scholarly journals Traditional vs. Machine-Learning Methods for Forecasting Sandy Shoreline Evolution Using Historic Satellite-Derived Shorelines

2021 ◽  
Vol 13 (5) ◽  
pp. 934
Author(s):  
Floris Calkoen ◽  
Arjen Luijendijk ◽  
Cristian Rodriguez Rivero ◽  
Etienne Kras ◽  
Fedor Baart
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Mean Squared Error ◽  
Computation Time ◽  
Ordinary Least Squares ◽  
Anthropogenic Pressures ◽  
Time Series Forecast ◽  
Shoreline Evolution ◽  
Shoreline Prediction ◽  
Probabilistic Machine Learning

Forecasting shoreline evolution for sandy coasts is important for sustainable coastal management, given the present-day increasing anthropogenic pressures and a changing future climate. Here, we evaluate eight different time-series forecasting methods for predicting future shorelines derived from historic satellite-derived shorelines. Analyzing more than 37,000 transects around the globe, we find that traditional forecast methods altogether with some of the evaluated probabilistic Machine Learning (ML) time-series forecast algorithms, outperform Ordinary Least Squares (OLS) predictions for the majority of the sites. When forecasting seven years ahead, we find that these algorithms generate better predictions than OLS for 54% of the transect sites, producing forecasts with, on average, 29% smaller Mean Squared Error (MSE). Importantly, this advantage is shown to exist over all considered forecast horizons, i.e., from 1 up to 11 years. Although the ML algorithms do not produce significantly better predictions than traditional time-series forecast methods, some proved to be significantly more efficient in terms of computation time. We further provide insight in how these ML algorithms can be improved so that they can be expected to outperform not only OLS regression, but also the traditional time-series forecast methods. These forecasting algorithms can be used by coastal engineers, managers, and scientists to generate future shoreline prediction at a global level and derive conclusions thereof.

scholarly journals Attacking Data Transforming Learners at Training Time

2019 ◽  
Vol 33 ◽  
pp. 3167-3174
Author(s):  
Scott Alfeld ◽  
Ara Vartanian ◽  
Lucas Newman-Johnson ◽  
Benjamin I.P. Rubinstein
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Linear Regression ◽  
Ordinary Least Squares ◽  
General Purpose ◽  
Training Time ◽  
Target Values ◽  
Gradient Based ◽  
Deployment Time ◽  
Compositional Learning

While machine learning systems are known to be vulnerable to data-manipulation attacks at both training and deployment time, little is known about how to adapt attacks when the defender transforms data prior to model estimation. We consider the setting where the defender Bob first transforms the data then learns a model from the result; Alice, the attacker, perturbs Bob’s input data prior to him transforming it. We develop a general-purpose “plug and play” framework for gradient-based attacks based on matrix differentials, focusing on ordinary least-squares linear regression. This allows learning algorithms and data transformations to be paired and composed arbitrarily: attacks can be adapted through the use of the chain rule—analogous to backpropagation on neural network parameters—to compositional learning maps. Bestresponse attacks can be computed through matrix multiplications from a library of attack matrices for transformations and learners. Our treatment of linear regression extends state-ofthe-art attacks at training time, by permitting the attacker to affect both features and targets optimally and simultaneously. We explore several transformations broadly used across machine learning with a driving motivation for our work being autogressive modeling. There, Bob transforms a univariate time series into a matrix of observations and vector of target values which can then be fed into standard learners. Under this learning reduction, a perturbation from Alice to a single value of the time series affects features of several data points along with target values.

scholarly journals Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Hye-Jin Kim ◽  
Sung Min Park ◽  
Byung Jin Choi ◽  
Seung-Hyun Moon ◽  
Yong-Hyuk Kim
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Quality Control ◽  
Mean Squared Error ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Weather Element ◽  
Applied Machine Learning ◽  
Squared Error ◽  
Atmospheric Data

We propose three quality control (QC) techniques using machine learning that depend on the type of input data used for training. These include QC based on time series of a single weather element, QC based on time series in conjunction with other weather elements, and QC using spatiotemporal characteristics. We performed machine learning-based QC on each weather element of atmospheric data, such as temperature, acquired from seven types of IoT sensors and applied machine learning algorithms, such as support vector regression, on data with errors to make meaningful estimates from them. By using the root mean squared error (RMSE), we evaluated the performance of the proposed techniques. As a result, the QC done in conjunction with other weather elements had 0.14% lower RMSE on average than QC conducted with only a single weather element. In the case of QC with spatiotemporal characteristic considerations, the QC done via training with AWS data showed performance with 17% lower RMSE than QC done with only raw data.

