Irregular Components Research Articles

Unobserved Components Model for Forecasting Sugarcane Yield in Haryana

Unlike classical regression analysis, the state space models have time-dependent parameters and provide a flexible class of dynamic and structural time series models. The unobserved component model (UCM) is a special type of state space models widely used to analyze and forecast time series. The present investigation has been carried out to study the trend of sugarcane(gur) yield in five districts (Ambala, Karnal, Panipat, Yamunanagar and Kurukshetra) of Haryana state using the unobserved component models with level, trend and irregular components. For this purpose, the time series data on sugarcane yield from 1966-67 to 2016-17 of Ambala and Karnal, 1971-72 to 2016-17 of Kurukshetra and 1980-81 to 2016-17 of Panipat and Yamunanagar districts have been used. For all the districts, the irregular component was found to be highly significant (p=0.01) while both level and trend component variances were observed non-significant. Significance analysis of the individual component(s) has also been performed for possible dropping of the level and trend components by setting their variances equal to zero. The state space models may be effectively used pertaining to Indian agriculture data, as it takes into account the time dependency of the underlying parameters which may further enhance the predictive accuracy of the most popularly used ARIMA models with parameter constancy. Moreover, the unobserved component model is capable of handling both stationary as well as non-stationary time series and thus found more suitable for sugarcane yield modeling which is a trended yield (i.e. non-stationary in nature).

Magnetorheological finishing of an irregular-shaped small-bore complex component using a small ball-end permanent-magnet polishing head

A novel magnetorheological finishing (MRF) process using a small ball-end permanent-magnet polishing head is proposed, and a four-axes linkage dedicated MRF machine tool is fabricated to achieve the nanofinishing of an irregular ψ-shaped small-bore complex component with concave surfaces of a curvature radius less than 3 mm. The processing method of the complex component is introduced. Magnetostatic simulation during the entire finishing path is carried out to analyze the material removal characteristics. A typical ψ-shaped small-bore complex component is polished on the developed device, and a fine surface quality is obtained with surface roughness Ra of 0.0107 μm and surface accuracy of the finished spherical surfaces of 0.3320 μm (PV). These findings indicate that the proposed MRF process can perform the nanofinishing of a kind of small-bore complex component with small-curvature-radius concave surfaces.

Hide-and-Seek with time-series filters: a model-based Monte Carlo study

Time-series filters have become a major tool for univariate and multivariate analysis of business cycles. Yet, the caveats of filtering, such as distortions in spectral density often mentioned in the literature, may have substantial implications for empirical analysis. This paper focuses on two main problems: univariate and multivariate spurious inferences. While detrending the real world data, the true cyclical component is unknown, which makes it problematic to assess the efficiency of time-series filters. Using model-based Monte Carlo simulations solves this issue by introducing four different scenarios with a known trend, cyclical components and shocks. The goal of this exercise is to create realistic long-run macroeconomic time-series. To assess the performance of the five well-established time-series filters, spectral densities of the detrended fluctuations are analyzed and changes in the cross-correlation structure and deviations from the original implied fluctuations are examined. Analysis confirms and complements findings from the existing literature and provides some new insights: (i) presence of the Gibbs–Wilbraham phenomenon (for the Christiano–Fitzgerald and Baxter–King filters), yet no obvious evidence of the Slutzky–Yule phenomenon; (ii) the erroneous choice of filtering bands may lead to spurious inferences about the spectral density peaks of the detrended fluctuations; (iii) preservation of the spectral pattern of the original regular and irregular components after detrending with minor changes in the magnitude of the spectral density peaks; (iv) substantial outlier changes in the cross-correlation structure. The latter distortion may have far-reaching implications for further time-series analysis and may lead to spurious inferences about the interaction between the detrended series.

Analysis of Changes in Corneal Topography after 27-Gauge Transconjunctival Microincision Vitrectomy Combined with Cataract Surgery.

