Wind forecast uncertainty prediction using Machine Learning techniques on Big Weather Data

2017 ◽  
Author(s):  
Rene Cabos ◽  
Peter Hecker ◽  
Nils Kneuper ◽  
Jens Schiefele
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Weather Data ◽  
Forecast Uncertainty ◽  
Uncertainty Prediction ◽  
Learning Techniques

scholarly journals Predicting Rainfall using Machine Learning Techniques

2021 ◽  
Author(s):  
Nikhil Oswal
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Machine Learning Techniques ◽  
Evaluation Metrics ◽  
Weather Data ◽  
Human Society ◽  
Rainfall Prediction ◽  
Modeling Methods ◽  
Learning Techniques ◽  
Processing Techniques

<p>Rainfall prediction is one of the challenging and uncertain tasks which has a signicant impact on human society. Timely and accurate predictions can help to proactively reduce human and nancial loss. This study presents a set of experiments which involve the use of prevalent machine learning techniques to build models to predict whether it is going to rain tomorrow or not based on weather data for that particular day in major cities of Australia. This comparative study is conducted concentrating on three aspects: modeling inputs, modeling methods, and pre-processing techniques. The results provide a comparison of various evaluation metrics of these machine learning techniques and their reliability to predict the rainfall by analyzing the weather data.</p>

scholarly journals Predicting Rainfall using Machine Learning Techniques

2021 ◽  
Author(s):  
Nikhil Oswal
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Machine Learning Techniques ◽  
Evaluation Metrics ◽  
Weather Data ◽  
Human Society ◽  
Rainfall Prediction ◽  
Modeling Methods ◽  
Learning Techniques ◽  
Processing Techniques

<p>Rainfall prediction is one of the challenging and uncertain tasks which has a signicant impact on human society. Timely and accurate predictions can help to proactively reduce human and nancial loss. This study presents a set of experiments which involve the use of prevalent machine learning techniques to build models to predict whether it is going to rain tomorrow or not based on weather data for that particular day in major cities of Australia. This comparative study is conducted concentrating on three aspects: modeling inputs, modeling methods, and pre-processing techniques. The results provide a comparison of various evaluation metrics of these machine learning techniques and their reliability to predict the rainfall by analyzing the weather data.</p>

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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