Готові списки джерел за темами / Forecasting of data in the form of time series

Добірка наукової літератури з теми "Forecasting of data in the form of time series"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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Статті в журналах з теми "Forecasting of data in the form of time series"

Angers, Jean-François, Atanu Biswas, and Raju Maiti. "Bayesian Forecasting for Time Series of Categorical Data." Journal of Forecasting 36, no. 3 (May 9, 2016): 217–29. http://dx.doi.org/10.1002/for.2426.

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Kasyoki, Alexander. "Simple Steps for Fitting Arima Model to Time Series Data for Forecasting Using R." International Journal of Science and Research (IJSR) 4, no. 3 (April 5, 2015): 318–21. http://dx.doi.org/10.21275/sub151897.

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Hermansah, Hermansah, Dedi Rosadi, Abdurakhman Abdurakhman, and Herni Utami. "SELECTION OF INPUT VARIABLES OF NONLINEAR AUTOREGRESSIVE NEURAL NETWORK MODEL FOR TIME SERIES DATA FORECASTING." MEDIA STATISTIKA 13, no. 2 (December 28, 2020): 116–24. http://dx.doi.org/10.14710/medstat.13.2.116-124.

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NARNN is a type of ANN model consisting of a limited number of parameters and widely used for various applications. This study aims to determine the appropriate NARNN model, for the selection of input variables of nonlinear autoregressive neural network model for time series data forecasting, using the stepwise method. Furthermore, the study determines the optimal number of neurons in the hidden layer, using a trial and error method for some architecture. The NARNN model is combined in three parts, namely the learning method, the activation function, and the ensemble operator, to get the best single model. Its application in this study was conducted on real data, such as the interest rate of Bank Indonesia. The comparison results of MASE, RMSE, and MAPE values with ARIMA and Exponential Smoothing models shows that the NARNN is the best model used to effectively improve forecasting accuracy.

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Phong, Pham Đinh. "A TIME SERIES FORECASTING MODEL BASED ON LINGUISTIC FORECASTING RULES." Journal of Computer Science and Cybernetics 37, no. 1 (March 29, 2021): 23–42. http://dx.doi.org/10.15625/1813-9663/37/1/15852.

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The fuzzy time series (FTS) forecasting models have been being studied intensively over the past few years. Most of the researches focus on improving the effectiveness of the FTS forecasting models using time-invariant fuzzy logical relationship groups proposed by Chen et al. In contrast to Chen’s model, a fuzzy set can be repeated in the right-hand side of the fuzzy logical relationship groups of Yu’s model. N. C. Dieu enhanced Yu’s forecasting model by using the time-variant fuzzy logical relationship groups instead of the time-invariant ones. The forecasting models mentioned above partition the historical data into subintervals and assign the fuzzy sets to them by the human expert’s experience. N. D. Hieu et al. proposed a linguistic time series by utilizing the hedge algebras quantification to converse the numerical time series data to the linguistic time series. Similar to the FTS forecasting model, the obtained linguistic time series can define the linguistic, logical relationships which are used to establish the linguistic, logical relationship groups and form a linguistic forecasting model. In this paper, we propose a linguistic time series forecasting model based on the linguistic forecasting rules induced from the linguistic, logical relationships instead of the linguistic, logical relationship groups proposed by N. D. Hieu. The experimental studies using the historical data of the enrollments of University of Alabama observed from 1971 to 1992 and the daily average temperature data observed from June 1996 to September 1996 in Taipei show the outperformance of the proposed forecasting models over the counterpart ones.

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Orzeszko, Witold. "Several Aspects of Nonparametric Prediction of Nonlinear Time Series." Przegląd Statystyczny 65, no. 1 (January 30, 2019): 7–24. http://dx.doi.org/10.5604/01.3001.0014.0522.

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Nonparametric regression is an alternative to the parametric approach, which consists of applying parametric models, i.e. models of the certain functional form with a fixed number of parameters. As opposed to the parametric approach, nonparametric models have a general form, which can be approximated increasingly precisely when the sample size grows. Hereby they do not impose such restricted assumptions about the form of the modelling dependencies and in consequence, they are more flexible and let the data speak for themselves. That is why they are a promising tool for forecasting, especially in case of nonlinear time series. One of the most popular nonparametric regression method is the Nadaraya- Watson kernel smoothing. Nowadays, there are a number of variations of this method, like the local-linear kernel estimator, which combines the local linear approximation and the kernel estimator. In the paper a Monte Carlo study is conducted in order to assess the usefulness of the kernel smoothers to nonlinear time series forecasting and to compare them with the other techniques of forecasting.

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Albara, Al-Khowarizmi, and Riyan Pradesyah. "Power Business Intelligence in the Data Science Visualization Process to Forecast CPO Prices." International Journal of Science, Technology & Management 2, no. 6 (November 20, 2021): 2198–208. http://dx.doi.org/10.46729/ijstm.v2i6.403.

