scholarly journals A Combined Control Systems and Machine Learning Approach to Forecasting Iceberg Flux off Newfoundland

2021 ◽  
Vol 13 (14) ◽  
pp. 7705
Author(s):  
Jennifer B. Ross ◽  
Grant R. Bigg ◽  
Yifan Zhao ◽  
Edward Hanna
Keyword(s):  
Machine Learning ◽  
Control Systems ◽  
Late Summer ◽  
Forecast Model ◽  
Seasonal Forecast ◽  
Rate Of Change ◽  
Learning Tools ◽  
Systems Model ◽  
The North ◽  
The Impact

Icebergs have long been a threat to shipping in the NW Atlantic and the iceberg season of February to late summer is monitored closely by the International Ice Patrol. However, reliable predictions of the severity of a season several months in advance would be useful for planning monitoring strategies and also for shipping companies in designing optimal routes across the North Atlantic for specific years. A seasonal forecast model of the build-up of seasonal iceberg numbers has recently become available, beginning to enable this longer-term planning of marine operations. Here we discuss extension of this control systems model to include more recent years within the trial ensemble sample set and also increasing the number of measures of the iceberg season that are considered within the forecast. These new measures include the seasonal iceberg total, the rate of change of the seasonal increase, the number of peaks in iceberg numbers experienced within a given season, and the timing of the peak(s). They are predicted by a range of machine learning tools. The skill levels of the new measures are tested, as is the impact of the extensions to the existing seasonal forecast model. We present a forecast for the 2021 iceberg season, predicting a medium iceberg year.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

scholarly journals Dynamical mechanisms linking Indian monsoon precipitation and the circumglobal teleconnection

2021 ◽  
Author(s):  
Jonathan D. Beverley ◽  
Steven J. Woolnough ◽  
Laura H. Baker ◽  
Stephanie J. Johnson ◽  
Antje Weisheimer ◽  
...  
Keyword(s):  
North America ◽  
Northern Hemisphere ◽  
Rossby Waves ◽  
Wave Train ◽  
Forecast Model ◽  
Seasonal Forecast ◽  
The North ◽  
Circumglobal Teleconnection ◽  
Rossby Wave Train ◽  
Ecmwf Model

AbstractThe circumglobal teleconnection (CGT) is an important mode of circulation variability, with an influence across many parts of the northern hemisphere. Here, we examine the excitation mechanisms of the CGT in the ECMWF seasonal forecast model, and the relationship between the Indian summer monsoon (ISM), the CGT and the extratropical northern hemisphere circulation. Results from relaxation experiments, in which the model is corrected to reanalysis in specific regions, suggest that errors over northwest Europe are more important in inhibiting the model skill at representing the CGT, in addition to northern hemisphere skill more widely, than west-central Asia and the ISM region, although the link between ISM precipitation and the extratropical circulation is weak in all experiments. Thermal forcing experiments in the ECMWF model, in which a heating is applied over India, suggest that the ISM does force an extratropical Rossby wave train, with upper tropospheric anticyclonic anomalies over east Asia, the North Pacific and North America associated with increased ISM heating. However, this eastward-propagating branch of the wave train does not project into Europe, and the response there occurs largely through westward-propagating Rossby waves. Results from barotropic model experiments show a response that is highly consistent with the seasonal forecast model, with similar eastward- and westward-propagating Rossby waves. This westward-propagating response is shown to be important in the downstream reinforcement of the wave train between Asia and North America.

scholarly journals Spoken words as biomarkers: using machine learning to gain insight into communication as a predictor of anxiety

2020 ◽  
Vol 27 (6) ◽  
pp. 929-933
Author(s):  
George Demiris ◽  
Kristin L Corey Magan ◽  
Debra Parker Oliver ◽  
Karla T Washington ◽  
Chad Chadwick ◽  
...  
Keyword(s):  
Machine Learning ◽  
Secondary Data ◽  
Health Indicators ◽  
Machine Learning Algorithms ◽  
Standardized Assessments ◽  
Learning Tools ◽  
Data Set ◽  
Problem Solving Therapy ◽  
Audio Communication ◽  
The Impact

Abstract Objective The goal of this study was to explore whether features of recorded and transcribed audio communication data extracted by machine learning algorithms can be used to train a classifier for anxiety. Materials and Methods We used a secondary data set generated by a clinical trial examining problem-solving therapy for hospice caregivers consisting of 140 transcripts of multiple, sequential conversations between an interviewer and a family caregiver along with standardized assessments of anxiety prior to each session; 98 of these transcripts (70%) served as the training set, holding the remaining 30% of the data for evaluation. Results A classifier for anxiety was developed relying on language-based features. An 86% precision, 78% recall, 81% accuracy, and 84% specificity were achieved with the use of the trained classifiers. High anxiety inflections were found among recently bereaved caregivers and were usually connected to issues related to transitioning out of the caregiving role. This analysis highlighted the impact of lowering anxiety by increasing reciprocity between interviewers and caregivers. Conclusion Verbal communication can provide a platform for machine learning tools to highlight and predict behavioral health indicators and trends.

