30 research outputs found
Energy Time Series Forecasting Based on Pattern Sequence Similarity
This paper presents a new approach to forecast the behavior of time series based on similarity of pattern sequences. First,
clustering techniques are used with the aim of grouping and labeling the samples from a data set. Thus, the prediction of a data point is
provided as follows: first, the pattern sequence prior to the day to be predicted is extracted. Then, this sequence is searched in the
historical data and the prediction is calculated by averaging all the samples immediately after the matched sequence. The main novelty
is that only the labels associated with each pattern are considered to forecast the future behavior of the time series, avoiding the use of
real values of the time series until the last step of the prediction process. Results from several energy time series are reported and the
performance of the proposed method is compared to that of recently published techniques showing a remarkable improvement in the
prediction.Ministerio de Ciencia y Tecnología TIN2007- 68084-C-00Junta de Andalucia P07-TIC- 0261
Forecasting Stock Time-Series using Data Approximation and Pattern Sequence Similarity
Time series analysis is the process of building a model using statistical
techniques to represent characteristics of time series data. Processing and
forecasting huge time series data is a challenging task. This paper presents
Approximation and Prediction of Stock Time-series data (APST), which is a two
step approach to predict the direction of change of stock price indices. First,
performs data approximation by using the technique called Multilevel Segment
Mean (MSM). In second phase, prediction is performed for the approximated data
using Euclidian distance and Nearest-Neighbour technique. The computational
cost of data approximation is O(n ni) and computational cost of prediction task
is O(m |NN|). Thus, the accuracy and the time required for prediction in the
proposed method is comparatively efficient than the existing Label Based
Forecasting (LBF) method [1].Comment: 11 page
A novel ensemble method for electric vehicle power consumption forecasting: Application to the Spanish system
The use of electric vehicle across the world has become one of the most challenging issues for environmental policies. The galloping climate change and the expected running out of fossil fuels turns the use of such non-polluting cars into a priority for most developed countries. However, such a use has led to major concerns to power companies, since they must adapt their generation to a new scenario, in which electric vehicles will dramatically modify the curve of generation. In this paper, a novel approach based on ensemble learning is proposed. In particular, ARIMA, GARCH and PSF algorithms' performances are used to forecast the electric vehicle power consumption in Spain. It is worth noting that the studied time series of consumption is non-stationary and adds difficulties to the forecasting process. Thus, an ensemble is proposed by dynamically weighting all algorithms over time. The proposal presented has been implemented for a real case, in particular, at the Spanish Control Centre for the Electric Vehicle. The performance of the approach is assessed by means of WAPE, showing robust and promising results for this research field.Ministerio de Economía y Competitividad Proyectos ENE2016-77650-R, PCIN-2015-04 y TIN2017-88209-C2-R
Pattern-Based Analysis of Time Series: Estimation
While Internet of Things (IoT) devices and sensors create continuous streams
of information, Big Data infrastructures are deemed to handle the influx of
data in real-time. One type of such a continuous stream of information is time
series data. Due to the richness of information in time series and inadequacy
of summary statistics to encapsulate structures and patterns in such data,
development of new approaches to learn time series is of interest. In this
paper, we propose a novel method, called pattern tree, to learn patterns in the
times-series using a binary-structured tree. While a pattern tree can be used
for many purposes such as lossless compression, prediction and anomaly
detection, in this paper we focus on its application in time series estimation
and forecasting. In comparison to other methods, our proposed pattern tree
method improves the mean squared error of estimation
Holistic Measures for Evaluating Prediction Models in Smart Grids
The performance of prediction models is often based on "abstract metrics"
that estimate the model's ability to limit residual errors between the observed
and predicted values. However, meaningful evaluation and selection of
prediction models for end-user domains requires holistic and
application-sensitive performance measures. Inspired by energy consumption
prediction models used in the emerging "big data" domain of Smart Power Grids,
we propose a suite of performance measures to rationally compare models along
the dimensions of scale independence, reliability, volatility and cost. We
include both application independent and dependent measures, the latter
parameterized to allow customization by domain experts to fit their scenario.
While our measures are generalizable to other domains, we offer an empirical
analysis using real energy use data for three Smart Grid applications:
planning, customer education and demand response, which are relevant for energy
sustainability. Our results underscore the value of the proposed measures to
offer a deeper insight into models' behavior and their impact on real
applications, which benefit both data mining researchers and practitioners.Comment: 14 Pages, 8 figures, Accepted and to appear in IEEE Transactions on
Knowledge and Data Engineering, 2014. Authors' final version. Copyright
transferred to IEE
Rule Optimization of Fuzzy Inference System Sugeno using Evolution Strategy for Electricity Consumption Forecasting
The need for accurate load forecasts will increase in the future because of the dramatic changes occurring in the electricity consumption. Sugeno fuzzy inference system (FIS) can be used for short-term load forecasting. However, challenges in the electrical load forecasting are the data used the data trend. Therefore, it is difficult to develop appropriate fuzzy rules for Sugeno FIS. This paper proposes Evolution Strategy method to determine appropriate rules for Sugeno FIS that have minimum forecasting error. Root Mean Square Error (RMSE) is used to evaluate the goodness of the forecasting result. The numerical experiments show the effectiveness of the proposed optimized Sugeno FIS for several test-case problems. The optimized Sugeno FIS produce lower RMSE comparable to those achieved by other well-known method in the literature
Discovering And Labelling Of Temporal Granularity Patterns In Electric Power Demand With A Brazilian Case Study
Clustering is commonly used to group data in order to represent the behaviour of a system as accurately as possible by obtaining patterns and profiles. In this paper, clustering is applied with partitioning-clustering techniques, specifically, Partitioning around Medoids (PAM) to analyse load curves from a city of South-eastern Brazil in São Paulo state. A top-down approach in time granularity is performed to detect and to label profiles which could be affected by seasonal trends and daily/hourly time blocks. Time-granularity patterns are useful to support the improvement of activities related to distribution, transmission and scheduling of energy supply. Results indicated four main patterns which were post-processed in hourly blocks by using shades of grey to help final-user to understand demand thresholds according to the meaning of dark grey, light grey and white colours. A particular and different behaviour of load curve was identified for the studied city if it is compared to the classical behaviour of urban cities.36357559