Calibration Windows Research Articles

An interstage gain calibration technique for pipelined ADCs exploiting complementary dithering and calibration windows detector

An interstage gain calibration technique for pipelined ADCs exploiting complementary dithering and calibration windows detector

Spatio-temporal comparisons of the hydrogen-alpha line width and ALMA 3 mm brightness temperature in the weak solar network

Comparisons between the Atacama Large Millimeter/sub-millimeter Array (ALMA) 3 mm emission and a range of optical and UV solar observations have found the strongest correspondence between the width of the hydrogen alpha line at 656.3 nm and the 3 mm brightness temperature. Previous studies on the oscillatory power of p-modes using ALMA Band 3 and Band 6 data in the 3–5 min period bandpass have found a confusing mix of results, with many reporting a complete lack of the p-mode enhancement typically found in other chromospheric observables. We study these issues using an extensive, publicly available coordinated data set targeting a region of weak network flux near disk center at time SOL 2017-03-17T15:42-16:45. We focus on the Interferometric Bidimensional Spectropolarimeter (IBIS) H-alpha and ALMA 3 mm data series. We confirm the strong correlation between the H-alpha line width and the 3 mm brightness temperature, but find a bimodal relation between the two diagnostics, with a shallower slope of 7.4e-5 Å/K in cooler regions and steeper slope of 1.2e-4 Å/K in hotter regions. The origin of the bimodal distribution is unknown, but does hold for the duration of the observations. Both slopes are steeper than a previously reported value, but this is likely due to systematic differences in the analysis. We then calculate the oscillatory power in the H-alpha and 3 mm data. The IBIS data clearly show the p-mode oscillations in spatially averaged power spectra while the ALMA data do not. However, when we remove IBIS data at times corresponding to the ALMA calibration windows, the spatially averaged power spectra for the two data series are nearly identical, with a Pearson correlation coefficient of 0.9895. Further, the power in the two bands remains strongly correlated when the spatial information is retained but the power is integrated over different temporal frequency bands. We therefore argue that the lack of observed p-modes in the ALMA data may be predominantly due to spectral windowing induced by the timing and duration of the calibration observations. Finally, we find that spatial maps of oscillatory power at 3 mm display the pattern of magnetic shadows and halos typically displayed by other chromospheric diagnostics.

LASSO principal component averaging: A fully automated approach for point forecast pooling

This paper develops a novel, fully automated forecast averaging scheme which combines LASSO estimation with principal component averaging (PCA). LASSO-PCA (LPCA) explores a pool of predictions based on a single model but calibrated to windows of different sizes. It uses information criteria to select tuning parameters and hence reduces the impact of researchers’ ad hoc decisions. The method is applied to average predictions of hourly day-ahead electricity prices over 650 point forecasts obtained with various lengths of calibration windows. It is evaluated on four European and American markets with an out-of-sample period of almost two and a half years and compared to other semi- and fully automated methods, such as the simple mean, AW/WAW, LASSO, and PCA. The results indicate that LASSO averaging is very efficient in terms of forecast error reduction, whereas PCA is robust to the selection of the specification parameter. LPCA inherits the advantages of both methods and outperforms other approaches in terms of the mean absolute error, remaining insensitive to the choice of a tuning parameter.

Evaluating the discrimination ability of proper multi-variate scoring rules

Proper scoring rules are commonly applied to quantify the accuracy of distribution forecasts. Given an observation they assign a scalar score to each distribution forecast, with the lowest expected score attributed to the true distribution. The energy and variogram scores are two rules that have recently gained some popularity in multivariate settings because their computation does not require a forecast to have parametric density function and so they are broadly applicable. Here we conduct a simulation study to compare the discrimination ability between the energy score and three variogram scores. Compared with other studies, our simulation design is more realistic because it is supported by a historical data set containing commodity prices, currencies and interest rates, and our data generating processes include a diverse selection of models with different marginal distributions, dependence structure, and calibration windows. This facilitates a comprehensive comparison of the performance of proper scoring rules in different settings. To compare the scores we use three metrics: the mean relative score, error rate and a generalized discrimination heuristic. Overall, we find that the variogram score with parameter p=0.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p=0.5$$\\end{document} outperforms the energy score and the other two variogram scores.

PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices

Recently, the development in combining point forecasts of electricity prices obtained with different length of calibration windows have provided an extremely efficient and simple tool for improving predictive accuracy. However, the proposed methods are strongly dependent on expert knowledge and may not be directly transferred from one to another model or market. Hence, we consider a novel extension and propose to use principal component analysis (PCA) to automate the procedure of averaging over a rich pool of predictions. We apply PCA to a panel of over 650 point forecasts obtained for different calibration windows length. The robustness of the approach is evaluated with three different forecasting tasks, i.e., forecasting day-ahead prices, forecasting intraday ID3 prices one day in advance, and finally very short term forecasting of ID3 prices (i.e., six hours before delivery). The empirical results are compared using the Mean Absolute Error measure and Giacomini and White test for conditional predictive ability (CPA). The results indicate that PCA averaging not only yields significantly more accurate forecasts than individual predictions but also outperforms other forecast averaging schemes.

Averaging Predictive Distributions Across Calibration Windows for Day-Ahead Electricity Price Forecasting

The recent developments in combining point forecasts of day-ahead electricity prices across calibration windows have provided an extremely simple, yet a very efficient tool for improving predictive accuracy. Here, we consider two novel extensions of this concept to probabilistic forecasting: one based on Quantile Regression Averaging (QRA) applied to a set of point forecasts obtained for different calibration windows, the other on a technique dubbed Quantile Regression Machine (QRM), which first averages these point predictions, then applies quantile regression to the combined forecast. Once computed, we combine the probabilistic forecasts across calibration windows by averaging probabilities of the corresponding predictive distributions. Our results show that QRM is not only computationally more efficient, but also yields significantly more accurate distributional predictions, as measured by the aggregate pinball score and the test of conditional predictive ability. Moreover, combining probabilistic forecasts brings further significant accuracy gains.

A Note on Averaging Day-Ahead Electricity Price Forecasts Across Calibration Windows

We propose a novel concept in energy forecasting and show that averaging day-ahead electricity price forecasts of a predictive model across 28–728 day calibration windows yields better results than selecting only one “optimal” window length. Even more significant accuracy gains can be achieved by averaging over a few, carefully selected windows.

Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting

We conduct an extensive empirical study on the selection of calibration windows for day-ahead electricity price forecasting, which involves six year-long datasets from three major power markets and four autoregressive expert models fitted either to raw or transformed prices. Since the variability of prediction errors across windows of different lengths and across datasets can be substantial, selecting ex-ante one window is risky. Instead, we argue that averaging forecasts across different calibration windows is a robust alternative and introduce a new, well-performing weighting scheme for averaging these forecasts.

Optimizing the Lee-Carter Approach in the Presence of Structural Changes in Time and Age Patterns of Mortality Improvements.

Researchers using the Lee-Carter approach have often assumed that the time-varying index evolves linearly and that the parameters describing the age pattern of mortality decline are time-invariant. However, as several empirical studies suggest, the two assumptions do not seem to hold when the calibration window begins too early. This problem gives rise to the question of identifying the longest calibration window for which the two assumptions hold true. To address this question, we contribute a likelihood ratio-based sequential test to jointly test whether the two assumptions are satisfied. Consistent with the mortality structural changes observed in previous studies, our testing procedure indicates that the starting points of the optimal calibration windows for most populations fall between 1960 and 1990. Using an out-of-sample analysis, we demonstrate that in most cases, models that are estimated to the optimized calibration windows result in more accurate forecasts than models that are fitted to all available data or data beyond 1950. We further apply the proposed testing procedure to data over different age ranges. We find that the optimal calibration windows for age group 0-49 are generally shorter than those for age group 50-89, indicating that mortality at younger ages might have undergone (another) structural change in recent years.

