Forecasting inflation using disaggregates and machine learning

This paper examines the effectiveness of several forecasting methods for predicting inflation, focusing on aggregating disaggregated forecasts - also known in the literature as the bottom-up approach. Taking the Brazilian case as an application, we consider different disaggregation levels for inflation and employ a range of traditional time series techniques as well as linear and nonlinear machine learning (ML) models to deal with a larger number of predictors. For many forecast horizons, the aggregation of disaggregated forecasts performs just as well survey-based expectations and models that generate forecasts using the aggregate directly. Overall, ML methods outperform traditional time series models in predictive accuracy, with outstanding performance in forecasting disaggregates. Our results reinforce the benefits of using models in a data-rich environment for inflation forecasting, including aggregating disaggregated forecasts from ML techniques, mainly during volatile periods. Starting from the COVID-19 pandemic, the random forest model based on both aggregate and disaggregated inflation achieves remarkable predictive performance at intermediate and longer horizons.Comment: 44 pages, 9 figure

Virulência de isolados de Magnaporthe oryzae do trigo e poáceas invasoras.

Editores técnicos: Joseani Mesquita Antunes, Ana Lídia Variani Bonato, Márcia Barrocas Moreira Pimentel

Familly with two different cases of post- and pre-natal L1 syndrome; When hydrocephaly become "multidisciplinary headache"

open11openBukvic, Nenad; Boaretto, Francesca; Loverro, Giuseppe; Susca, Francesco C.; Lovaglio, Rosaura; Patruno, Margherita; Bukvic, Dragoslav; Starcevic, Srdjan; Vazza, Giovanni; Mostaciuollo, Maria Luisa; Resta, NicolettaBukvic, Nenad; Boaretto, Francesca; Loverro, Giuseppe; Susca, Francesco C.; Lovaglio, Rosaura; Patruno, Margherita; Bukvic, Dragoslav; Starcevic, Srdjan; Vazza, Giovanni; Mostaciuollo, Maria Luisa; Resta, Nicolett

Virulência de isolados de Magnaporthe oryzae do trigo e poáceas invasoras.

Orientador: João Leodato Nunes Maciel

Adubação foliar em tomateiro estaqueado. (Lycopersicum esculentum, mill). Santa Cruz - Kada.

Experimento realizado em solo latossol vermelho escuro-fase arenosa intergra de Terra roxa estruturada.Resumo

Raças de Magnaporthe oryzae do trigo em 2013.

Editores técnicos: Joseani Mesquita Antunes, Ana Lídia Variani Bonato, Márcia Barrocas Moreira Pimentel

Variabilidade genética de Magnaporthe oryzae do trigo e os tipos compatíveis de populações simpátricas do patógeno.

Coorientador: João Leodato Nunes Maciel

Cana-de-açúcar cultivada em solo adubado com lodo de esgoto: nutrientes, metais pesados e produtividade.

A pesquisa objetivou avaliar o uso de lodo de esgoto (Le) na adubação de soqueira (2o corte) de cana-de-açúcar (Saccharum spp., var. RB72-454). Aplicou-se Le ao solo, localizando-o no fundo de um sulco com 15 cm de profundidade e distando 40 cm da linha de cana. Avaliaram-se os efeitos das doses do Le (0, 15 e 30 t.ha-1) nas produtividades de biomassa e de açúcar, nos teores de nutrientes e de metais pesados do solo e da planta. O Le diminuiu a acidez potencial (H + Al) do solo e forneceu nutrientes para a cana-de-açúcar, principalmente P, S, Ca, Cu e Zn, o que refletiu em aumentos de produtividades de colmos e de açúcar por hectare. O Le causou aumentos de exportações de P, S, Ca, Cu, K, Mg e Ni pela parte aérea da cana-de-açúcar; tais aumentos, por sua vez, foram motivados pelos aumentos dos teores destes elementos no tecido vegetal, e da produtividade em biomassa. Os metais pesados (Cd, Cr, Ni e Pb), contidos no Le, não apresentaram perigo à cadeia trófica à curto prazo

Discriminating chaotic and stochastic time series using permutation entropy and artificial neural networks

Extracting relevant properties of empirical signals generated by nonlinear, stochastic, and high-dimensional systems is a challenge of complex systems research. Open questions are how to differentiate chaotic signals from stochastic ones, and how to quantify nonlinear and/or high-order temporal correlations. Here we propose a new technique to reliably address both problems. Our approach follows two steps: first, we train an artificial neural network (ANN) with flicker (colored) noise to predict the value of the parameter, $\alpha$, that determines the strength of the correlation of the noise. To predict $\alpha$ the ANN input features are a set of probabilities that are extracted from the time series by using symbolic ordinal analysis. Then, we input to the trained ANN the probabilities extracted from the time series of interest, and analyze the ANN output. We find that the $\alpha$ value returned by the ANN is informative of the temporal correlations present in the time series. To distinguish between stochastic and chaotic signals, we exploit the fact that the difference between the permutation entropy (PE) of a given time series and the PE of flicker noise with the same $\alpha$ parameter is small when the time series is stochastic, but it is large when the time series is chaotic. We validate our technique by analysing synthetic and empirical time series whose nature is well established. We also demonstrate the robustness of our approach with respect to the length of the time series and to the level of noise. We expect that our algorithm, which is freely available, will be very useful to the community

Spatial permutation entropy distinguishes resting brain states

We use ordinal analysis and spatial permutation entropy to distinguish between eyes-open and eyes-closed resting brain states. To do so, we analyze EEG data recorded with 64 electrodes from 109 healthy subjects, under two one-minute baseline runs: One with eyes open, and one with eyes closed. We use spatial ordinal analysis to distinguish between these states, where the permutation entropy is evaluated considering the spatial distribution of electrodes for each time instant. We analyze both raw and post-processed data considering only the alpha-band frequency (8–12 Hz) which is known to be important for resting states in the brain. We conclude that spatial ordinal analysis captures information about correlations between time series in different electrodes. This allows the discrimination of eyes closed and eyes open resting states in both raw and filtered data. Filtering the data only amplifies the distinction between states. Importantly, our approach does not require EEG signal pre-processing, which is an advantage for real-time applications, such as brain-computer interfaces.B.R.R.B. and E.E.N.M. acknowledge support of São Paulo Research Foundation (FAPESP), Brazil, Proc. 2018/03211-6 and 2021/09839-0; and Financiadora de Estudos e Projetos (FINEP), Brazil. R.C.B. acknowledges support of Western Institute for Neuroscience Clinical Research Postdoctoral Fellowship and Western Academy for Advanced Research. K.L.R. acknowledges supported of German Academic Exchange Service (DAAD). C.M. acknowledges support of Ministerio de Ciencia, Innovación ������ Universidades (PID2021-123994NB-C21), Spain and Institució Catalana de Recerca i Estudis Avançats (ICREA), Spain.Peer ReviewedPostprint (published version