Purpose To investigate changes in the corneal shape before and after vitrectomy, over a period of time, using a 27-gauge system. Methods Forty-five eyes underwent a combination of cataract surgery and vitrectomy. The surgeries were performed using a 27-gauge transconjunctival vitrectomy system, in which the corneal topography could be performed up to three months after the surgery. The surgeries were performed for an epiretinal membrane in 11 eyes, a macular hole in 14 eyes, and rhegmatogenous retinal detachment in 20 eyes. All of the surgeries were performed by the same surgeon, and in all cases, a 4-port 27-gauge vitrectomy device was used. Cataract surgery in all patients was performed with a 2.4 mm corneoscleral incision at 11 o'clock. The surgeries were performed without suturing the operative wound in all cases. Corneal topography was performed using a TMS-4 topographer (Tomey Corporation, Tokyo, Japan). The examinations were performed the day before and 1 day, 1 week, 1 month, and 3 months after the surgery. The results of corneal topography for the spherical, regular astigmatic, asymmetric, and high-order irregular astigmatic components were compared before and after surgery. Results No significant differences were seen in any of the components in the epiretinal membrane group, but significant differences were seen in the asymmetric components and the high-order irregular astigmatic components between the macular hole and rhegmatogenous retinal detachment groups (p < 0.05). There were no significant changes in intraocular pressure on any measurement time in the postoperative period compared to preoperative intraocular pressure. Conclusion Irregular astigmatism was seen after surgery when 27-gauge vitrectomy with a 4-port system was performed together with cataract surgery with a 2.4 mm incision.

RETRACTED ARTICLE: A UCM Approach for Forecasting the Seasonal Rainfall Patterns in Coastal Andhra Pradesh, India 1901–2017

The changes in amount and pattern of seasonal rainfall have a significant impact on agriculture and water resources management in Coastal Andhra Pradesh (CAP) of India. This paper presents modeling and forecasting of the seasonal rainfall patterns in CAP using an unobserved components model (UCM) with the hidden components trend, seasonal, cyclical and irregular. The seasonal rainfall data for CAP were provided by the India Meteorological Department and the analysis for the four rainfall seasons, namely, winter, pre-monsoon, monsoon and post-monsoon. The UCM with deterministic level, deterministic trigonometric seasonal, deterministic cycle and stochastic irregular components is selected from the parsimonious models for forecasting the seasonal rainfall patterns in CAP based on the Bayesian information criterion (BIC), significance tests and statistical fit. The model parameters are obtained by using the maximum likelihood method, and the validity of the selected UCM is determined using normal correlation diagnostics and LJung–Box test statistics for residuals. The forecasting of seasonal rainfall patterns for the years 2018–2020 was carried out with the help of the selected UCM. Further, the UCM forecast reveals that winter rainfall will be around 36.4 mm in 2018, 11.3 mm in 2019 and 54.4 mm in 2020; pre-monsoon rainfall will be 91.3 mm in 2018, 135.1 mm in 2019 and 106.4 mm in 2020; monsoon rainfall will be 686.4 mm in 2018, 663.9 mm in 2019 and 652.9 mm in 2020; post-monsoon rainfall will be 286.2 mm in 2018, 267.9 mm in 2019 and 309.1 mm in 2020.

Design of irregular QC-LDPC code based multi-level coded modulation scheme for high speed optical communication systems

In this paper, we focus on the design of irregular QC-LDPC code based multi-level coded modulation (MLCM) scheme by jointly optimizing the component code rate and the degree distribution of the irregular QC-LDPC component code. Firstly, the sub-channel capacities of MLCM systems is analyzed and discussed, based on which the optimal component code rate can be obtained. Secondly, an extrinsic information transfer chart based two-stage searching algorithm is proposed to find the good irregular QC-LDPC code ensembles with optimal component code rates for their corresponding sub-channels. Finally, by constructing the irregular QC-LDPC component codes from the designed ensembles with the aim of possibly enlarging the girth and reducing the number of the shortest cycles, the designed irregular QC-LDPC code based 16QAM and 64QAM MLCM systems can achieve 0.4dB and 1.2dB net coding gain, respectively, compared with the recently proposed regular QC-LDPC code based 16QAM and 64QAM MLCM systems.

ABOUT FANOUTS PLACEMENT IN BGA COMPONENT AREA

During the routing in the BGA area, two related problems need to be solved: the first one is the arrangement of the two‑ports under the BGA component in such a way that the maximum number of vias (through) can be accommodated, the second is the placement of vias and the assignment of vias to each BGA contact (or group of equipotential contacts). The approach to solving the first problem was previously proposed by the authors. The second problem was considered in the literature as applied to irregular BGA components, but the proposed method needs clarification in terms of using information on the arrangement of equipotential contacts and the assignment of vias to them. The proposed approach does not guarantee the optimal solution, because along with precise algorithms (for matching problem and assignments) it uses a number of heuristics, however it allows to automate the process of arranging the fanouts and, therefore, significantly reduce the complexity of the routing.