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Forecasting is one of the techniques in data mining by utilizing the data available in the data warehouse. With the development of science, forecasting techniques have also entered the computational field where the forecasting technique uses the artificial neural network (ANN) method. Where is the method for simple forecasting using the Time Series method. However, the ability to create data visualizations certainly hinders researchers from maximizing research results. Of course, with the development of the Power BI software, the data science process is more neatly presented in the form of visualization, where the data science process involves various fields so that in this paper the results of forecasting the price of crude palm oil (CPO) are presented for the development of the CPO business with the hope of implementing the Business Process. intelligence (BI) by involving ANN, namely the time series for forecasting. From the final results, accuracy in forecasting with time series involves 2 accuracy techniques, the first using MAPE and getting a result of 0.03214% and the second using MSE to get 962.91 results.

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Belas, Andrii, and Petro Bidyuk. "Convolutional neural networks for modeling and forecasting nonlinear nonstationary processes." ScienceRise, no. 3 (June 30, 2021): 12–20. http://dx.doi.org/10.21303/2313-8416.2021.001924.

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The object of research. The object of research is modeling and forecasting nonlinear nonstationary processes presented in the form of time-series data. Investigated problem. There are several popular approaches to solving the problems of adequate model constructing and forecasting nonlinear nonstationary processes, such as autoregressive models and recurrent neural networks. However, each of them has its advantages and drawbacks. Autoregressive models cannot deal with the nonlinear or combined influence of previous states or external factors. Recurrent neural networks are computationally expensive and cannot work with sequences of high length or frequency. The main scientific result. The model for forecasting nonlinear nonstationary processes presented in the form of the time series data was built using convolutional neural networks. The current study shows results in which convolutional networks are superior to recurrent ones in terms of both accuracy and complexity. It was possible to build a more accurate model with a much fewer number of parameters. It indicates that one-dimensional convolutional neural networks can be a quite reasonable choice for solving time series forecasting problems. The area of practical use of the research results. Forecasting dynamics of processes in economy, finances, ecology, healthcare, technical systems and other areas exhibiting the types of nonlinear nonstationary processes. Innovative technological product. Methodology of using convolutional neural networks for modeling and forecasting nonlinear nonstationary processes presented in the form of time-series data. Scope of the innovative technological product. Nonlinear nonstationary processes presented in the form of time-series data.

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JOSHI, BHAGAWATI P., and SANJAY KUMAR. "A COMPUTATIONAL METHOD FOR FUZZY TIME SERIES FORECASTING BASED ON DIFFERENCE PARAMETERS." International Journal of Modeling, Simulation, and Scientific Computing 04, no. 01 (December 27, 2012): 1250023. http://dx.doi.org/10.1142/s1793962312500237.

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Present study proposes a method for fuzzy time series forecasting based on difference parameters. The developed method has been presented in a form of simple computational algorithm. It utilizes various difference parameters being implemented on current state for forecasting the next state values to accommodate the possible vagueness in the data in an efficient way. The developed model has been simulated on the historical student enrollments data of University of Alabama and the obtained forecasted values have been compared with the existing methods to show its superiority. Further, the developed model has also been implemented in forecasting the movement of market prices of share of State Bank of India (SBI) at Bombay Stock Exchange (BSE), India.

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Ramli, Nazirah, Siti Musleha Ab Mutalib, and Daud Mohamad. "Fuzzy Time Series Forecasting Model based on Frequency Density and Similarity Measure Approach." International Journal of Engineering & Technology 7, no. 4.30 (November 30, 2018): 281. http://dx.doi.org/10.14419/ijet.v7i4.30.22284.

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This paper proposes an enhanced fuzzy time series (FTS) prediction model that can keep some information under a various level of confidence throughout the forecasting procedure. The forecasting accuracy is developed based on the similarity between the fuzzified historical data and the fuzzy forecast values. No defuzzification process involves in the proposed method. The frequency density method is used to partition the interval, and the area and height type of similarity measure is utilized to get the forecasting accuracy. The proposed model is applied in a numerical example of the unemployment rate in Malaysia. The results show that on average 96.9% of the forecast values are similar to the historical data. The forecasting error based on the distance of the similarity measure is 0.031. The forecasting accuracy can be obtained directly from the forecast values of trapezoidal fuzzy numbers form without experiencing the defuzzification procedure.

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Murat, Małgorzata, Iwona Malinowska, Magdalena Gos, and Jaromir Krzyszczak. "Forecasting daily meteorological time series using ARIMA and regression models." International Agrophysics 32, no. 2 (April 1, 2018): 253–64. http://dx.doi.org/10.1515/intag-2017-0007.

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Abstract The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.

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Більше джерел

Дисертації з теми "Forecasting of data in the form of time series"

Li, Jing. "Clustering and forecasting for rain attenuation time series data." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-219615.