THE IMPROVED BPNN-NAR AND BPNN-NARMA MODELS ON MALAYSIAN AGGREGATE COST INDICES WITH OUTLYING DATA

2016 ◽  
Vol 78 (12-3) ◽  
Author(s):  
Saadi Ahmad Kamaruddin ◽  
Nor Azura Md Ghani ◽  
Norazan Mohamed Ramli
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mean Squared Error ◽  
Ordinary Least Squares ◽  
Series Data ◽  
Backpropagation Neural Network ◽  
Data Set ◽  
Squared Error ◽  
The Impact ◽  
Nonlinear Autoregressive

Neurocomputing have been adapted in time series forecasting arena, but the presence of outliers that usually occur in data time series may be harmful to the data network training. This is because the ability to automatically find out any patterns without prior assumptions and loss of generality. In theory, the most common training algorithm for Backpropagation algorithms leans on reducing ordinary least squares estimator (OLS) or more specifically, the mean squared error (MSE). However, this algorithm is not fully robust when outliers exist in training data, and it will lead to false forecast future value. Therefore, in this paper, we present a new algorithm that manipulate algorithms firefly on least median squares estimator (FFA-LMedS) for  Backpropagation neural network nonlinear autoregressive (BPNN-NAR) and Backpropagation neural network nonlinear autoregressive moving (BPNN-NARMA) models to reduce the impact of outliers in time series data. The performances of the proposed enhanced models with comparison to the existing enhanced models using M-estimators, Iterative LMedS (ILMedS) and Particle Swarm Optimization on LMedS (PSO-LMedS) are done based on root mean squared error (RMSE) values which is the main highlight of this paper. In the meanwhile, the real-industrial monthly data of Malaysian Aggregate cost indices data set from January 1980 to December 2012 (base year 1980=100) with different degree of outliers problem is adapted in this research. At the end of this paper, it was found that the enhanced BPNN-NARMA models using M-estimators, ILMedS and FFA-LMedS performed very well with RMSE values almost zero errors. It is expected that the findings would assist the respected authorities involve in Malaysian construction projects to overcome cost overruns.

scholarly journals PREDIKSI DATA TRANSAKSI PENJUALAN TIME SERIES MENGGUNAKAN REGRESI LSTM

2020 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Marie Luthfi Ashari ◽  
Mujiono Sadikin
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Percentage Error ◽  
Short Term ◽  
Term Memory ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Long Short Term Memory

Sebagai upaya untuk memenangkan persaingan di pasar, perusahaan farmasi harus menghasilkan produk obat – obatan yang berkualitas. Untuk menghasilkan produk yang berkualitas, diperlukan perencanaan produksi yang baik dan efisien. Salah satu dasar perencanaan produksi adalah prediksi penjualan. PT. Metiska Farma telah menerapkan metode prediksi dalam proses produksi, akan tetapi prediksi yang dihasilkan tidak akurat sehingga menyebabkan tidak optimal dalam memenuhi permintaan pasar. Untuk meminimalisir masalah kurang akuratnya proses prediksi tersebut, dalam penelitian yang disajikan pada makalah ini dilakukan uji coba prediksi menggunakan teknik Machine Learning dengan metode Regresi Long Short Term Memory (LSTM). Teknik yang diusulkan diuji coba menggunakan dataset penjualan produk “X” dari PT. Metiska Farma dengan parameter kinerja Root Mean Squared Error (RMSE) dan MAPE (Mean Absolute Percentage Error). Hasil penelitian ini berupa nilai rata – rata evaluasi error dari pemodelan data training dan data testing. Di mana hasil menunjukan bahwa Regresi LSTM memiliki nilai prediksi penjualan dengan evaluasi model melalui RMSE sebesar 286.465.424 untuk data training dan 187.013.430 untuk data testing. Untuk nilai MAPE sebesar 787% dan 309% untuk data training dan data testing secara berurut.