Robust Portfolio Optimization with Multivariate Copulas: A Worst-Case CVaR approach

Using data from the S&P 500 stocks from 1990 to 2015, we address the uncertainty of distribution of assets’ returns in Conditional Value-at-Risk (CVaR) minimization model by applying multidimensional mixed Archimedean copula function and obtaining its robust counterpart. We implement a dynamic investing viable strategy where the portfolios are optimized using three different length of rolling calibration windows. The out-of-sample performance is evaluated and compared against two benchmarks: a multidimensional Gaussian copula model and a constant mix portfolio. Our empirical analysis shows that the Mixed Copula-CVaR approach generates portfolios with better downside risk statistics for any rebalancing period and it is more profitable than the Gaussian Copula-CVaR and the 1/N portfolios for daily and weekly rebalancing. To cope with the dimensionality problem we select a set of assets that are the most diversified, in some sense, to the S&P 500 index in the constituent set. The accuracy of the VaR forecasts is determined by how well they minimize a capital requirement loss function. In order to mitigate data-snooping problems, we apply a test for superior predictive ability to determine which model significantly minimizes this expected loss function. We find that the minimum average loss of the mixed Copula-CVaR approach is smaller than the average performance of the Gaussian Copula-CVaR.

Calibration and Validation of a Mechanistic Micromilling Force Prediction Model

Mechanistic force prediction models require a calibration phase to determine the cutting coefficients describing the tool–target material interaction. The model prediction performance depends on the experimental correctness and representativeness of input data, especially in micromilling, where facing process uncertainties is a big challenge. The present paper focuses on input data correctness introducing a clear and repeatable calibration experimental procedure based on accurate force data acquisitions. Input data representativeness has been directly connected to the calibration window choice, i.e., the selection of the space of process parameters combinations used to calibrate the model. Also, the model validation has to be carefully carried out to make the model significant: the present paper proposes a clear and repeatable validation procedure based on the model performance index calculation over the whole process operating window, i.e., the space of parameters where the process works correctly. An objective indication of the model suitability can be obtained by applying this procedure. Comparisons among prediction performances produced by different calibration windows are allowed. This paper demonstrates how the calibration window selection determines the model prediction performance, which seems to improve if calibration is carried out where forces assume high values. Some important considerations on the process parameters role on cutting forces and on the model capability have also been drawn from the model validation results.

On the predictive ability of mechanistic models for the Haitian cholera epidemic.

Predictive models of epidemic cholera need to resolve at suitable aggregation levels spatial data pertaining to local communities, epidemiological records, hydrologic drivers, waterways, patterns of human mobility and proxies of exposure rates. We address the above issue in a formal model comparison framework and provide a quantitative assessment of the explanatory and predictive abilities of various model settings with different spatial aggregation levels and coupling mechanisms. Reference is made to records of the recent Haiti cholera epidemics. Our intensive computations and objective model comparisons show that spatially explicit models accounting for spatial connections have better explanatory power than spatially disconnected ones for short-to-intermediate calibration windows, while parsimonious, spatially disconnected models perform better with long training sets. On average, spatially connected models show better predictive ability than disconnected ones. We suggest limits and validity of the various approaches and discuss the pathway towards the development of case-specific predictive tools in the context of emergency management.