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Clustering is one of unsupervised learning algorithm to group similar objects into the same cluster and the objects in the same cluster are more similar to each other than those in the other clusters. Forecasting is making prediction based on the past data and efficient artificial intelligence models to predict data developing tendency, which can help to make appropriate decisions ahead. The datasets used in this thesis are the signal attenuation time series data from the microwave networks. Microwave networks are communication systems to transmit information between two fixed locations on the earth. They can support increasing capacity demands of mobile networks and play an important role in next generation wireless communication technology. But inherent vulnerability to random fluctuation such as rainfall will cause significant network performance degradation. In this thesis, K-means, Fuzzy c-means and 2-state Hidden Markov Model are used to develop one step and two step rain attenuation data clustering models. The forecasting models are designed based on k-nearest neighbor method and implemented with linear regression to predict the real-time rain attenuation in order to help microwave transport networks mitigate rain impact, make proper decisions ahead of time and improve the general performance.
Clustering is een van de unsupervised learning algorithmen om groep soortgelijke objecten in dezelfde cluster en de objecten in dezelfde cluster zijn meer vergelijkbaar met elkaar dan die in de andere clusters. Prognoser är att göra förutspårningar baserade på övergående data och effektiva artificiella intelligensmodeller för att förutspå datautveckling, som kan hjälpa till att fatta lämpliga beslut. Dataseten som används i denna avhandling är signaldämpningstidsseriedata från mikrovågsnätverket. Mikrovågsnät är kommunikationssystem för att överföra information mellan två fasta platser på jorden. De kan stödja ökade kapacitetsbehov i mobilnät och spela en viktig roll i nästa generationens trådlösa kommunikationsteknik. Men inneboende sårbarhet för slumpmässig fluktuering som nedbörd kommer att orsaka betydande nätverksförstöring. I den här avhandlingen används K-medel, Fuzzy c-medel och 2-state Hidden Markov Model för att utveckla ett steg och tvåstegs regen dämpning dataklyvningsmodeller. Prognosmodellerna är utformade utifrån k-närmaste granne-metoden och implementeras med linjär regression för att förutsäga realtidsdämpning för att hjälpa mikrovågstransportnät att mildra regnpåverkan, göra rätt beslut före tid och förbättra den allmänna prestandan.

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Khadivi, Pejman. "Online Denoising Solutions for Forecasting Applications." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/72907.

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Dealing with noisy time series is a crucial task in many data-driven real-time applications. Due to the inaccuracies in data acquisition, time series suffer from noise and instability which leads to inaccurate forecasting results. Therefore, in order to improve the performance of time series forecasting, an important pre-processing step is the denoising of data before performing any action. In this research, we will propose various approaches to tackle the noisy time series in forecasting applications. For this purpose, we use different machine learning methods and information theoretical approaches to develop online denoising algorithms. In this dissertation, we propose four categories of time series denoising methods that can be used in different situations, depending on the noise and time series properties. In the first category, a seasonal regression technique is proposed for the denoising of time series with seasonal behavior. In the second category, multiple discrete universal denoisers are developed that can be used for the online denoising of discrete value time series. In the third category, we develop a noisy channel reversal model based on the similarities between time series forecasting and data communication and use that model to deploy an out-of-band noise filtering in forecasting applications. The last category of the proposed methods is deep-learning based denoisers. We use information theoretic concepts to analyze a general feed-forward deep neural network and to prove theoretical bounds for deep neural networks behavior. Furthermore, we propose a denoising deep neural network method for the online denoising of time series. Real-world and synthetic time series are used for numerical experiments and performance evaluations. Experimental results show that the proposed methods can efficiently denoise the time series and improve their quality.
Ph. D.

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Li, Yuntao. "Federated Learning for Time Series Forecasting Using Hybrid Model." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254677.

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Time Series data has become ubiquitous thanks to affordable edge devices and sensors. Much of this data is valuable for decision making. In order to use these data for the forecasting task, the conventional centralized approach has shown deficiencies regarding large data communication and data privacy issues. Furthermore, Neural Network models cannot make use of the extra information from the time series, thus they usually fail to provide time series specific results. Both issues expose a challenge to large-scale Time Series Forecasting with Neural Network models. All these limitations lead to our research question:Can we realize decentralized time series forecasting with a Federated Learning mechanism that is comparable to the conventional centralized setup in forecasting performance?In this work, we propose a Federated Series Forecasting framework, resolving the challenge by allowing users to keep the data locally, and learns a shared model by aggregating locally computed updates. Besides, we design a hybrid model to enable Neural Network models utilizing the extra information from the time series to achieve a time series specific learning. In particular, the proposed hybrid outperforms state-of-art baseline data-central models with NN5 and Ericsson KPI data. Meanwhile, the federated settings of purposed model yields comparable results to data-central settings on both NN5 and Ericsson KPI data. These results together answer the research question of this thesis.
Tidseriedata har blivit allmänt förekommande tack vare överkomliga kantenheter och sensorer. Mycket av denna data är värdefull för beslutsfattande. För att kunna använda datan för prognosuppgifter har den konventionella centraliserade metoden visat brister avseende storskalig datakommunikation och integritetsfrågor. Vidare har neurala nätverksmodeller inte klarat av att utnyttja den extra informationen från tidsserierna, vilket leder till misslyckanden med att ge specifikt tidsserierelaterade resultat. Båda frågorna exponerar en utmaning för storskalig tidsserieprognostisering med neurala nätverksmodeller. Alla dessa begränsningar leder till vår forskningsfråga:Kan vi realisera decentraliserad tidsserieprognostisering med en federerad lärningsmekanism som presterar jämförbart med konventionella centrala lösningar i prognostisering?I det här arbetet föreslår vi ett ramverk för federerad tidsserieprognos som löser utmaningen genom att låta användaren behålla data lokalt och lära sig en delad modell genom att aggregera lokalt beräknade uppdateringar. Dessutom utformar vi en hybrid modell för att möjliggöra neurala nätverksmodeller som kan utnyttja den extra informationen från tidsserierna för att uppnå inlärning av specifika tidsserier. Den föreslagna hybrida modellen presterar bättre än state-of-art centraliserade grundläggande modeller med NN5och Ericsson KPIdata. Samtidigt ger den federerade ansatsen jämförbara resultat med de datacentrala ansatserna för både NN5och Ericsson KPI-data. Dessa resultat svarar tillsammans på forskningsfrågan av denna avhandling.