Study on Icing Prediction of Power Transmission Lines Based on Ensemble Empirical Mode Decomposition and Feature Selection Optimized Extreme Learning Machine

The ice coating on the transmission line is extremely destructive to the safe operation of the power grid. Under natural conditions, the thickness of ice coating on the transmission line shows a nonlinear growth trend and many influencing factors increase the difficulty of forecasting. Therefore, a hybrid model was proposed in this paper, which mixed Ensemble Empirical Mode Decomposition (EEMD), Random Forest (RF) and Chaotic Grey Wolf Optimization-Extreme Learning Machine (CGWO-ELM) algorithms to predict short-term ice thickness. Firstly, the Ensemble Profit Mode Decomposition model was introduced to decompose the original ice thickness data into components representing different wave characteristics and to eliminate irregular components. In order to verify the accuracy of the model, two transmission lines in ‘hunan’ province were selected for case study. Then the reserved components were modeled one by one, building the random forest feature selection algorithm and Partial Autocorrelation Function (PACF) to extract the feature input of the model. At last, a component prediction model of ice thickness based on feature selection and CGWO-ELM was established for prediction. Simulation results show that the model proposed in this paper not only has good prediction performance, but also can greatly improve the accuracy of ice thickness prediction by selecting input terminal according to RF characteristics.

Sources of the seasonal-trend behaviour and periodicity modulation of 7Be air concentration in the atmospheric surface layer observed in southeastern Spain

The components and the periodicity characterising airborne 7Be monthly concentration collected in Granada (SE Spain) between 2000 and 2018 are studied. These 19 years of measurements are particularly significant for the 7Be analysis as they cover the complete 24th solar cycle, 2008–2018.A Time Series Decomposition (TSD) technique has been applied to determine the different components present in the dataset: the trend, seasonal and irregular components. Then, the atmospheric parameters (Precipitation (Pp), Relative Humidity (RH), Temperature (Temp), Wind speed (WS), Total Cloudiness (TC), Low Cloudiness (LC)), teleconnection indices and the solar activity (Sun Spot Number, SSN) were used to justify the variability of each component.SSN and the teleconnection indices (Western Mediterranean Oscillation (WeMO), Atlantic Multidecadal Oscillation (AMO) and Quasi Biennial Oscillation (QBO)) are found to have a major impact on the trend component of 7Be. In turn, all the atmospheric parameters were found to have strong impact on the seasonal component.In order to observe the change in the impact of the parameters from the 24th solar cycle (from 2008 to 2018) to the complete time series (starting in 2000), the comparison between the two periods has been carried out. The results show that, over the 24th solar cycle, the SSN impacted predominantly on the trend component, whereas atmospheric parameters showed a slightly higher impact on the seasonal component. In addition, some atmospheric factors (Temp, RH, Pp, TC, LC) appeared to partially affect the irregular component.The analysis of the two phases of the 24th solar cycle shows that the influence of SSN is higher during the descending phase than during the ascending one. This behaviour is opposed to the one found for the 23rd solar cycle, which clarifies the special characteristics of the 24th cycle.Besides that, the Continuous Wavelet Transform (CWT) analysis was applied in order to extract the possible periodicities characterising 7Be data. The analysis revealed three sets of periodicities. Then, the Wavelet Coherence Analysis (WCA) method was particularly useful to study coherences between 7Be data and teleconnection indices. The 7Be concentrations in the large period (11–14 years) detected by CWT was found to be mainly modulated by WeMO, AMO and QBO, while the NAO modulates the smaller periods.