Towards multivariate statistical process control in the wood pellet industry

Abstract Multivariate statistical process control (MSPC) based on principal component analysis (PCA) and partial least squares (PLS) regression was simulated, based on industrial data collected over a two-year period within a plant producing wood pellets as biofuel. The data used in the simulations consisted of values of five variables of analysed intermediate products (sawdust and powder) and end products (pellets), acquired during processes with seven on-line settings of controls. PCA global modelling revealed an overlap in the data between years and detected three different pellet types. Correlations within the dataset indicated there was a time lag of up to 14 h. Therefore, PLS prediction of current product values was based on observations containing the current process settings and all variable values within a preceding 18 h time interval. Global models showed that predictions of the dryness of sawdust, milled sawdust and pellets had good accuracy, whereas predictions of pellet bulk density and mechanical durability were less accurate. Dynamic and local PLS modelling showed that more accurate predictions of pellet dryness were obtained if all previous observations were included in the calibration set rather than observations in calibration windows of the 10 or 100 preceding observations. The results illustrate the possibilities to implement MSPC in the wood pellet industry, potentially handling huge amounts of data. To develop and implement the next phase of process control more parameters must be included in the MSPC models, e.g. data acquired using on-line instruments to continuously collect information on variations in the stream of material.

Modelling microbial population dynamics in nitritation processes

In the wastewater treatment industry nitrite is no longer viewed as a toxic and undesirable intermediate in the biological oxidation chain transforming ammonium into nitrate. On the contrary, it is receiving growing attention as the pivotal component in a variety of new processes for biological nutrient removal. These new practises aim at saving energy and resources through the nitritation shortcut as an alternative to full nitrification. In order to understand the biological and engineering implications of nitritation, models have to be adapted to these new process conditions and related changes in microbial community. For this reason a previous modification of the ASM3 model [Iacopozzi, I., Innocenti, V., Marsili-Libelli, S., Giusti, E. 2007. A modified Activated Sludge Model No. 3 (ASM3) with two-step nitrification-denitrification. Environmental Modelling & Software 22: 847–861] was adapted to these particular process conditions, that the conventional models fail to reproduce satisfactorily. As with many new nitrogen removal processes, the nitritating condition does not occur spontaneously on a major scale, but it must be induced and maintained through computer control. The data used to calibrate this modified model were obtained from a long-horizon experiment in which a pilot-scale Sequencing Batch Reactor (SBR) fed with landfill leachate was kept in nitritating conditions. In this paper we set out to investigate the following points: (a) demonstrate that our previous two-step nitrification model could be adapted to describe the nitritating condition; (b) provide a thorough sensitivity/identifiability assessment of this new model, since no general sensitivity study on two-step nitrification models is available as yet; (c) try to relate the observed changes in parameter values to the possible modifications in microbial inventory during the shift from nitrification to nitritation, even if the available data were very scarce. On the first count, the model was modified by fully separating the nitrite and nitrate denitrification routes (a partial decoupling was already attempted in the previous model) and using the mixed substrate assumption. In this way, correct anoxic respiration rates and substrate consumption were obtained even when the oxidation was limited to nitrite alone. The second aspect was investigated by carrying out a complete sensitivity study considering several output combinations and ranking the model parameters according to their identifiability, showing that the most sensitive parameters are those related to the microbial characteristics. For the third aspect, several calibration “windows” were selected, in which nitrite and nitrate measurements were available, and the identifiable parameters were calibrated with each data set. From the examination of the parametric variations in the calibration windows it can be concluded that the nitritating conditions represent a critical process condition forcing drastic changes in the microbial inventory, which may be partly reflected by parametric changes.

Ru(DMSO)4Cl2 nano-aggregated Nafion membrane modified electrode for simultaneous electrochemical detection of hypoxanthine, xanthine and uric acid

