scholarly journals Rainfall Forecasting Model Using ARIMA and Kalman Filter in Makassar, Indonesia

2021 ◽  
Vol 2123 (1) ◽  
pp. 012044
Author(s):  
Sukarna ◽  
Elma Yulia Putri Ananda ◽  
Maya Sari Wahyuni
Keyword(s):  
Kalman Filter ◽  
Moving Average ◽  
Arima Model ◽  
Rainfall Data ◽  
Percentage Error ◽  
Filter Method ◽  
Rainfall Forecasting ◽  
Forecasting Methods ◽  
Filter Methods ◽  
Kalman Filter Method

Abstract Many forecasting methods have been used for forecasting rainfall data. Kalman Filter is one of the forecasting methods that could give better forecasts. To our knowledge, the Kalman Filter method has not been used to forecast rainfall data in Makassar, Indonesia. This study aims to provide more precise forecasts for rainfall data in Makassar, Indonesia by using Autoregressive Integrated Moving Average (ARIMA) and Kalman Filter methods. Rainfall data from January 2010 to December 2020 were used. The best model selection is based on the smallest Mean Absolute Percentage Error (MAPE) value. The results showed that the best ARIMA model is ARIMA(0,1,1)(0,1,1)12 with MAPE is 111.48, while MAPE value by using the Kalman Filter algorithm is 47.00 indicating that Kalman Filter has better prediction than ARIMA model.

scholarly journals On Modelling Seasonal Arima Series: Application on Road Accidents in Northeast Algeria

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Kalman Filter ◽  
Parametric Identification ◽  
Series Data ◽  
Percentage Error ◽  
Filter Method ◽  
Paper Study ◽  
Road Accidents ◽  
Short Term ◽  
Kalman Filter Method ◽  
Short Term Prediction

This paper study and modeless a number of road accidental injuries in the region of Skikda (northeast Algeria) according to Box- Jenkins method using EViews software using series data from January 2001 to December 2016. Also, Kalman filter method is given. To this end, Kalman filter method is used for short term prediction and parametric identification purpose. The other side, a comparative study is given to compare between the two methods by de following criteria: Mean absolute percentage error (MAPE), root mean square percentage error (RMSPE) and the Theils’s U statistic. This application used Eviews 5.0 and SPSS 26 software’s.

Self-Tuning Weighted Fusion Kalman Filter for ARMA Signals

2014 ◽  
Vol 538 ◽  
pp. 439-442
Author(s):  
Wen Qiang Liu ◽  
Gui Li Tao ◽  
Na Han
Keyword(s):  
Kalman Filter ◽  
Single Channel ◽  
Moving Average ◽  
Correlation Method ◽  
Filter Method ◽  
Model Parameters ◽  
Weighted Fusion ◽  
Self Tuning ◽  
Kalman Filter Method ◽  
Noise Statistics

For the multisensor single channel autoregressive moving average (ARMA) signal with a white measurement noise and autoregressive (AR) colored measurement noises as common disturbance noises, when model parameters and noise statistics are partially unknown, a self-tuning weighted fusion Kalman filter is presented based on classical Kalman filter method. The local estimates are obtained by applying the recursive instrumental variable (RIV) and correlation method. Then the optimal weighted fusion Kalman filter is obtained by substituting all the fusion estimates into the corresponding optimal Kalman filter. A simulation example shows its effectiveness.

Reliable home error identification of a 2-DOF parallel robot based on regularization methods

2018 ◽  
Vol 233 (7) ◽  
pp. 2502-2515
Author(s):  
Jiangping Mei ◽  
Jiawei Zang ◽  
Yabin Ding
Keyword(s):  
Kalman Filter ◽  
Ridge Regression ◽  
Parallel Robot ◽  
Initial Position ◽  
Regression Method ◽  
Filter Method ◽  
Filter Methods ◽  
Curve Method ◽  
Kalman Filter Method ◽  
Active Link

This paper deals with the home error (the initial position error of active link) identification of a 2-DOF parallel robot. The identification model containing the home errors of the robot and the assembly errors of a new measuring instrument is developed using distance measurement. After that, an adaptive ridge regression method and a regularized Kalman filter method are proposed to improve the identification reliability and accuracy. Particularly, a modified L-curve method is proposed to provide suitable regularization parameters for the regularized Kalman filter. Based on the selected optimal measurement positions, experiments are carried out, in which the two regularized identification methods are compared with the ordinary ridge regression and the Kalman filter methods. Results show that the reliability and accuracy of the two methods are much better than the ordinary ridge regression method, and the divergence problem of the Kalman filter can be well resolved by the regularized Kalman filter.

scholarly journals Performance evaluation of GPS / BDS combined single-point positioning based on RTKLIB

2020 ◽  
Vol 165 ◽  
pp. 03009
Author(s):  
Li Yan-yi ◽  
Huang Jin ◽  
Tang Ming-xiu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Single Point ◽  
Least Square Method ◽  
Least Square ◽  
Filter Method ◽  
Single System ◽  
Positioning Error ◽  
Dual Mode ◽  
Kalman Filter Method

In order to evaluate the performance of GPS / BDS, RTKLIB, an open-source software of GNSS, is used in this paper. In this paper, the least square method, the weighted least square method and the extended Kalman filter method are respectively applied to BDS / GPS single system for data solution. Then, the BDS system and GPS system are used for fusion positioning and the positioning results of the two systems are compared with that of the single system. Through the comparison of experiments, on the premise of using the extended Kalman filter method for positioning, when the GPS signal is not good, BDS data is introduced for dual-mode positioning, the positioning error in e direction is reduced by 36.97%, the positioning error in U direction is reduced by 22.95%, and the spatial positioning error is reduced by 16.01%, which further reflects the advantages of dual-mode positioning in improving a system robustness and reducing the error.