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Ben, Taieb Souhaib. "Machine learning strategies for multi-step-ahead time series forecasting." Doctoral thesis, Universite Libre de Bruxelles, 2014. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209234.

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How much electricity is going to be consumed for the next 24 hours? What will be the temperature for the next three days? What will be the number of sales of a certain product for the next few months? Answering these questions often requires forecasting several future observations from a given sequence of historical observations, called a time series.

Historically, time series forecasting has been mainly studied in econometrics and statistics. In the last two decades, machine learning, a field that is concerned with the development of algorithms that can automatically learn from data, has become one of the most active areas of predictive modeling research. This success is largely due to the superior performance of machine learning prediction algorithms in many different applications as diverse as natural language processing, speech recognition and spam detection. However, there has been very little research at the intersection of time series forecasting and machine learning.

The goal of this dissertation is to narrow this gap by addressing the problem of multi-step-ahead time series forecasting from the perspective of machine learning. To that end, we propose a series of forecasting strategies based on machine learning algorithms.

Multi-step-ahead forecasts can be produced recursively by iterating a one-step-ahead model, or directly using a specific model for each horizon. As a first contribution, we conduct an in-depth study to compare recursive and direct forecasts generated with different learning algorithms for different data generating processes. More precisely, we decompose the multi-step mean squared forecast errors into the bias and variance components, and analyze their behavior over the forecast horizon for different time series lengths. The results and observations made in this study then guide us for the development of new forecasting strategies.

In particular, we find that choosing between recursive and direct forecasts is not an easy task since it involves a trade-off between bias and estimation variance that depends on many interacting factors, including the learning model, the underlying data generating process, the time series length and the forecast horizon. As a second contribution, we develop multi-stage forecasting strategies that do not treat the recursive and direct strategies as competitors, but seek to combine their best properties. More precisely, the multi-stage strategies generate recursive linear forecasts, and then adjust these forecasts by modeling the multi-step forecast residuals with direct nonlinear models at each horizon, called rectification models. We propose a first multi-stage strategy, that we called the rectify strategy, which estimates the rectification models using the nearest neighbors model. However, because recursive linear forecasts often need small adjustments with real-world time series, we also consider a second multi-stage strategy, called the boost strategy, that estimates the rectification models using gradient boosting algorithms that use so-called weak learners.

Generating multi-step forecasts using a different model at each horizon provides a large modeling flexibility. However, selecting these models independently can lead to irregularities in the forecasts that can contribute to increase the forecast variance. The problem is exacerbated with nonlinear machine learning models estimated from short time series. To address this issue, and as a third contribution, we introduce and analyze multi-horizon forecasting strategies that exploit the information contained in other horizons when learning the model for each horizon. In particular, to select the lag order and the hyperparameters of each model, multi-horizon strategies minimize forecast errors over multiple horizons rather than just the horizon of interest.

We compare all the proposed strategies with both the recursive and direct strategies. We first apply a bias and variance study, then we evaluate the different strategies using real-world time series from two past forecasting competitions. For the rectify strategy, in addition to avoiding the choice between recursive and direct forecasts, the results demonstrate that it has better, or at least has close performance to, the best of the recursive and direct forecasts in different settings. For the multi-horizon strategies, the results emphasize the decrease in variance compared to single-horizon strategies, especially with linear or weakly nonlinear data generating processes. Overall, we found that the accuracy of multi-step-ahead forecasts based on machine learning algorithms can be significantly improved if an appropriate forecasting strategy is used to select the model parameters and to generate the forecasts.

Lastly, as a fourth contribution, we have participated in the Load Forecasting track of the Global Energy Forecasting Competition 2012. The competition involved a hierarchical load forecasting problem where we were required to backcast and forecast hourly loads for a US utility with twenty geographical zones. Our team, TinTin, ranked fifth out of 105 participating teams, and we have been awarded an IEEE Power & Energy Society award.

Doctorat en sciences, Spécialisation Informatique
info:eu-repo/semantics/nonPublished

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Mercurio, Danilo. "Adaptive estimation for financial time series." Doctoral thesis, [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=972597263.

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Winn, David. "An analysis of neural networks and time series techniques for demand forecasting." Thesis, Rhodes University, 2007. http://hdl.handle.net/10962/d1004362.

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This research examines the plausibility of developing demand forecasting techniques which are consistently and accurately able to predict demand. Time Series Techniques and Artificial Neural Networks are both investigated. Deodorant sales in South Africa are specifically studied in this thesis. Marketing techniques which are used to influence consumer buyer behaviour are considered, and these factors are integrated into the forecasting models wherever possible. The results of this research suggest that Artificial Neural Networks can be developed which consistently outperform industry forecasting targets as well as Time Series forecasts, suggesting that producers could reduce costs by adopting this more effective method.

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Marriott, Richard Keyworth. "Estimating and forecasting a demand chain for food using cross-section and time-series data." Thesis, University of Bristol, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266903.