On forcings of length of day changes: From 9-day to 18.6-year oscillations

We analyze fluctuations in Earth's rotation velocity (therefore also the equivalent length of day), using >50 years of IERS observations and the powerful method of Singular Spectral Analysis. The first 16 eigenvalues uncovered by SSA correspond to 10 components, all with physical sense. The first component is the trend, the second is the lunar node tide (18.6 yr, amplitude 1.3 ms). Next are variations with a period that implies forcing related to solar activity (11 years, 0.46 ms amplitude). Then, zonal oscillations linked to the solar (1 year, 0.81 ms; 0.5 year, 0.76 ms;) and lunar (27.54 days, 0.39 ms; 13.66 days, 0.73 ms; 13.63 days, 0.27 ms; 9.13 days, 0.14 ms) tidal potentials. The QBO at 2.36 years (0.08 ms) is interpreted as a Sun-related oscillation. The components at 13.63 and 13.66 days could contain a solar contribution. SSA is an efficient detrending algorithm and way to identify irregular (quasi-periodical) oscillatory components: its application to l.o.d. data yields refined observations, in good agreement with recent models but with some new results. There is no extracted component that could not be attributed to an existing periodic or quasi-periodic physical phenomenon. Progress in computers and signal processing has allowed us to resume and extend the analysis of l.o.d., and in particular to show evidence of a solar signature in the series. Uncovering the mechanisms through which solar activity acts on Earth's rotation is an exciting project for geophysicists that deserve renewed attention.

Score-driven models of stochastic seasonality in location and scale: an application case study of the Indian rupee to USD exchange rate

ABSTRACTWe estimate the stochastic seasonality of the Indian rupee (INR) to United States dollar (USD) exchange rate by using new dynamic conditional score (DCS) specifications. We use the DCS-Skew-Gen- (DCS-skewed generalized t distribution) and DCS-NIG (DCS-normal-inverse Gaussian distribution) models, which are alternatives to the DCS- (DCS-Student’s -distribution) and DCS-EGB2 (DCS-exponential generalized beta distribution of the second kind) models from the literature. DCS models are robust to outliers, and such models effectively disentangle the local level, seasonality and irregular components. For the latter, we apply DCS-EGARCH (DCS-exponential generalized autoregressive conditional heteroscedasticity) scale dynamics, and we use new DCS models with seasonal volatility. We use INR/USD data for the period of 1 January 1982 to 7 July 2017. We find that the DCS-Skew-Gen- and DCS-NIG models are superior to the DCS- and DCS-EGB2 models, respectively. The amplitude of the INR/USD seasonality is relatively high during the last decade of the sample. We explain this by using the currency movements that are related to increased seasonal exports and imports of India. We show the robustness of our results for different exchange rate regimes: (i) pegged exchange rate regime period (until February 1993); (ii) liberalized exchange rate management system period (since March 1993).

A wavelet-based hybrid neural network for short-term electricity prices forecasting

Forecasting is a very important and difficult task for various economic activities. Despite the great evolution of time series modeling, forecasters are still in the hunt for better strategies to improve mathematical models and come up with more accurate predictions. In this respect, several new models, mixing autoregressive processes to artificial neural networks (ANNs), have recently emerged. This is particularly the case for energy economics, where old forecasting tools are replaced by new hybrid strategies. Along the same lines, this paper aims to define a wavelet-based hybridization, involving nonlinear smooth functions, autoregressive fractionally integrated moving average (ARFIMA) model and feedforward ANNs, for electricity spot prices forecasting. The use of the wavelet decomposition in this model allows to characterize certain patterns of power time series, such as the nonlinear trend and multiple seasonal effects, and to exactly extrapolate them over the time scale. In fact, such patterns have already been pointed out as potential causes of the ANN’s inaccuracy. An ARFIMA–ANN model is then used to forecast the resulting irregular component. In the last stage, the smooth and irregular components are recombined to constitute the forecasted price. We will demonstrate the cost-effectiveness of the proposed method using hourly power prices from the Nord Pool Exchange. The testing time series consists of 52,614 observations and corresponds to the period ranging from 2012 to 2017. The results show that the new method is able to provide better interval forecasting than four benchmark models.

A Structural‐Factor Approach to Modeling High‐Dimensional Time Series and Space‐Time Data

This article considers a structural‐factor approach to modeling high‐dimensional time series and space‐time data by decomposing individual series into trend, seasonal, and irregular components. For ease in analyzing many time series, we employ a time polynomial for the trend, a linear combination of trigonometric series for the seasonal component, and a new factor model for the irregular components. The new factor model simplifies the modeling process and achieves parsimony in parameterization. We propose a Bayesian information criterion to consistently select the order of the polynomial trend and the number of trigonometric functions, and use a test statistic to determine the number of common factors. The convergence rates for the estimators of the trend and seasonal components and the limiting distribution of the test statistic are established under the setting that the number of time series tends to infinity with the sample size, but at a slower rate. We study the finite‐sample performance of the proposed analysis via simulation, and analyze two real examples. The first example considers modeling weekly PM2.5 data of 15 monitoring stations in the southern region of Taiwan and the second example consists of monthly value‐weighted returns of 12 industrial portfolios.

