High Dimensional Nonstationary Time Series Modelling with Generalized Dynamic Semiparametric Factor Model

(High dimensional) time series which reveal nonstationary and possibly periodic behavior occur frequently in many fields of science. In this article, we separate the modeling of high dimensional time series to time propagation of low dimensional time series and high dimensional time invariant functions via functional factor analysis. We propose a two-step estimation procedure. At the first step, we detect the deterministic trends of the time series by incorporating time basis selected by the group Lasso-type technique and choose the space basis based on smoothed functional principal component analysis. We show properties of this estimator under various situations extending current variable selection studies. At the second step, we obtain the detrended low dimensional stochastic process, but it also poses an important question: is it justified, from an inferential point of view, to base further statistical inference on the estimated stochastic time series? We show that the difference of the inference based on the estimated time series and "true" unobserved time series is asymptotically negligible, which finally allows one to study the dynamics of the whole high-dimensional system with a low dimensional representation together with the deterministic trend. We apply the method to our motivating empirical problems: studies of the dynamic behavior of temperatures (further used for pricing weather derivatives), implied volatilities and risk patterns and correlated brain activities (neuro-economics related) using fMRI data, where a panel version model is also presented

Assessing the potential of an algorithm based on mean climatic data to predict wheat yield

peer reviewedThe real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in combination with real-time monitoring of the input climatic data of these models, but unknown future weather remains the main obstacle to reliable yield prediction. Since accurate weather forecasts can be made only a short time in advance, much information can be derived from analyzing past weather data. This paper presents a methodology that addresses the problem of unknown future weather by using a daily mean climatic database, based exclusively on available past measurements. It involves building climate matrix ensembles, combining different time ranges of projected mean climate data and real measured weather data originating from the historical database or from real-time measurements performed in the field. Used as an input for the STICS crop model, the datasets thus computed were used to perform statistical within-season biomass and yield prediction. This work demonstrated that a reliable predictive delay of 3-4 weeks could be obtained. In combination with a local micrometeorological station that monitors climate data in real-time, the approach also enabled us to (i) predict potential yield at the local level, (ii) detect stress occurrence and (iii) quantify yield loss (or gain) drawing on real monitored climatic conditions of the previous few days.Suivi en temps réel de l’environnement d’une parcelle agricole par un réseau de micro-capteurs en vue d’optimiser l’apport en engrais azoté

Precipitation downscaling in Canadian Prairie Provinces using the LARS-WG and GLM approaches

Two stochastic precipitation simulation models, namely the Long Ashton Research Station weather generator (LARS-WG) and a Generalized Linear Model-based weather generator (GLM-WG), are evaluated for downscaling daily precipitation at four selected locations (Banff, Calgary, Saskatoon and Winnipeg) in the Canadian Prairies. These weather generators model precipitation occurrence and amount components separately. Large-scale climate variables (including mean temperature, sea level pressure and relative humidity, derived from National Centers for Environmental Prediction reanalysis data) and observed precipitation records are used to calibrate and validate GLM-WG, while only observed precipitation records are used to calibrate and validate LARS-WG. A comparison of common statistical properties (i.e. annual/monthly means, variability of daily and monthly precipitation and monthly proportion of dry days) and characteristics of drought and extreme precipitation events derived from simulated and observed daily precipitation for the calibration (1961-1990) and validation (1991-2003) periods shows that both weather generators are able to simulate most of the statistical properties of the historical precipitation records, but GLM-WG appears to perform better than LARS-WG for simulating precipitation extremes and temporal variability of drought severity indices. For developing projected changes to precipitation characteristics, a change factor approach based on Canadian Global Climate Model (CGCM) simulated current (1961-1990) and future (2071-2100) period precipitation is used for driving simulations of LARS-WG, while for driving GLM-WG simulations, large-scale predictor variables derived from CGCM current and future period outputs are used. Results of both weather generators suggest significant increases to the mean annual precipitation for the 2080s. Changes to selected return levels of annual daily precipitation extremes are found to be both location- and generator-dependent, with highly significant increases noted for Banff with LARS-WG and for both Banff and Calgary with GLM-WG. Overall, 5- and 10-yr return levels are associated with increases (with the exception of Winnipeg) while 30- and 50-yr return levels are associated with site-dependent increases or decreases. A simple precipitation-based drought severity index suggests decreases in drought severity for the 2080s. © 2013 Canadian Water Resources Association