scholarly journals Fast Prediction with Sparse Multikernel LS-SVR Using Multiple Relevant Time Series and Its Application in Avionics System

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Yang M. Guo ◽  
Pei He ◽  
Xiang T. Wang ◽  
Ya F. Zheng ◽  
Chong Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Mean Squared Error ◽  
Computation Time ◽  
Support Vector ◽  
Good Prediction ◽  
Trend Prediction ◽  
Good Prediction Accuracy ◽  
Fast Prediction

Health trend prediction is critical to ensure the safe operation of highly reliable systems. However, complex systems often present complex dynamic behaviors and uncertainty, which makes it difficult to develop a precise physical prediction model. Therefore, time series is often used for prediction in this case. In this paper, in order to obtain better prediction accuracy in shorter computation time, we propose a new scheme which utilizes multiple relevant time series to enhance the completeness of the information and adopts a prediction model based on least squares support vector regression (LS-SVR) to perform prediction. In the scheme, we apply two innovative ways to overcome the drawbacks of the reported approaches. One is to remove certain support vectors by measuring the linear correlation to increase sparseness of LS-SVR; the other one is to determine the linear combination weights of multiple kernels by calculating the root mean squared error of each basis kernel. The results of prediction experiments indicate preliminarily that the proposed method is an effective approach for its good prediction accuracy and low computation time, and it is a valuable method in applications.

Generalized Forecast Averaging in Autoregressions with a Near Unit Root

2020 ◽  
Author(s):  
Mohitosh Kejriwal ◽  
Xuewen Yu
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Mean Squared Error ◽  
Model Averaging ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
Finite Sample ◽  
Deterministic Components ◽  
Asymptotic Mean Squared Error ◽  
Near Unit Root

Summary This paper develops a new approach to forecasting a highly persistent time series that employs feasible generalized least squares (FGLS) estimation of the deterministic components in conjunction with Mallows model averaging. Within a local-to-unity asymptotic framework, we derive analytical expressions for the asymptotic mean squared error and one-step-ahead mean squared forecast risk of the proposed estimator and show that the optimal FGLS weights are different from their ordinary least squares (OLS) counterparts. We also provide theoretical justification for a generalized Mallows averaging estimator that incorporates lag order uncertainty in the construction of the forecast. Monte Carlo simulations demonstrate that the proposed procedure yields a considerably lower finite-sample forecast risk relative to OLS averaging. An application to U.S. macroeconomic time series illustrates the efficacy of the advocated method in practice and finds that both persistence and lag order uncertainty have important implications for the accuracy of forecasts.

scholarly journals Imputation of GPS Coordinate Time Series Using MissForest

2021 ◽  
Vol 13 (12) ◽  
pp. 2312
Author(s):  
Shengkai Zhang ◽  
Li Gong ◽  
Qi Zeng ◽  
Wenhao Li ◽  
Feng Xiao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Missing Values ◽  
Mean Squared Error ◽  
Principal Component ◽  
Polar Regions ◽  
Coordinate Time ◽  
Coordinate Time Series ◽  
Gps Time Series ◽  
Relationship Of

The global positioning system (GPS) can provide the daily coordinate time series to help geodesy and geophysical studies. However, due to logistics and malfunctioning, missing values are often “seen” in GPS time series, especially in polar regions. Acquiring a consistent and complete time series is the prerequisite for accurate and reliable statical analysis. Previous imputation studies focused on the temporal relationship of time series, and only a few studies used spatial relationships and/or were based on machine learning methods. In this study, we impute 20 Greenland GPS time series using missForest, which is a new machine learning method for data imputation. The imputation performance of missForest and that of four traditional methods are assessed, and the methods’ impacts on principal component analysis (PCA) are investigated. Results show that missForest can impute more than a 30-day gap, and its imputed time series has the least influence on PCA. When the gap size is 30 days, the mean absolute value of the imputed and true values for missForest is 2.71 mm. The normalized root mean squared error is 0.065, and the distance of the first principal component is 0.013. MissForest outperforms the other compared methods. MissForest can effectively restore the information of GPS time series and improve the results of related statistical processes, such as PCA analysis.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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