4,184 research outputs found
A procedure for identification of appropriate state space and ARIMA models based on time-series cross-validation
In this work, a cross-validation procedure is used to identify an appropriate Autoregressive
Integrated Moving Average model and an appropriate state space model for a time series. A minimum
size for the training set is specified. The procedure is based on one-step forecasts and uses different
training sets, each containing one more observation than the previous one. All possible state space
models and all ARIMA models where the orders are allowed to range reasonably are fitted considering
raw data and log-transformed data with regular differencing (up to second order differences) and,
if the time series is seasonal, seasonal differencing (up to first order differences). The value of root
mean squared error for each model is calculated averaging the one-step forecasts obtained. The model
which has the lowest root mean squared error value and passes the Ljung–Box test using all of the
available data with a reasonable significance level is selected among all the ARIMA and state space
models considered. The procedure is exemplified in this paper with a case study of retail sales of
different categories of women’s footwear from a Portuguese retailer, and its accuracy is compared
with three reliable forecasting approaches. The results show that our procedure consistently forecasts
more accurately than the other approaches and the improvements in the accuracy are significant.info:eu-repo/semantics/publishedVersio
Temporally-aware algorithms for the classification of anuran sounds
Several authors have shown that the sounds of anurans can be used as an indicator of
climate change. Hence, the recording, storage and further processing of a huge
number of anuran sounds, distributed over time and space, are required in order to
obtain this indicator. Furthermore, it is desirable to have algorithms and tools for
the automatic classification of the different classes of sounds. In this paper, six
classification methods are proposed, all based on the data-mining domain, which
strive to take advantage of the temporal character of the sounds. The definition and
comparison of these classification methods is undertaken using several approaches.
The main conclusions of this paper are that: (i) the sliding window method attained
the best results in the experiments presented, and even outperformed the hidden
Markov models usually employed in similar applications; (ii) noteworthy overall
classification performance has been obtained, which is an especially striking result
considering that the sounds analysed were affected by a highly noisy background;
(iii) the instance selection for the determination of the sounds in the training dataset
offers better results than cross-validation techniques; and (iv) the temporally-aware
classifiers have revealed that they can obtain better performance than their nontemporally-aware
counterparts.ConsejerĂa de InnovaciĂłn, Ciencia y Empresa (Junta de AndalucĂa, Spain): excellence eSAPIENS number TIC 570
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