scholarly journals Treatment of Outlier Using Interpolation Method in Malaysia Tourist Arrivals

2018 ◽  
Vol 7 (3.7) ◽  
pp. 51
Author(s):  
Maria Elena Nor ◽  
Norsoraya Azurin Wahir ◽  
G P. Khuneswari ◽  
Mohd Saifullah Rusiman
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Performance Indicator ◽  
Interpolation Method ◽  
Spline Interpolation ◽  
Forecast Accuracy ◽  
Linear Interpolation ◽  
Negative Influence ◽  
Series Data ◽  
Missing Value

The presence of outliers is an example of aberrant data that can have huge negative influence on statistical method under the assumption of normality and it affects the estimation. This paper introduces an alternative method as outlier treatment in time series which is interpolation. It compares two interpolation methods using performance indicator. Assuming outlier as a missing value in the data allows the application of the interpolation method to interpolate the missing value, thus comparing the result using the forecast accuracy. The monthly time series data from January 1998 until December 2015 of Malaysia Tourist Arrivals were used to deal with outliers. The results found that the cubic spline interpolation method gave the best result than the linear interpolation and the improved time series data indicated better performance in forecasting rather than the original time series data of Box-Jenkins model. 

scholarly journals Artificial neural network forecasting performance with missing value imputations

2020 ◽  
Vol 9 (1) ◽  
pp. 33
Author(s):  
Nur Haizum Abd Rahman ◽  
Muhammad Hisyam Lee
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Missing Values ◽  
Time Series Data ◽  
Forecast Accuracy ◽  
Absolute Error ◽  
Series Data ◽  
Missing Value ◽  
Artificial Neural

<span lang="EN-US">This paper presents time series forecasting method in order to achieve high accuracy performance. In this study, the modern time series approach with the presence of missing values problem is developed. The artificial neural networks (ANNs) is used to forecast the future values with the missing value imputations methods used known as average, normal ratio and also the modified method. The results are validated by using mean absolute error (MAE) and root mean square error (RMSE). The result shown that by considering the right method in missing values problems can improved artificial neural network forecast accuracy. It is proven in both MAE and RMSE measurements as forecast improved from 8.75 to 4.56 and from 10.57 to 5.85 respectively. Thus, this study suggests by understanding the problem in time series data can produce accurate forecast and the correct decision making can be produced.</span>

scholarly journals Generation of Time Series Data from Octave Bandwidth SPL of Acoustic Loading Using Interpolation Method

2021 ◽  
Vol 24 (1) ◽  
pp. 1-11
Author(s):  
Eun-Su Go ◽  
In-Gul Kim ◽  
Minhyeok Jeon ◽  
Hyun-Jun Cho ◽  
Jae-Sang Park ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Interpolation Method ◽  
Series Data

scholarly journals Time series analysis of radiant heat using 75 hours VIIRS satellite day and night band nightfire data

2020 ◽  
Vol 2020 (1) ◽  
pp. 98-117
Author(s):  
Jyoti U. Devkota
Keyword(s):  
Time Series ◽  
Infrared Imaging ◽  
Time Series Data ◽  
Spline Interpolation ◽  
Radiant Heat ◽  
Series Data ◽  
Time Interval ◽  
Industrial Sites ◽  
Steel Mills ◽  
Gas Flares

Abstract The nightfires illuminated on the earth surface are caught by the satellite. These are emitted by various sources such as gas flares, biomass burning, volcanoes, and industrial sites such as steel mills. Amount of nightfires in an area is a proxy indicator of fuel consumption and CO2 emission. In this paper the behavior of radiant heat (RH) data produced by nightfire is minutely analyzed over a period of 75 hour; the geographical coordinates of energy sources generating these values are not considered. Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) satellite earth observation nightfire data were used. These 75 hours and 28252 observations time series RH (unit W) data is from 2 September 2018 to 6 September 2018. The dynamics of change in the overall behavior these data and with respect to time and irrespective of its geographical occurrence is studied and presented here. Different statistical methodologies are also used to identify hidden groups and patterns which are not obvious by remote sensing. Underlying groups and clusters are formed using Cluster Analysis and Discriminant Analysis. The behavior of RH for three consecutive days is studied with the technique Analysis of Variance. Cubic Spline Interpolation and merging has been done to create a time series data occurring at equal minute time interval. The time series data is decomposed to study the effect of various components. The behavior of this data is also analyzed in frequency domain by study of period, amplitude and the spectrum.

scholarly journals Total Imports and Its Components Influence on Inflation in Kenya

2020 ◽  
pp. 54-62
Author(s):  
Evans Ovamba Kiganda ◽  
Margaret Atieno Omondi
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Central Bank ◽  
Granger Causality ◽  
Time Series Data ◽  
Negative Influence ◽  
Variance Decomposition ◽  
Series Data ◽  
Causality Tests ◽  
Granger Causality Tests