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Díaz, González Fernando. "Federated Learning for Time Series Forecasting Using LSTM Networks: Exploiting Similarities Through Clustering." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254665.

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Анотація:

Federated learning poses a statistical challenge when training on highly heterogeneous sequence data. For example, time-series telecom data collected over long intervals regularly shows mixed fluctuations and patterns. These distinct distributions are an inconvenience when a node not only plans to contribute to the creation of the global model but also plans to apply it on its local dataset. In this scenario, adopting a one-fits-all approach might be inadequate, even when using state-of-the-art machine learning techniques for time series forecasting, such as Long Short-Term Memory (LSTM) networks, which have proven to be able to capture many idiosyncrasies and generalise to new patterns. In this work, we show that by clustering the clients using these patterns and selectively aggregating their updates in different global models can improve local performance with minimal overhead, as we demonstrate through experiments using realworld time series datasets and a basic LSTM model.
Federated Learning utgör en statistisk utmaning vid träning med starkt heterogen sekvensdata. Till exempel så uppvisar tidsseriedata inom telekomdomänen blandade variationer och mönster över längre tidsintervall. Dessa distinkta fördelningar utgör en utmaning när en nod inte bara ska bidra till skapandet av en global modell utan även ämnar applicera denna modell på sin lokala datamängd. Att i detta scenario införa en global modell som ska passa alla kan visa sig vara otillräckligt, även om vi använder oss av de mest framgångsrika modellerna inom maskininlärning för tidsserieprognoser, Long Short-Term Memory (LSTM) nätverk, vilka visat sig kunna fånga komplexa mönster och generalisera väl till nya mönster. I detta arbete visar vi att genom att klustra klienterna med hjälp av dessa mönster och selektivt aggregera deras uppdateringar i olika globala modeller kan vi uppnå förbättringar av den lokal prestandan med minimala kostnader, vilket vi demonstrerar genom experiment med riktigt tidsseriedata och en grundläggande LSTM-modell.

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Li, Lei. "Fast Algorithms for Mining Co-evolving Time Series." Research Showcase @ CMU, 2011. http://repository.cmu.edu/dissertations/112.

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Анотація:

Time series data arise in many applications, from motion capture, environmental monitoring, temperatures in data centers, to physiological signals in health care. In the thesis, I will focus on the theme of learning and mining large collections of co-evolving sequences, with the goal of developing fast algorithms for finding patterns, summarization, and anomalies. In particular, this thesis will answer the following recurring challenges for time series: 1. Forecasting and imputation: How to do forecasting and to recover missing values in time series data? 2. Pattern discovery and summarization: How to identify the patterns in the time sequences that would facilitate further mining tasks such as compression, segmentation and anomaly detection? 3. Similarity and feature extraction: How to extract compact and meaningful features from multiple co-evolving sequences that will enable better clustering and similarity queries of time series? 4. Scale up: How to handle large data sets on modern computing hardware? We develop models to mine time series with missing values, to extract compact representation from time sequences, to segment the sequences, and to do forecasting. For large scale data, we propose algorithms for learning time series models, in particular, including Linear Dynamical Systems (LDS) and Hidden Markov Models (HMM). We also develop a distributed algorithm for finding patterns in large web-click streams. Our thesis will present special models and algorithms that incorporate domain knowledge. For motion capture, we will describe the natural motion stitching and occlusion filling for human motion. In particular, we provide a metric for evaluating the naturalness of motion stitching, based which we choose the best stitching. Thanks to domain knowledge (body structure and bone lengths), our algorithm is capable of recovering occlusions in mocap sequences, better in accuracy and longer in missing period. We also develop an algorithm for forecasting thermal conditions in a warehouse-sized data center. The forecast will help us control and manage the data center in a energy-efficient way, which can save a significant percentage of electric power consumption in data centers.

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Kruger, Albertus Stephanus. "An investigation into the use of combined linear and neural network models for time series data / A.S. Kruger." Thesis, North-West University, 2009. http://hdl.handle.net/10394/4782.

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Time series forecasting is an important area of forecasting in which past observations of the same variable are collected and analyzed to develop a model describing the underlying relationship. The model is then used to extrapolate the time series into the future. This modeling approach is particularly useful when little knowledge is available on the underlying data generating process or when there is no satisfactory explanatory model that relates the prediction variable to other explanatory variables. Time series can be modeled in a variety of ways e.g. using exponential smoothing techniques, regression models, autoregressive (AR) techniques, moving averages (MA) etc. Recent research activities in forecasting also suggested that artificial neural networks can be used as an alternative to traditional linear forecasting models. This study will, along the lines of an existing study in the literature, investigate the use of a hybrid approach to time series forecasting using both linear and neural network models. The proposed methodology consists of two basic steps. In the first step, a linear model is used to analyze the linear part of the problem and in the second step a neural network model is developed to model the residuals from the linear model. The results from the neural network can then be used to predict the error terms for the linear model. This means that the combined forecast of the time series will depend on both models. Following an overview of the models, empirical tests on real world data will be performed to determine the forecasting performance of such a hybrid model. Results have indicated that depending on the forecasting period, it might be worthwhile to consider the use of a hybrid model.
Thesis (M.Sc. (Computer Science))--North-West University, Vaal Triangle Campus, 2010.

