Time Series data is large in volume, highly dimensional and continuous updating. Time series data analysis for forecasting, is one of the most important aspects of the practical usage. Accurate rainfall forecasting with the help of time series data analysis will help in evaluating drought and flooding situations in advance. In this paper, Artificial Neural Network (ANN) technique has been used to develop one-month and twomonth ahead forecasting models for rainfall prediction using monthly rainfall data of Northern India. In these model, Feed Forward Neural Network (FFNN) using Back Propagation Algorithm and LevenbergMarquardt training function has been used. The performance of both the models has been assessed based on Regression Analysis, Mean Square Error (MSE) and Magnitude of Relative Error (MRE). Proposed ANN model showed optimistic results for both the models for forecasting and found one month ahead forecasting model perform better than two months ahead forecasting model. This paper also gives some future directions for rainfall prediction and time series data analysis research.

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Development and Analysis of Artificial Neural Network Models for Rainfall Prediction by Using Time-Series Data

Multivariate time series clustering based on common principal component analysis

This paper presents a recurrent neural network (RNN) which is improved by using an efficient discrete wavelet transform (DWT) for predicting a high-frequency time series. In the combined DWT-RNN model, first, a multiresolution based on B-spline wavelet of high order d (BSd) is used to decompose the time series into several smooth data sets. Therefore, an approximation data set (with low-frequency) and several detail data sets (with high-frequency), with small wave amplitude, are obtained. Then, all decomposed components are used as RNN inputs. The proposed BSd-RNN model can approximate smooth patterns with satisfactory accuracy, and because of the local properties, BSd is a better choice than other common DWT such as Haar and Daubechies of order n (dbn), for preprocessing the high-frequency time series. According to results of performance metrics for predicting four different stock indices, the BSd-RNN model outperforms other common DWT-RNN model such as Haar-RNN and dbn-RNN. Also, the results show the BSd-RNN model outperforms other common artificial neural network (ANN) model such as multilayer feed-forward neural network (FFNN). Finally, The results show that BS3-RNN predicting model has better predictive ability than other compared models which use other wavelets or other ANNs.

Improving DWT-RNN model via B-spline wavelet multiresolution to forecast a high-frequency time series

Rainfall prediction is an important task due to the dependence of many people on it, especially in the agriculture sector. Prediction is difficult and even more complex due to the dynamic nature of rainfalls. In this study, we carry out monthly rainfall prediction over Simtokha a region in the capital of Bhutan, Thimphu. The rainfall data were obtained from the National Center of Hydrology and Meteorology Department (NCHM) of Bhutan. We study the predictive capability with Linear Regression, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional Long Short Term Memory (BLSTM) based on the parameters recorded by the automatic weather station in the region. Furthermore, this paper proposes a BLSTM-GRU based model which outperforms the existing machine and deep learning models. From the six different existing models under study, LSTM recorded the best Mean Square Error (MSE) score of 0.0128. The proposed BLSTM-GRU model outperformed LSTM by 41.1% with a MSE score of 0.0075. Experimental results are encouraging and suggest that the proposed model can achieve lower MSE in rainfall prediction systems.

https://www.mdpi.com/2072-4292/12/19/3174/pdf

Deep BLSTM-GRU Model for Monthly Rainfall Prediction: A Case Study of Simtokha, Bhutan

A smart ship collects various data with large volume, such as voyage, machinery, and weather data. Thus, big data analysis for smart ships is an important technology that can be widely applied to improve ship maintenance, operational efficiency, and equipment life management. In this study, an accurate regression model for the fuel consumption of the main engine by using an artificial neural network (ANN) was proposed by big data analysis including data collection, clustering, compression, and expansion. To obtain an accurate regression model, various numbers of hidden layers and neurons and different types of activation functions were tested in the ANN, and their effects on the accuracy and efficiency of the regression analysis were studied. The proposed regression model using ANN is a more accurate and efficient model to predict the fuel consumption of the main engine than polynomial regression and support vector machine.

Prediction of ship fuel consumption by using an artificial neural network

Time series data available in huge amounts can be used in decision-making. Such time series data can be converted into information to be used for forecasting. Various techniques are available for prediction and forecasting on the basis of time series data. Presently, the use of data mining techniques for this purpose is increasing day by day. In the present study, a comprehensive survey of data mining approaches and statistical techniques for rainfall prediction on time series data was conducted. A detailed comparison of different relevant techniques was also conducted and some plausible solutions are suggested for efficient time series data mining techniques for future algorithms.Â

Neelam Mishra

Papers

Development and Analysis of Artificial Neural Network Models for Rainfall Prediction by Using Time-Series Data

A Comprehensive Survey of Data Mining Techniques on Time Series Data for Rainfall Prediction