Some Comparisons in the Thermal Structure of Lakes Wood, Kalamalka, Okanagan, Skaha, and Osoyoos, British Columbia

1973 ◽  
Vol 30 (7) ◽  
pp. 917-925 ◽  
Author(s):  
J. O. Blanton
Keyword(s):  
Thermal Structure ◽  
Heat Content ◽  
Late Summer ◽  
Static Stability ◽  
Rate Of Change ◽  
Direct Relation ◽  
Maximum Heat ◽  
The North ◽  
Relative Ability ◽  
External Cooling

The rate of change of heat contents in the lakes and the relative ability of each lake to transfer heat vertically to warm its hypolimnion was investigated. Rates of warming of hypolimnion water ranged from 0.06 C/month in Okanagan to 0.54 C/month in the north basin of Osoyoos. All lakes reached maximum static stability through the thermocline in late August except for Osoyoos (N), which reached its maximum somewhat earlier. Maximum heat content ranged from 18,100 cal/cm2 in Wood Lake to 33,300 cal/cm2 in Lake Okanagan. Maximum values of heat content were observed in late August in all lakes.There is a direct relation between the hypolimnetic warming rates and the maximum observed stability in the thermocline regions of all the lakes. However, the relatively low rate of Lake Wood indicates that there is an external cooling source such as groundwater influx. The late summer increase in volume of the epilimnion in Wood and the corresponding entrainment of nutrients from the hypolimnion to the epilimnion appear to control the amount of production observed at that time.

scholarly journals Machine Learning and Causality

2019 ◽  
Vol 19 (228) ◽  
Author(s):  
Andrew Tiffin
Keyword(s):  
Machine Learning ◽  
Economic Policy ◽  
Banking Crisis ◽  
Causal Discovery ◽  
Learning Tools ◽  
The Core ◽  
The Impact

Machine learning tools are well known for their success in prediction. But prediction is not causation, and causal discovery is at the core of most questions concerning economic policy. Recently, however, the literature has focused more on issues of causality. This paper gently introduces some leading work in this area, using a concrete example—assessing the impact of a hypothetical banking crisis on a country’s growth. By enabling consideration of a rich set of potential nonlinearities, and by allowing individually-tailored policy assessments, machine learning can provide an invaluable complement to the skill set of economists within the Fund and beyond.

scholarly journals Impact of Ocean Observation Systems on Ocean Analysis and Seasonal Forecasts

2007 ◽  
Vol 135 (2) ◽  
pp. 409-429 ◽  
Author(s):  
A. Vidard ◽  
D. L. T. Anderson ◽  
M. Balmaseda
Keyword(s):  
Data Assimilation ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Tropical Atlantic ◽  
Seasonal Forecasts ◽  
The North ◽  
Ocean Data Assimilation ◽  
Observation Systems ◽  
The Impact

Abstract The relative merits of the Tropical Atmosphere–Ocean (TAO)/Triangle Trans-Ocean Buoy Network (TAO/TRITON) and Pilot Research Moored Array in the Tropical Atlantic mooring networks, the Voluntary Observing Ship (VOS) expendable bathythermograph (XBT) network, and the Argo float network are evaluated through their impact on ocean analyses and seasonal forecast skill. An ocean analysis is performed in which all available data are assimilated. In two additional experiments the moorings and the VOS datasets are withheld from the assimilation. To estimate the impact on seasonal forecast skill, the set of ocean analyses is then used to initialize a corresponding set of coupled ocean–atmosphere model forecasts. A further set of experiments is conducted to assess the impact of the more recent Argo array. A key parameter for seasonal forecast initialization is the depth of the thermocline in the tropical Pacific. This depth is quite similar in all of the experiments that involve data assimilation, but withdrawing the TAO data has a bigger effect than withdrawing XBT data, especially in the eastern half of the basin. The forecasts mainly indicate that the TAO/TRITON in situ temperature observations are essential to obtain optimum forecast skill. They are best combined with XBT, however, because this results in better predictions for the west Pacific. Furthermore, the XBTs play an important role in the North Atlantic. The ocean data assimilation performs less well in the tropical Atlantic. This may be partly a result of not having adequate observations of salinity.

scholarly journals Can Machine Learning-Based Portfolios Outperform Traditional Risk-Based Portfolios? The Need to Account for Covariance Misspecification

Risks ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 74 ◽  
Author(s):  
Prayut Jain ◽  
Shashi Jain
Keyword(s):  
Machine Learning ◽  
Predictive Ability ◽  
Forecast Model ◽  
Minimum Variance ◽  
Portfolio Performance ◽  
Risk Parity ◽  
Out Of Sample ◽  
Weighted Portfolio ◽  
The Impact ◽  
Allocation Methods

The Hierarchical risk parity (HRP) approach of portfolio allocation, introduced by Lopez de Prado (2016), applies graph theory and machine learning to build a diversified portfolio. Like the traditional risk-based allocation methods, HRP is also a function of the estimate of the covariance matrix, however, it does not require its invertibility. In this paper, we first study the impact of covariance misspecification on the performance of the different allocation methods. Next, we study under an appropriate covariance forecast model whether the machine learning based HRP outperforms the traditional risk-based portfolios. For our analysis, we use the test for superior predictive ability on out-of-sample portfolio performance, to determine whether the observed excess performance is significant or if it occurred by chance. We find that when the covariance estimates are crude, inverse volatility weighted portfolios are more robust, followed by the machine learning-based portfolios. Minimum variance and maximum diversification are most sensitive to covariance misspecification. HRP follows the middle ground; it is less sensitive to covariance misspecification when compared with minimum variance or maximum diversification portfolio, while it is not as robust as the inverse volatility weighed portfolio. We also study the impact of the different rebalancing horizon and how the portfolios compare against a market-capitalization weighted portfolio.

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