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Книги з теми "Forecasting of data in the form of time series"

A, Dickey David, ed. SAS for forecasting time series. 2nd ed. New York: J. Wiley, 2003.

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Brocklebank, John Clare. SAS system for forecasting time series. Cary, NC: SAS Institute, 1986.

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Maiti, Raju. Modelling and coherent forecasting of zero-inflated time series count data. Ahmedabad: Indian Institute of Management, 2013.

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Riazuddin, Riaz. Detection and forecasting of Islamic calendar effects in Time Series Data. Karachi: State Bank of Pakistan, 2002.

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Liu, Lon-Mu. Forecasting and time series analysis using the SCA statistical system. River Forest, Ill: Scientific Computing Associates, 2004.

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Bastian, Jörgen. Optimale Zeitreihenprognose: Empirische Probleme und Lösungen. Frankfurt am Main: P. Lang, 1985.

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Bagus, Erich. Computergestützte Zeitreihenprognose mit linear-rekursiven Modellen. Idstein: Schulz-Kirchner Verlag, 1994.

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Intelligent systems and financial forecasting. London: Springer, 1997.

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Dickey, David A., and John C. Brocklebank. SAS for Forecasting Time Series. WA (Wiley-SAS), 2003.

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Agung, I. Gusti Ngurah. Advanced Time Series Data Analysis: Forecasting Using EViews. Wiley & Sons, Incorporated, John, 2018.

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Частини книг з теми "Forecasting of data in the form of time series"

Devi, J. Vimala, and K. S. Kavitha. "Time Series Analysis and Forecasting on Crime Data." In Algorithms for Intelligent Systems, 281–97. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6332-1_26.

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Arratia, Argimiro, and Eduardo Sepúlveda. "Convolutional Neural Networks, Image Recognition and Financial Time Series Forecasting." In Mining Data for Financial Applications, 60–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37720-5_5.

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Zufferey, Thierry, Andreas Ulbig, Stephan Koch, and Gabriela Hug. "Forecasting of Smart Meter Time Series Based on Neural Networks." In Data Analytics for Renewable Energy Integration, 10–21. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50947-1_2.

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Carriegos, Miguel V., and Ramón Ángel Fernández-Díaz. "Towards Forecasting Time-Series of Cyber-Security Data Aggregates." In 13th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2020), 273–81. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57805-3_26.

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Cubadda, Gianluca, and Barbara Guardabascio. "On the Use of PLS Regression for Forecasting Large Sets of Cointegrated Time Series." In Advanced Statistical Methods for the Analysis of Large Data-Sets, 171–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21037-2_16.

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Momin, Bashirahamad, and Gaurav Chavan. "Univariate Time Series Models for Forecasting Stationary and Non-stationary Data: A Brief Review." In Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 2, 219–26. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63645-0_24.

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Barbaglia, Luca, Sergio Consoli, and Sebastiano Manzan. "Exploring the Predictive Power of News and Neural Machine Learning Models for Economic Forecasting." In Mining Data for Financial Applications, 135–49. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66981-2_11.

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AbstractForecasting economic and financial variables is a challenging task for several reasons, such as the low signal-to-noise ratio, regime changes, and the effect of volatility among others. A recent trend is to extract information from news as an additional source to forecast economic activity and financial variables. The goal is to evaluate if news can improve forecasts from standard methods that usually are not well-specified and have poor out-of-sample performance. In a currently on-going project, our goal is to combine a richer information set that includes news with a state-of-the-art machine learning model. In particular, we leverage on two recent advances in Data Science, specifically on Word Embedding and Deep Learning models, which have recently attracted extensive attention in many scientific fields. We believe that by combining the two methodologies, effective solutions can be built to improve the prediction accuracy for economic and financial time series. In this preliminary contribution, we provide an overview of the methodology under development and some initial empirical findings. The forecasting model is based on DeepAR, an auto-regressive probabilistic Recurrent Neural Network model, that is combined with GloVe Word Embeddings extracted from economic news. The target variable is the spread between the US 10-Year Treasury Constant Maturity and the 3-Month Treasury Constant Maturity (T10Y3M). The DeepAR model is trained on a large number of related GloVe Word Embedding time series, and employed to produce point and density forecasts.

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Arratia, Argimiro, Gustavo Avalos, Alejandra Cabaña, Ariel Duarte-López, and Martí Renedo-Mirambell. "Sentiment Analysis of Financial News: Mechanics and Statistics." In Data Science for Economics and Finance, 195–216. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66891-4_9.

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AbstractThis chapter describes the basic mechanics for building a forecasting model that uses as input sentiment indicators derived from textual data. In addition, as we focus our target of predictions on financial time series, we present a set of stylized empirical facts describing the statistical properties of lexicon-based sentiment indicators extracted from news on financial markets. Examples of these modeling methods and statistical hypothesis tests are provided on real data. The general goal is to provide guidelines for financial practitioners for the proper construction and interpretation of their own time-dependent numerical information representing public perception toward companies, stocks’ prices, and financial markets in general.

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Nayak, Sarat Chandra, Bijan Bihari Misra, and Satchidananda Dehuri. "Hybridization of the Higher Order Neural Networks with the Evolutionary Optimization Algorithms—An Application to Financial Time Series Forecasting." In Advances in Machine Learning for Big Data Analysis, 119–44. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8930-7_5.

