scholarly journals Automating Time Series Analysis to Predict/Forecast Rainfall in AGUELMAM SIDI ALI Watershed in Morocco

2019 ◽  
Vol 8 (9) ◽  
pp. 3008-3014
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Scales ◽  
Univariate Analysis ◽  
Crop Productivity ◽  
Suitable Model ◽  
Estimation Of Parameters ◽  
Rainfall Forecasting ◽  
Rainfall Prediction ◽  
Series Analysis

Moroccan economy is largely based upon rainfall, use of water resources and crop productivity, for that it’s considered as an agricultural country. It’s more required and more important for any farmer to forecast rainfall prediction in order to analyze crop productivity. Predicting the atmosphere or forecasting the state of the weather is considered as challenge for scientific research. The prediction of rainfall monthly or/and seasonal time scales is the application of science and technology to invent and to schedule the agriculture strategies. Recently different research articles achieve to forecast and/or predict rainfall monthly or seasonal time scales using different techniques. The methodology followed in this work, be focused on automating time series analysis to forecast / predict precipitation daily, monthly or seasonal in Aguelmam Sidi Ali basin in Morocco for last 32 years ago from 1975 to 2007. We first have to study the rainfall data theoretically using the simplest form statistical analysis, which is the univariate analysis, as long as only one variable is involved in our case study. To get the selected and suitable model of time series to automate, we used different autocorrelation methods based on various criterion such as: Akaike Information Criterion (AIC), estimation of parameters using Yule-Walker (YW) and Maximum Likelihood Estimation (MLE). The results of our experiment show that it is possible using our system to obtain accurate rainfall prediction, with a more details and with a very fast way. It shows also that it’s possible to predict for next months or next years. To minimize the risk of floods and natural disasters within a basin in general and within the Aguelmam Sidi Ali basin in particular, accurate and timely rainfall forecasting is required.

Rainfall Prediction Using Time Series Analysis

2017 ◽  
pp. 517-539
Author(s):  
Mehdi Vafakhah ◽  
Hussein Akbari Majdar ◽  
Saeid Eslamian
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Rainfall Prediction ◽  
Series Analysis

scholarly journals Nonlinear time series analysis of the fluctuations of the geomagnetic horizontal field

2002 ◽  
Vol 20 (2) ◽  
pp. 175-183 ◽  
Author(s):  
B. George ◽  
G. Renuka ◽  
K. Satheesh Kumar ◽  
C. P. Anil Kumar ◽  
C. Venugopal
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Correlation Dimension ◽  
Prediction Method ◽  
Nonlinear Time Series ◽  
Nonlinear Time Series Analysis ◽  
Suitable Model ◽  
Nonlinear Prediction ◽  
Series Analysis ◽  
Hourly Data

Abstract. A detailed nonlinear time series analysis of the hourly data of the geomagnetic horizontal intensity H measured at Kodaikanal (10.2° N; 77.5° E; mag: dip 3.5° N) has been carried out to investigate the dynamical behaviour of the fluctuations of H. The recurrence plots, spatiotemporal entropy and the result of the surrogate data test show the deterministic nature of the fluctuations, rejecting the hypothesis that H belong to the family of linear stochastic signals. The low dimensional character of the dynamics is evident from the estimated value of the correlation dimension and the fraction of false neighbours calculated for various embedding dimensions. The exponential decay of the power spectrum and the positive Lyapunov exponent indicate chaotic behaviour of the underlying dynamics of H. This is also supported by the results of the comparison of the chaotic characteristics of the time series of H with the pseudo-chaotic characteristics of coloured noise time series. We have also shown that the error involved in the short-term prediction of successive values of H, using a simple but robust, zero-order nonlinear prediction method, increases exponentially. It has also been suggested that there exists the possibility of characterizing the geomagnetic fluctuations in terms of the invariants in chaos theory, such as Lyapunov exponents and correlation dimension. The results of the analysis could also have implications in the development of a suitable model for the daily fluctuations of geomagnetic horizontal intensity.Key words. Geomagnetism and paleomagnetism (time variations, diurnal to secular) – History of geophysics (solar-planetary relationships) Magnetospheric physics (storms and substorms)

scholarly journals Rainfall prediction climatological station of Banjarbaru using arima kalman filter

2021 ◽  
Vol 2106 (1) ◽  
pp. 012003
Author(s):  
W A Mehta ◽  
Y Sukmawaty ◽  
Khairullah
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Time Series Analysis ◽  
Arima Model ◽  
Training Data ◽  
Actual Data ◽  
Model Parameters ◽  
Rainfall Prediction ◽  
Series Analysis ◽  
Parameter Values

Abstract Time series analysis is a method built in a particular time sequence for prediction. One of the models in time series analysis used for prediction is the ARIMA model introduced by Box and Jenkins. As time goes by, the ARIMA model was developed by applying algorithms, one of which was the Kalman Filter algorithm. This study aims to estimate the parameters of the ARIMA model used as the Kalman Filter’s initial value to forecast rainfall using ARIMA and ARIMA Kalman Filter. Determination of the ARIMA model is done by dividing the data into training and testing. The results obtained from the three training data have the same model, namely ARIMA (0,0,0) × (0,1,1)12 models but with different parameter values than those used as initial values for the Kalman Filter. The results obtained using the ARIMA model with Kalman Filter significantly affect the initial data of 90% training data model parameters with an RMSE value of 155,13. Then predictions are made, the results obtained by ARIMA Kalman Filter can follow the actual data, but from June to October, the prediction results cannot approach the actual data. According to events in the field, June to October is the dry season, where rainfall is deficient

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