Aim: The purpose of this study was to analyze the influence of total imports (TIMP) and its components of commercial imports (CIMP) and government imports (GIMP) on inflation in           Kenya. Study Design: Quantitative approach was employed to analyze the influence of imports on inflation in Kenya. Methodology: Monthly time series data from Central Bank of Kenya for the period 2005 to 2018 was used for analysis involving correlation analysis, variance decomposition, impulse response and Granger causality tests. Results: Results indicated that total imports and commercial imports had negative influence on inflation while government imports did not significantly influence inflation in Kenya. Unidirectional causality from total imports and commercial imports to inflation was noted while there was no causality between government imports and inflation. Conclusion: The study concluded that imports influence inflation in Kenya but commercial imports highly determined total imports influence on inflation in Kenya.

scholarly journals Vector Autoregressive X (VARX) Modeling for Indonesian Macroeconomic Indicators and Handling Different Time Variations with Cubic Spline Interpolation

2019 ◽  
pp. 175-180
Author(s):  
Ayu Septiani ◽  
I Made Sumertajaya ◽  
Muhammad Nur Aidi
Keyword(s):  
Economic Growth ◽  
Interest Rates ◽  
Time Series Data ◽  
Interpolation Method ◽  
Spline Interpolation ◽  
Cubic Spline ◽  
Series Data ◽  
Cubic Spline Interpolation ◽  
Vector Autoregressive ◽  
Time Variations

This study discusses data handling that has different time variations (for example, data available in quarterly form but the desired data is monthly) in this case the GDP variable in the quarter series, while the other five variables use monthly series, whereas in multivariate analysis the data condition must be the same, then an approach is taken to reduce monthly data from quarterly data using the interpolation method. Therefore, before conducting the VARX analysis the author interpolated GDP data from the quarter to monthly by interpolation. After the data is ready, VARX modeling of the exchange rate, economic growth (GDP), interest rates on Bank Indonesia Certificates (SBI), and inflation as endogenous variables and US interest rates (FFR) and US inflation as exogenous variables. The purpose of this study is to implement and evaluate the performance of Cubic Spline interpolation methods for time series data that have different time variations. Build VARX models and predict exchange rates, economic growth (GDP), SBI interest rates, and inflation based on US interest rates (FFR) and US inflation with the best models. Meanwhile, the interpolation method used by researchers to estimate the monthly value of the GDP variable based cubic spline interpolation. Based on the AIC value of the smallest VARX model obtained at 240.6668 so the best model obtained is the VARX (4.0) model.

scholarly journals An improved ARIMA model for hydrological simulations

2014 ◽  
Vol 1 (1) ◽  
pp. 841-876 ◽  
Author(s):  
H. R. Wang ◽  
C. Wang ◽  
X. Lin ◽  
J. Kang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Forecast Accuracy ◽  
Series Data ◽  
Monthly Data ◽  
Annual Variations ◽  
Characteristic Quantity ◽  
Improved Model

Abstract. Auto Regressive Integrated Moving Average (ARIMA) model is often used to calculate time series data formed by inter-annual variations of monthly data. However, the influence brought about by inter-monthly variations within each year is ignored. Based on the monthly data classified by clustering analysis, the characteristics of time series data are extracted. An improved ARIMA model is developed accounting for both the inter-annual and inter-monthly variation. The correlation between characteristic quantity and monthly data within each year is constructed by regression analysis first. The model can be used for predicting characteristic quantity followed by the stationary treatment for characteristic quantity time series by difference. A case study is conducted to predict the precipitation in Lanzhou precipitation station, China, using the model, and the results show that the accuracy of the improved model is significantly higher than the seasonal model, with the mean residual achieving 9.41 mm and the forecast accuracy increasing by 21%.

Application of Fuzzy Sets and Shadowed Sets in Predicting Time Series Data

2018 ◽  
pp. 297-315
Author(s):  
Mahua Bose ◽  
Kalyani Mali
Keyword(s):  
Time Series ◽  
Stock Price ◽  
Time Series Data ◽  
Forecast Accuracy ◽  
Positional Information ◽  
Data Partitioning ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Shadowed Set ◽  
Shadowed Sets

In recent years, several methods for forecasting fuzzy time series have been presented in different areas, such as stock price, student enrollments, climatology, production sector, etc. Choice of data partitioning technique is a central factor and it highly influences the forecast accuracy. In all existing works on fuzzy time series model, cluster with highest membership is used to form fuzzy logical relationships. But the position of the element within the cluster is not considered. The present study incorporates the idea of fuzzy discretization and shadowed set theory in defining intervals and uses the positional information of elements within a cluster in selection of rules for decision making. The objective of this work is to show the effect of the elements, lying outside the core area on forecast. Performance of the presented model is evaluated on standard datasets.

scholarly journals Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting

2019 ◽  
Vol 11 (3) ◽  
pp. 793 ◽  
Author(s):  
Rashad Aliyev ◽  
Sara Salehi ◽  
Rafig Aliyev
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Demand Forecasting ◽  
Forecast Accuracy ◽  
Fuzzy Rule ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Percentage Error

Receiving appropriate forecast accuracy is important in many countries’ economic activities, and developing effective and precise time series model is critical issue in tourism demand forecasting. In this paper, fuzzy rule-based system model for hotel occupancy forecasting is developed by analyzing 40 months’ time series data and applying fuzzy c-means clustering algorithm. Based on the values of root mean square error and mean absolute percentage error which are metrics for measuring forecast accuracy, it is defined that the model with 7 clusters and 4 inputs is the optimal forecasting model for hotel occupancy.

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