling, especially in the stratosphere [Lanzante et al., 2003a; Christy et al., 2003; CCSP, 2006] (see also Integrated Global Radiosonde Archive (IGRA) station history metadata available at http:// www1.ncdc.noaa.gov/pub/data/igra/igra-metadata.txt). [5] The IGRA metadata indicate that the VVT occurs in November or December 1995 for five of the six stations shown in Figure 1 of RR08 (Lihue, Truk, Koror, Yap, and Pago Pago). The literature cited above suggests that the major part of the drop in stratospheric temperature after 1995 (as seen quite clearly in Figure 5 of RR08) is almost certainly artificial. To further illustrate this point, Figure 1a shows several time series of stratospheric temperatures at Koror. The thick black curve is based on the IGRA data [Durre et al., 2006], which were used by RR08. Note that the IGRA data, although of very high quality (i.e., they have been carefully checked for consistency in time and in the vertical, random errors and outliers, as well as duplicate soundings, etc.), have not been adjusted to account for artificial inhomogeneities. A number of teams have produced adjusted radiosonde temperature time series, each using a different homogenization method. Also shown in Figure 1a are corresponding adjusted time series (HadAT2) [Thorne et al., 2005], RATPAC [Free et al., 2005], IUK [Sherwood et al., 2008], RAOBCORE 1.4 [Haimberger et al., 2008], and RICH [Haimberger et al., 2008] produced by these teams. In comparing the IGRA with the various adjusted time series in Figure 1a it is seen that the adjusted versions all show much less long-term cooling, as indicated by the accompanying trend lines. A closer look at Figure 1a reveals that most of the difference is attributable to the transition across 1995. The adjusted time series shows a JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 114, D12104, doi:10.1029/2008JD010542, 2009 Click Here for Full Article

Along with the development of power system, power load forecasting plays a more and more important role. It is not only an important part of power system dispatching, operation and planning, but also the foundation of economic operation and safe operation of power system. However, power load data generally have the characteristics of complexity and nonlinearity. At present, most forecasting methods, which are based on the traditional linear statistical theory, can not consider the time series and the nonlinear characteristics of load data at the same time. In this paper, a short term load forecasting model, GRU (gated recurrent unit) network is proposed, which is the variant of LSTM(long short term memory) being based on long and short memory neural network, and the simulation prediction is carried out by using the power load data of a region in Heilongjiang province. Comparing the prediction results with the traditional ones, it is proved that the error of GRU model is lower and the prediction effect is better.

MODerate-resolution Imaging Spectroradiometer (MODIS) vegetation index (VI) time-series data are among the most widely utilized remote sensing datasets. To improve the quality of MODIS VI time-series data, most prior methods have focused on correcting negatively biased VI noise by approaching the upper envelope of the VI time series. Such treatment, however, may cause overcorrections on some true local low VI values, resulting in inaccurate simulations of vegetation phenological characteristics. In addition, another challenge in reconstructing MODIS VI time series is to fill temporally continuous gaps. The earlier spatiotemporal Savitzky-Golay (STSG) method tackled this problem by utilizing multiyear VI data, but its performance heavily relies on the consistency of data across different years. In this study, we proposed an improved STSG (iSTSG) method. The new method accounts for the autocorrelation within the VI time series and fills missing values in the VI time series by leveraging spatiotemporal VI data from the current year alone. Furthermore, iSTSG incorporates an indicator to quantify potential overcorrections in the VI time series, aiming to more accurately simulate phenological characteristics. The experiments to reconstruct MODIS normalized difference VI (NDVI) time-series product (MOD13A2) at four typical sites (a million square kilometers for each site) suggest two clear advantages in iSTSG over the iterative SG (called Chen-SG) and STSG methods. First, iSTSG more accurately reconstructs the annual NDVI time series, exhibiting the smallest mean absolute differences (MADs) between the smoothed and the simulated reference NDVI time series (0.012, 0.018, and 0.020 for iSTSG, STSG, and Chen-SG, respectively). Second, iSTSG more effectively simulates phenological characteristics in the NDVI time series, including the onset dates for vegetation greenup and dormancy, as well as the crop harvest period. The advantages of iSTSG were also demonstrated when applied to the successor of MODIS, Visible Infrared Imaging Radiometer Suite (VIIRS) VI time-series product (VNP13A1). iSTSG can be implemented on the Google Earth Engine (GEE), offering significant benefits for various applications, particularly in crop mapping and vegetation/crop phenology studies.

The time series clustering is one of the important research contents in the time series data mining. Since the dimension of time series is common high, the performance of direct raw time series data clustering is not ideal. How to improve the clustering performance of time series is the main research point of this paper. Firstly, use Locality Preserving Projection (LPP) to time series samples for dimensionality reduction; secondly, carry out clustering ensemble to the lowdimension data; finally, compare the clustering performance with the existing methods such as Principal Component Analysis (PCA) and Piecewise Aggregate Approximation (PAA). Experiment results show that the proposed method is superior to the compared methods.

An optimized neural network modeling approach with genetic algorithm for short-term load forecasting based on only multiple delayed historical power load data is proposed. Genetic algorithm is used to globally optimize the number of delayed input neurons and the number of neurons in the hidden layer of the neural network architecture. Modification of Levenberg-Marquardt algorithm with Bayesian regularization is used to improve the generalization ability of the neural network. The performance of our proposed approach has been compared using actual power load data sets. Numerical results show that our proposed power load forecasting approach is comparable to the existing approaches that use multiple input variables such as power load data, day type load patterns and weather conditions

Accurate power load prediction is crucial for power dispatch and response. In order to improve the accuracy of power load prediction, this paper proposes a new method called Conv-AdaRNN which combines Convolutional Neural Network and Adaptive Learning and Forecasting for Time Series(AdaRNN) models. The method firstly uses CNN to extract valid information from effective factors, that affect the power load, such as temperature, humidity, and electricity price to construct feature vectors. Then, the data set is divided into the least relevant segments in time series according to the Time Series Distribution law. Finally, the AdaRNN, whose hidden states are assigned different weight to match time distribution, analyzes feature vectors belong to each segment and forecasts power load. Taking a certain area's power load as an example, the predicting results of the Conv-AdaRNN model were compared with those of gated recurrent unit (GRU) and AdaRNN models. The results show that, compared with other single prediction models, the Conv-AdaRNN achieved the better forecast precision.

As climate change intensifies, humid and hot regions are facing increasingly complex fluctuations in power load. Traditional load forecasting models often treat temperature as a key meteorological feature; however, in hot and humid environments, temperature alone fails to accurately capture the relationship between human thermal perception and electricity usage behavior This study proposes a CNN-BiLSTM model driven by the Heat Index for short-term power load forecasting in humid and hot climates. Based on hourly power load and meteorological data from Guangzhou in 2023, two model schemes were developed and compared—one using the Heat Index and the other using conventional temperature as input features. Experimental results demonstrate that the Heat Index more effectively captures the impact of hot-humid environment on power load. The model outperforms the temperature-driven scheme across multiple evaluation metrics, confirming the applicability and advantages of using the Heat Index as a forecasting feature in such climates.