A digital image processing model for characteristics capture and analysis of irregular electronic components

Amid diversification, high performance, high integration, and low-cost development in consumer electronic products, the conventional system on board function integration model is giving way to system on chip (SoC). Layout of solder balls under ball grid array (BGA) is getting irregular as system in package (SiP) is integrating multi-chip module and couple of active and passive components along with more complex circuit design. Due to the shortening life cycles of consumer electronic products and frequent new product launches, the incoming inspection operation of electronic components is getting tedious, let alone the component database updates required by mounter are also getting more frequent and time consuming due to increasingly complex and diversified components and soaring demands in characteristic measurement and identification difficulties. This study is aimed at building automated characteristic capture and analysis image processing model for BGA packages of irregular solder ball layout. It employs edge detection to get component body looks and dimensions by enhanced component edge characteristics. A image pre-processing model is used to identify consistency of individual solder ball characteristics with median filter, highlight image characteristics with binary conversion, remove noise with morphology, and connected component labeling to segment area covered by individual characteristic. In addition, a roundness and aspect ratio of the least rectangle-based solder ball characteristics identification method is proposed to determine number of, diameter of, and coordinates of center of border solder balls. The component characteristics acquired by the model comes in ± 3% difference against the actual one in terms of dimension. The model appears a good reference for system packaging operator in component database creation for the launch of new products in terms of reduced time for manual measurement and error as well as loss in mounter capacity. It also serves for electronic components incoming inspection to reduce inspection operation time and simplify the process.

Estimation of irregular obstructed vertical sky components under various CIE skies

Abstract Solar radiation and daylight illuminance data on vertical surfaces are crucial for renewable energy utilization and daylighting designs. The prediction of solar irradiance or illuminance on vertical surfaces is more complex than horizontal surfaces under densely-built urban environments. Previously, we proposed a mathematical model to estimate the obstructed vertical sky component (OVSC) under various obstructed skies and the obstructed conditions of infinite-long urban canyons. However, the obstructions may be always irregular in both horizontal and vertical surfaces in real cases. This paper proposes an approach to determine the vertical sky-diffuse component under irregular skyline patterns. Totally, 63 different configurations of the irregular obstructions were identified and the OVSCs were calculated using RADIANCE. The simulated results were used to correlate with those of infinite-long urban canyons. Strong correlations with high determination coefficients were obtained. The findings indicate that the proposed methods are useful for building energy efficient and daylighting designs.

НЕРЕГЛАМЕНТОВАНІ ЕЛЕМЕНТИ АВСТРО-УГОРСЬКОГО ВІЙСЬКОВОГО ОДНОСТРОЮ ПІД ЧАС ПЕРШОЇ СВІТОВОЇ ВІЙНИ (1914–1918)

The article is devoted to the irregular components of the Austro-Hungarian military uniform in the First World War. At that time, the set of such elements was a certain peculiarity of the uniformed soldiers of the Armed Forces of Austria-Hungary. The main demonstrations of this phenomenon, the origins and the development of relevant uniform practices are considered in the article. The importance of military uniformology studies in militaristic anthropology is emphasized.

Image Denoising via Nonlocal Low Rank Approximation With Local Structure Preserving

The nuclear norm minimization method emerged from a patch-based low-rank model leads to an excellent image denoising performance, where the non-local self-similarity over image patches is exploited. However, natural images are normally with complex and irregular image patches, which cannot be well represented using only a low-rank model, and thus most of them suffer from the over-penalty problem especially for images with lots of local irregular structures (e.g., fine details or sharp edges), and then results in over-smoothing problem after denoising. On the other hand, in order to represent the irregular components, edges defined over pixel level are often exploited. While the total variation (TV) is a well-known prior to remove noises and preserve edges, it might yield undesired staircase artifacts. The total generalized variation (TGV), a generalization of TV, can largely alleviate such staircase artifacts. Consequently, in order to deal with the over-smoothing problem aroused by a low-rank model, we propose a re-weighted TGV regularized nuclear norm minimization model for local structure preserving image denoising. Thanks to the split Bregman method, our proposed model can be effectively solved. A re-weighted strategy is developed to adaptively update the weight parameters of TGV regularization. The encouraging experimental results on noisy images demonstrate the effectiveness of our proposed method.