Ru(DMSO)4Cl2 is a good precursor complex in synthetic inorganic chemistry and also considered to be an efficient anti-cancer drug. Here in, we are reporting ∼80 nm nano-aggregates of Ru(DMSO)4Cl2 complex stabilized Nafion membrane modified glassy carbon electrode (GCE/Nf–{RuDMSO–Cl–H2O}–MME) for simultaneous electro-catalytic oxidations and separation-less detection of purine bases; uric acid (UA), xanthine (X) and hypoxanthine (Hx) in a physiological solution. The MME was characterized using solid state UV–Vis, X-ray photoelectron spectroscopy (XPS) and transmission electron-microscopy (TEM) techniques. It has been found that Ru(DMSO)4Cl2 undergoes dissolution reactions within the water rich hydrophilic micro-channels of Nafion membrane and get electro-statically stabilized as [RuIIClx(DMSO)y(H2O)4−(x+y)]n+ through the cationic sulphonic acid structures. The MME film behaves like metallic electronic conductor, investigated with Fe(CN)63-/4- redox couple. This new electrode shows efficient electrochemical sensing of the UA, X and Hx with a calibration windows (detection limit) up to 700 (0.372 μM), 500 (2.351 μM) and 300 μM (2.37 μM) respectively. The electrode shows exceptional stability and applicability to various real samples.

Time-dependent <i>Z-R</i> relationships for estimating rainfall fields from radar measurements

Abstract. The operational use of weather radars has become a widespread and useful tool for estimating rainfall fields. The radar-gauge adjustment is a commonly adopted technique which allows one to reduce bias and dispersion between radar rainfall estimates and the corresponding ground measurements provided by rain gauges. This paper investigates a new methodology for estimating radar-based rainfall fields by recalibrating at each time step the reflectivity-rainfall rate (Z-R) relationship on the basis of ground measurements provided by a rain gauge network. The power-law equation for converting reflectivity measurements into rainfall rates is readjusted at each time step, by calibrating its parameters using hourly Z-R pairs collected in the proximity of the considered time step. Calibration windows with duration between 1 and 24 h are used for estimating the parameters of the Z-R relationship. A case study pertaining to 19 rainfall events occurred in the north-western Italy is considered, in an area located within 25 km from the radar site, with available measurements of rainfall rate at the ground and radar reflectivity aloft. Results obtained with the proposed method are compared to those of three other literature methods. Applications are described for a posteriori evaluation of rainfall fields and for real-time estimation. Results suggest that the use of a calibration window of 2–5 h yields the best performances, with improvements that reach the 28% of the standard error obtained by using the most accurate fixed (climatological) Z-R relationship.

NOAA-AVHRR NDVI decomposition and subpixel classification using linear mixing in the Argentinean Pampa

Abstract Crop Normalized Difference Vegetation Index (NDVI) time profiles and crop acreage estimates were derived from the application of linear mixture modelling to Advanced Very High Resolution Radiometer (AVHRR) data over a test area in the southern part of the Pampa region, Argentina. Bands 1 and 2 from seven AVHRR scenes (June to January 1991) were combined to produce fraction images of winter crops, summer crops and pastures. A Landsat Thematic Mapper (TM) scene of the region was classified and superimposed to the AVHRR Local Area Coverage (LAC) data by means of a correlation technique. Each class signature was extracted by regressing the AVHRR response on the cover types proportions, estimated from Landsat-TM data, over sets of calibration windows. The crop NDVI profiles were hence derived from the class signatures in bands 1 and 2. These profiles appeared consistent with the cover types, but variability depending on the set of windows was noted. The assessment of the class signatures was indirectly accomplished through the subpixel classifications of the AVHRR data, performed using the different sets of class spectra. Although some discrepancies between AVHRR and Landsat–TM estimates were observed at the individual window level, the classification results compared quite well on a regional scale with Landsat–TM estimates: crop acreage was estimated to an overall accuracy ranging from 89 to 95 per cent according to the spectra used in the classification. Definitely, the proposed methodology should permit a better exploitation of the temporal resolution of AVHRR data in both the areas of yield prediction and vegetation classification. Furthermore, the perational application of such a methodology for crop monitoring will undoubtedlybe facilitated with the coming sensor systems such as the ModerateResolution Imaging Spectroradiometer (MODIS), the SPOT Vegetation Monitoring Instrument or the ‘Satelite Argentino Cientifico’ (SAC–C).