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Basellini, Ugofilippo, and Carlo Giovanni Camarda. "A Three-Component Approach to Model and Forecast Age-at-Death Distributions." In Developments in Demographic Forecasting, 105–29. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42472-5_6.

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Abstract Mortality forecasting has recently received growing interest, as accurate projections of future lifespans are needed to ensure the solvency of insurance and pension providers. Several innovative stochastic methodologies have been proposed in most recent decades, the majority of them being based on age-specific mortality rates or on summary measures of the life table. The age-at-death distribution is an informative life-table function that provides readily available information on the mortality pattern of a population, yet it has been mostly overlooked for mortality projections. In this chapter, we propose to analyse and forecast mortality developments over age and time by introducing a novel methodology based on age-at-death distributions. Our approach starts from a nonparametric decomposition of the mortality pattern into three independent components corresponding to Childhood, Early-Adulthood and Senescence, respectively. We then model the evolution of each component-specific death density with a relational model that associates a time-invariant standard to a series of observed distributions by means of a transformation of the age axis. Our approach allows us to capture mortality developments over age and time, and forecasts can be derived from parameters’ extrapolation using standard time series models. We illustrate our methods by estimating and forecasting the mortality pattern of females and males in two high-longevity countries using data of the Human Mortality Database. We compare the forecast accuracy of our model and its projections until 2050 with three other forecasting methodologies.

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Тези доповідей конференцій з теми "Forecasting of data in the form of time series"

Iosevich, S., G. Arutyunyants, and Z. Hou. "Dynamic aggregation for time series forecasting." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363996.

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Kalinggo, Bonnie Alexandra, and Zulkarnain. "Time Series Forecasting for Non-stationary Data." In APCORISE 2020: 3rd Asia Pacific Conference on Research in Industrial and Systems Engineering 2020. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3400934.3400952.

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Almuammar, Manal, and Maria Fasli. "Deep Learning for Non-stationary Multivariate Time Series Forecasting." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006192.

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Horelu, Adriana, Catalin Leordeanu, Elena Apostol, Dan Huru, Mariana Mocanu, and Valentin Cristea. "Forecasting Techniques for Time Series from Sensor Data." In 2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2015. http://dx.doi.org/10.1109/synasc.2015.49.

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Wu, Wenrui, Tao Tao, Jing Shang, Ding Xiao, Chuan Shi, and Yong Jiang. "Sequence Attention for Multivariate Time Series Forecasting." In 2021 IEEE Sixth International Conference on Data Science in Cyberspace (DSC). IEEE, 2021. http://dx.doi.org/10.1109/dsc53577.2021.00019.

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Abbas, Zainab, Jon Reginbald Ivarsson, Ahmad Al-Shishtawy, and Vladimir Vlassov. "Scaling Deep Learning Models for Large Spatial Time-Series Forecasting." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9005475.

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Li, Zhaoxi, Jun He, Hongyan Liu, and Xiaoyong Du. "Combining Global and Sequential Patterns for Multivariate Time Series Forecasting." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378380.

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Ferreira, Tiago A. E., Germano C. Vasconcelos, and Paulo J. L. Adeodato. "A New Evolutionary Approach for Time Series Forecasting." In 2007 IEEE Symposium on Computational Intelligence and Data Mining. IEEE, 2007. http://dx.doi.org/10.1109/cidm.2007.368933.

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Muntean, Maria Viorela, and Daniela Onita. "Agent for Preprocessing and Forecasting Time-Series Data." In 2018 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2018. http://dx.doi.org/10.1109/ecai.2018.8679048.

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Feng, Cong, Erol Kevin Chartan, Bri-Mathias Hodge, and Jie Zhang. "Characterizing Time Series Data Diversity for Wind Forecasting." In UCC '17: 10th International Conference on Utility and Cloud Computing. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3148055.3148065.

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Звіти організацій з теми "Forecasting of data in the form of time series"

Mikayilov, Jeyhun, Ryan Alyamani, Abdulelah Darandary, Muhammad Javid, Fakhri Hasanov, Saleh T. AlTurki, and Rey B. Arnaiz. Modeling and Forecasting Industrial Electricity Demand for Saudi Arabia: Uncovering Regional Characteristics. King Abdullah Petroleum Studies and Research Center, January 2022. http://dx.doi.org/10.30573/ks--2021-dp19.

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The objective of this study is to investigate Saudi Arabia’s industrial electricity consumption at the regional level. We apply structural time series modeling to annual data over the period of 1990 to 2019. In addition to estimating the size and significance of the price and income elasticities for regional industrial electricity demand, this study projects regional industrial electricity demand up to 2030. This is done using estimated equations and assuming different future values for price and income. The results show that the long-run income and price elasticities of industrial electricity demand vary across regions. The underlying energy demand trend analysis indicates some efficiency improvements in industrial electricity consumption patterns in all regions.

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Mikayilov, Jeyhun, Ryan Alyamani, Abdulelah Darandary, Muhammad Javid, and Fakhri Hasanov. Modeling and Forecasting Industrial Electricity Demand for Saudi Arabia: Uncovering Regional Characteristics. King Abdullah Petroleum Studies and Research Center, January 2022. http://dx.doi.org/10.30573/ks--2021-dp22.

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Russell, H. A. J., and S. K. Frey. Canada One Water: integrated groundwater-surface-water-climate modelling for climate change adaptation. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329092.