An Empirical Study of Error Evaluation in Trend and Seasonal Time Series Forecasting Based on SSA

SSA (Singular Spectrum Analysis) starts to become a popular method in decomposing time series into some separable and interpretable series. This study provides an error evaluation in the SSA-based model for trend and multiple seasonal time series forecasting. This error evaluation is obtained by means of a numerical study on the mean square error of the estimators and mean absolute percentage error of the forecast values. Four distinct types of data generating processes (DGP) with varying sample sizes are considered in this experimental study. The parameters are estimated from the component series of SSA. Each DGP is decomposed into trend, periodic and irregular components. All these components except the irregular one are fitted by appropriate deterministic function separately. Based on the numerical simulation results, the estimated parameters are closer to the true values as the sample size increases. As the illustrative example of the real data set implementation, we used the monthly atmospheric concentrations of CO2 from Moana Loa observatory for period January 1959 to June 1972. The proposed method produces better forecast values than the results of SSA-LRF (Linear Recurrent Formula) and TLSAR (Two Level Seasonal Autoregressive). The results encourage the improvement in the time series modeling on the more complex pattern.

Score-driven currency exchange rate seasonality as applied to the Guatemalan Quetzal/US Dollar

In this paper we introduce new Dynamic Conditional Score (DCS) models for the Skew-Gen-t (Skewed Generalized t) and NIG (Normal-Inverse Gaussian) distributions as alternatives to the recent DCS models for the Student’s-t and EGB2 (Exponential Generalized Beta of the second kind) distributions, respectively. The DCS models we propose include stochastic local level, stochastic seasonality, and irregular components with DCS-EGARCH (Exponential Generalized Autoregressive Conditional Heteroscedasticity) volatility dynamics. DCS models are robust to extreme observations, whereas standard financial time series models are not. We use data from the Guatemalan Quetzal (GTQ) to United States Dollar (USD) exchange rate for the period of 4th January 1994–30th June 2017. This dataset exhibits significant rises and falls in the GTQ/USD that lead to extreme observations, stochastic seasonality with dynamic amplitude, and volatility dynamics. These seasonality dynamics of the GTQ/USD are related to the Guatemalan trade-related currency movements, receipt and payment of foreign loans, and remittance payments of Guatemalans working abroad. We show that the in-sample statistical performance of the DCS-Skew-Gen-t and the DCS-NIG models is superior to that of the DCS-t and the DCS-EGB2 models, respectively. Furthermore, we show that the statistical performance of all DCS models is superior to that of the standard financial time series model.

Seasonal patterns of tuberculosis case notification in the tropics of Africa: A six-year trend analysis in Ethiopia.

ObjectiveSeasonal variations affect the health system’s functioning, including tuberculosis (TB) services, but there is little evidence about seasonal variations in TB case notification in tropical countries, including Ethiopia. This study sought to fill this gap in knowledge using TB data reported from 10 zones, 5 each from Amhara and Oromia regions.MethodsNotified TB cases for 2010–2016 were analyzed using SPSS version 20. We calculated the quarterly and annual average TB case notification rates and the proportion of seasonal amplitudes. We applied Winters’ multiplicative method of exponential smoothing to break down the original time series into seasonal, trend, and irregular components and to build a suitable model for forecasting.ResultsA total of 205,575 TB cases were identified (47.8% from Amhara, 52.2% from Oromia), with a male-to-female ratio of 1.2:1. The means of 8,200 (24%), 7,992 (23%), 8,849 (26%), and 9,222 (27%) TB cases were reported during July-September, October-December, January-March, and April-June, respectively. The seasonal component of our model indicated a peak in April-June and a trough in October-December. The seasonal amplitude in Amhara region is 10% greater than that of Oromia (p < 0.05).ConclusionsTB is shown to be a seasonal disease in Ethiopia, with a peak in quarter four and a low in quarter two of the fiscal year. The peak TB case notification rate corresponds with the end of the dry season in the two agrarian regions of Ethiopia. TB prevention and control interventions, such as efforts to increase community TB awareness about TB transmission and contact tracing, should consider seasonal variation. Regional variations in TB seasonality may require consideration of geographic-specific TB case-finding strategies. The mechanisms underlying the seasonal variation of TB are complex, and further study is needed.