Fault diagnose of the rolling bearings vibration signals based on Kalman filter method

2021 ◽  
Author(s):  
Qingpeng Han ◽  
Xinhang Shen ◽  
Bin Wu ◽  
Rui Zhu ◽  
Daolei Wang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Filter Method ◽  
Fault Diagnose ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Kalman Filter Method

Towards optimal ATM cash replenishment using time series analysis

2021 ◽  
pp. 1-13
Author(s):  
Muhammad Rafi ◽  
Mohammad Taha Wahab ◽  
Muhammad Bilal Khan ◽  
Hani Raza
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Automatic Teller Machine ◽  
Proposed Model ◽  
Auto Regressive ◽  
Specific Amount

Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs.

scholarly journals Thermal anomalies detection before strong earthquakes (<i>M</i> > 6.0) using interquartile, wavelet and Kalman filter methods

2011 ◽  
Vol 11 (4) ◽  
pp. 1099-1108 ◽  
Author(s):  
M. R. Saradjian ◽  
M. Akhoondzadeh
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Wavelet Transform ◽  
Anomaly Detection ◽  
Land Surface ◽  
Seasonal Effects ◽  
Filter Method ◽  
Strong Earthquakes ◽  
Thermal Anomalies ◽  
Filter Methods

Abstract. Thermal anomaly is known as a significant precursor of strong earthquakes, therefore Land Surface Temperature (LST) time series have been analyzed in this study to locate relevant anomalous variations prior to the Bam (26 December 2003), Zarand (22 February 2005) and Borujerd (31 March 2006) earthquakes. The duration of the three datasets which are comprised of MODIS LST images is 44, 28 and 46 days for the Bam, Zarand and Borujerd earthquakes, respectively. In order to exclude variations of LST from temperature seasonal effects, Air Temperature (AT) data derived from the meteorological stations close to the earthquakes epicenters have been taken into account. The detection of thermal anomalies has been assessed using interquartile, wavelet transform and Kalman filter methods, each presenting its own independent property in anomaly detection. The interquartile method has been used to construct the higher and lower bounds in LST data to detect disturbed states outside the bounds which might be associated with impending earthquakes. The wavelet transform method has been used to locate local maxima within each time series of LST data for identifying earthquake anomalies by a predefined threshold. Also, the prediction property of the Kalman filter has been used in the detection process of prominent LST anomalies. The results concerning the methodology indicate that the interquartile method is capable of detecting the highest intensity anomaly values, the wavelet transform is sensitive to sudden changes, and the Kalman filter method significantly detects the highest unpredictable variations of LST. The three methods detected anomalous occurrences during 1 to 20 days prior to the earthquakes showing close agreement in results found between the different applied methods on LST data in the detection of pre-seismic anomalies. The proposed method for anomaly detection was also applied on regions irrelevant to earthquakes for which no anomaly was detected, indicating that the anomalous behaviors can be related to impending earthquakes. The proposed method receives its credibility from the overall capabilities of the three integrated methods.

An Implicit Algorithm of Solving Nonlinear Filtering Problems

2014 ◽  
Vol 16 (2) ◽  
pp. 382-402
Author(s):  
Feng Bao ◽  
Yanzhao Cao ◽  
Xiaoying Han
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Nonlinear Filtering ◽  
State Equation ◽  
Nonlinear Filter ◽  
Filter Method ◽  
Simulation Methods ◽  
Current State ◽  
Numerical Simulation Methods ◽  
Kalman Filter Method

AbstractNonlinear filter problems arise in many applications such as communications and signal processing. Commonly used numerical simulation methods include Kalman filter method, particle filter method, etc. In this paper a novel numerical algorithm is constructed based on samples of the current state obtained by solving the state equation implicitly. Numerical experiments demonstrate that our algorithm is more accurate than the Kalman filter and more stable than the particle filter.

scholarly journals Autoregressive Integrated Moving Average (ARIMA) Sebagai Model Peramalan Kasus Demam Berdarah Dengue

2020 ◽  
Vol 10 (2) ◽  
pp. 76-80
Author(s):  
Roro Kushartanti ◽  
Maulina Latifah
Keyword(s):  
Community Health ◽  
Health Center ◽  
Moving Average ◽  
Arima Model ◽  
Community Health Center ◽  
Percentage Error ◽  
Dengue Case ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Method

ARIMA is a forecasting method time series that does not require a specific data pattern. This study aims to analyze the forecasting of Semarang City DHF cases specifically in the Rowosari Community Health Center. The study used monthly data on DHF cases in the Rowosari Community Health Center in 2016, 2017, and 2019 as many as 36 dengue case data. The best ARIMA model for forecasting is a model that meets the requirements for parameter significance, white noise and has the MAPE (Mean Absolute Percentage Error Smallest) value. The results of the analysis show that the best model for predicting the number of dengue cases in the Rowosari Public Health Center Semarang is the ARIMA model (1,0,0) with a MAPE value of 43.98% and a significance coefficient of 0.353, meaning that this model is suitable and feasible to be used as a forecasting model. DHF cases in the Rowosari Community Health Center in Semarang City.

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