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Canada 1 Water is a 3-year governmental multi-department-private-sector-academic collaboration to model the groundwater-surface-water of Canada coupled with historic climate and climate scenario input. To address this challenge continental Canada has been allocated to one of 6 large watershed basins of approximately two million km2. The model domains are based on natural watershed boundaries and include approximately 1 million km2 of the United States. In year one (2020-2021) data assembly and validation of some 20 datasets (layers) is the focus of work along with conceptual model development. To support analysis of the entire water balance the modelling framework consists of three distinct components and modelling software. Land Surface modelling with the Community Land Model will support information needed for both the regional climate modelling using the Weather Research & Forecasting model (WRF), and input to HydroGeoSphere for groundwater-surface-water modelling. The inclusion of the transboundary watersheds will provide a first time assessment of water resources in this critical international domain. Modelling is also being integrated with Remote Sensing datasets, notably the Gravity Recovery and Climate Experiment (GRACE). GRACE supports regional scale watershed analysis of total water flux. GRACE along with terrestrial time-series data will serve provide validation datasets for model results to ensure that the final project outputs are representative and reliable. The project has an active engagement and collaborative effort underway to try and maximize the long-term benefit of the framework. Much of the supporting model datasets will be published under open access licence to support broad usage and integration.

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Semerikov, Serhiy, Illia Teplytskyi, Yuliia Yechkalo, Oksana Markova, Vladimir Soloviev, and Arnold Kiv. Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. [б. в.], June 2019. http://dx.doi.org/10.31812/123456789/3178.

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The authors of the given article continue the series presented by the 2018 paper “Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot”. This time, they consider mathematical informatics as the basis of higher engineering education fundamentalization. Mathematical informatics deals with smart simulation, information security, long-term data storage and big data management, artificial intelligence systems, etc. The authors suggest studying basic principles of mathematical informatics by applying cloud-oriented means of various levels including those traditionally considered supplementary – spreadsheets. The article considers ways of building neural network models in cloud-oriented spreadsheets, Google Sheets. The model is based on the problem of classifying multi-dimensional data provided in “The Use of Multiple Measurements in Taxonomic Problems” by R. A. Fisher. Edgar Anderson’s role in collecting and preparing the data in the 1920s-1930s is discussed as well as some peculiarities of data selection. There are presented data on the method of multi-dimensional data presentation in the form of an ideograph developed by Anderson and considered one of the first efficient ways of data visualization.

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Bendikov, Michael, and Thomas C. Harmon. Development of Agricultural Sensors Based on Conductive Polymers. United States Department of Agriculture, August 2006. http://dx.doi.org/10.32747/2006.7591738.bard.

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In this 1-year feasibility study, we tried polymerization of several different monomers, commercial as well as novel, specially designed and synthesized for this project in the presence of the nitrate ion to produce imprinted conductive polymers. Polymers 1 and 2 (shown below) produced a response to nitrate, but one inferior to that produced by a polypyrrole (Ppy)-based sensor (which we demonstrated prior to this study). Thus, we elected to proceed with improving the stability of the Ppy-based sensor. In order to improve stability of the Ppy-based sensor, we created a two-layer design which includes nitrate-doped Ppy as an inner layer, and nitrate-doped PEDOT as the outer layer. PEDOT is known for its high environmental stability and conductivity. This design has demonstrated promise, but is still undergoing optimization and stability testing. Previously we had failed to create nitrate-doped PEDOT in the absence of a Ppy layer. Nitrate-doped PEDOT should be very promising for sensor applications due to its high stability and exceptional sensing properties as we showed previously for sensing of perchlorate ions (by perchlorate-doped PEDOT). During this year, we have succeeded in preparing nitrate-doped PEDOT (4 below) by designing a new starting monomer (compound 3 below) for polymerization. We are currently testing this design for nitrate sensing. In parallel with the fabrication design studies, we fabricated and tested nitrate-doped Ppy sensors in a series of flow studies under laboratory and field conditions. Nitrate-doped Ppy sensors are less stable than is desirable but provide excellent nitrate sensing characteristics for the short-term experiments focusing on packaging and deployment strategies. The fabricated sensors were successfully interfaced with a commercial battery-powered self-logging (Onset Computer Hobo Datalogger) and a wireless data acquisition and transmission system (Crossbow Technologies MDA300 sensor interface and Mica2 wireless mote). In a series of flow-through experiments with water, the nitrate-doped Ppy sensors were exposed to pulses of dissolved nitrate and compared favorably with an expensive commercial sensor. In 24-hour field tests in both Merced and in Palmdale, CA agricultural soils, the sensors responded to introduced nitrate pulses, but with different dynamics relative to the larger commercial sensors. These experiments are on-going but suggest a form factor (size, shape) effect of the sensor when deployed in a porous medium such as soil. To fill the need for a miniature reference electrode, we identified and tested one commercial version (Cypress Systems, ESA Mini-reference electrode) which works well but is expensive ($190). To create an inexpensive miniature reference electrode, we are exploring the use of AgCl-coated silver wire. This electrode is not a “true” reference electrode; however, it can calibrated once versus a commercial reference electrode at the time of deployment in soil. Thus, only one commercial reference electrode would suffice to support a multiple sensor deployment.

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