A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin

2014 ◽  
Vol 29 (2) ◽  
pp. 589-602 ◽  
Author(s):  
Shivshanker Singh Patel ◽  
Parthasarathy Ramachandran
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
River Basin ◽  
River Flow ◽  
Flow Time ◽  
Machine Learning Techniques ◽  
Cauvery River ◽  
Learning Techniques ◽  
Cauvery River Basin

Metaheuristic based MLP-SARIMAX HybridizationOne Hour Ahead Solar Radiation Forecasting

2021 ◽  
Author(s):  
Hugo Abreu Mendes ◽  
João Fausto Lorenzato Oliveira ◽  
Paulo Salgado Gomes Mattos Neto ◽  
Alex Coutinho Pereira ◽  
Eduardo Boudoux Jatoba ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Time Series ◽  
Solar Radiation ◽  
Statistical Models ◽  
Clean Energy ◽  
Machine Learning Techniques ◽  
Exogenous Variables ◽  
Learning Techniques ◽  
Photovoltaic Plants

Within the context of clean energy generation, solar radiation forecast is applied for photovoltaic plants to increase maintainability and reliability. Statistical models of time series like ARIMA and machine learning techniques help to improve the results. Hybrid Statistical + ML are found in all sorts of time series forecasting applications. This work presents a new way to automate the SARIMAX modeling, nesting PSO and ACO optimization algorithms, differently from R's AutoARIMA, its searches optimal seasonality parameter and combination of the exogenous variables available. This work presents 2 distinct hybrid models that have MLPs as their main elements, optimizing the architecture with Genetic Algorithm. A methodology was used to obtain the results, which were compared to LSTM, CLSTM, MMFF and NARNN-ARMAX topologies found in recent works. The obtained results for the presented models is promising for use in automatic radiation forecasting systems since it outperformed the compared models on at least two metrics.

scholarly journals Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques

2017 ◽  
Vol 226 ◽  
pp. 012117 ◽  
Author(s):  
Kasturi Kanchymalay ◽  
N. Salim ◽  
Anupong Sukprasert ◽  
Ramesh Krishnan ◽  
Ummi Raba’ah Hashim
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Palm Oil ◽  
Multivariate Time Series ◽  
Time Series Forecasting ◽  
Oil Price ◽  
Machine Learning Techniques ◽  
Crude Palm Oil ◽  
Learning Techniques

scholarly journals Forecasting of Air Passengers using ARIMA Modeling

2019 ◽  
Vol 8 (11S) ◽  
pp. 1050-1054
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Autocorrelation Function ◽  
Arima Model ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Forecasting Method ◽  
Partial Autocorrelation Function ◽  
Time Series Method ◽  
Arima Modeling

Air passengers prediction is said to be the centre of gravity of the growth. With people on the move constantly, there is bound to be some dissatisfaction amongst the customers which could be due to various reason, varying from overbooking of flights to ground operations. This dissatisfaction can be controlled till a limit, in ballpark figuring. In the past, this has been done using various machine learning techniques. For this prediction, in this project, ARIMA Modeling is used which is a time series forecasting method, based on machine learning. To test the stationarity of the data, which is done using Dickey Fuller test. If the data is stationary, it is fit into the ARIMA Model. If the data isn’t stationary, it is made stationary by differencing or by logarithmic transformation. The logarithmic method to make the data stationary. Once the data is stationary, using the Partial autocorrelation function and the autocorrelation function, values of p and q are found, which are required in the time series method. These values are then fit into the ARIMA Modeling and hence, the results are predicted. Upon the use and fitting of various models, the ARIMA(2,1,2) has been the best fit, having the least RMS and RMSE values.

Applications of Artificial Intelligence in the Realm of Business Intelligence

2021 ◽  
pp. 358-386
Author(s):  
Prakhar Mehrotra
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Time Series ◽  
Natural Language Processing ◽  
Language Processing ◽  
Business Intelligence ◽  
Machine Learning Techniques ◽  
Current State ◽  
Learning Techniques ◽  
High Level

The objective of this chapter is to discuss the integration of advancements made in the field of artificial intelligence into the existing business intelligence tools. Specifically, it discusses how the business intelligence tool can integrate time series analysis, supervised and unsupervised machine learning techniques and natural language processing in it and unlock deeper insights, make predictions, and execute strategic business action from within the tool itself. This chapter also provides a high-level overview of current state of the art AI techniques and provides examples in the realm of business intelligence. The eventual goal of this chapter is to leave readers thinking about what the future of business intelligence would look like and how enterprise can benefit by integrating AI in it.

Newly explored machine learning model for river flow time series forecasting at Mary River, Australia

2020 ◽  
Vol 192 (12) ◽  
Author(s):  
Fang Cui ◽  
Sinan Q. Salih ◽  
Bahram Choubin ◽  
Suraj Kumar Bhagat ◽  
Pijush Samui ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
River Flow ◽  
Learning Model ◽  
Flow Time ◽  
Time Series Forecasting ◽  
Machine Learning Model
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