Using Naave Forecasts to Assess Limits to Forecast Accuracy and the Quality of Fit of Forecasts to Time Series Data

2014 ◽  
Author(s):  
Paul Goodwin
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Forecast Accuracy ◽  
Series Data ◽  
Quality Of Fit

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 Inferring phage-bacteria infection networks from time series data

2016 ◽  
Author(s):  
Luis F. Jover ◽  
Justin Romberg ◽  
Joshua S. Weitz
Keyword(s):  
Time Series ◽  
Time Course ◽  
Community Dynamics ◽  
Time Series Data ◽  
Plaque Assay ◽  
Interaction Network ◽  
Series Data ◽  
Factors Affecting ◽  
Evolutionary Changes

In communities with bacterial viruses (phage) and bacteria, the phage-bacteria infection network establishes which virus types infects which host types. The structure of the infection network is a key element in understanding community dynamics. Yet, this infection network is often difficult to ascertain. Introduced over 60 years ago, the plaque assay remains the gold-standard for establishing who infects whom in a community. This culture-based approach does not scale to environmental samples with increased levels of phage and bacterial diversity, much of which is currently unculturable. Here, we propose an alternative method of inferring phage-bacteria infection networks. This method uses time series data of fluctuating population densities to estimate the complete interaction network without having to test each phage-bacteria pair individually. We use in silico experiments to analyze the factors affecting the quality of network reconstruction and find robust regimes where accurate reconstructions are possible. In addition, we present a multi-experiment approach where time series from different experiments are combined to improve estimates of the infection network and mitigate against the possibility of evolutionary changes to infection during the time-course of measurement.

scholarly journals Persepsi Kualitas Mahasiswa Program Studi DIII Akuntansi Polines Selama Praktek Kerja Lapangan (PKL) di Industri

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Moh.Hasanudin Marliyati ◽  
Sri Murtini ◽  
Resi Yudhaningsih ◽  
Retno Retno
Keyword(s):  
Information Technology ◽  
Time Series ◽  
Communication Skills ◽  
Time Series Data ◽  
Soft Skills ◽  
Series Data ◽  
Central Java ◽  
Hard Skills ◽  
Main Components

<p>This research aimed at exploring the quality of accounting diploma <br />students during their internship program in industries. The term of student’s <br />quality described in this research isexplained using 5 main components as follows: (1) communication skills (2) teamwork (3) independence (4) creativity (5) accounting and information technology (IT)-related skills. The research’s sample is industries where students of Diploma in Accounting of State Polytechnic of Semarang (SPS) took their intership and the students themselves whom have completed their internship program for three months in various institutions such as private enterprises, state owned enterprises, local government offices spread out around Central Java. The data on this research is time series data taken from 2015 to 2016 and was collected using questionnaires from the corresponding industries about the students competencies both hard skills and soft skills. <br />Data was scored using Likert scale, ranges from Poor (1) to Excellent (5) and <br />analyzed using statistic descriptive. The result showed that average students’ <br />quality during their internship was good. Among the 5 skills observed, the <br />corresponding industries ranked teamwork skills as the highest, followed by <br />independence, creativity, communication skills and the accounting and IT -related skills. It is expected that the result can be used for future development of Accounting Program Study of SPS.</p>

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.

scholarly journals A Comparison of Three Temporal Smoothing Algorithms to Improve Land Cover Classification: A Case Study from NEPAL

2020 ◽  
Vol 12 (18) ◽  
pp. 2888 ◽  
Author(s):  
Nishanta Khanal ◽  
Mir Abdul Matin ◽  
Kabir Uddin ◽  
Ate Poortinga ◽  
Farrukh Chishtie ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Input Image ◽  
Series Data ◽  
Data Set ◽  
Temporal Smoothing ◽  
Land Cover Data ◽  
Cover Class

Time series land cover data statistics often fluctuate abruptly due to seasonal impact and other noise in the input image. Temporal smoothing techniques are used to reduce the noise in time series data used in land cover mapping. The effects of smoothing may vary based on the smoothing method and land cover category. In this study, we compared the performance of Fourier transformation smoothing, Whittaker smoother and Linear-Fit averaging smoother on Landsat 5, 7 and 8 based yearly composites to classify land cover in Province No. 1 of Nepal. The performance of each smoother was tested based on whether it was applied on image composites or on land cover primitives generated using the random forest machine learning method. The land cover data used in the study was from the years 2000 to 2018. Probability distribution was examined to check the quality of primitives and accuracy of the final land cover maps were accessed. The best results were found for the Whittaker smoothing for stable classes and Fourier smoothing for other classes. The results also show that classification using a properly selected smoothing algorithm outperforms a classification based on its unsmoothed data set. The final land cover generated by combining the best results obtained from different smoothing approaches increased our overall land cover map accuracy from 79.18% to 83.44%. This study shows that smoothing can result in a substantial increase in the quality of the results and that the smoothing approach should be carefully considered for each land cover class.

scholarly journals Inferring phage–bacteria infection networks from time-series data

2016 ◽  
Vol 3 (11) ◽  
pp. 160654 ◽  
Author(s):  
Luis F. Jover ◽  
Justin Romberg ◽  
Joshua S. Weitz
Keyword(s):  
Time Series ◽  
Time Course ◽  
Community Dynamics ◽  
Time Series Data ◽  
Plaque Assay ◽  
Interaction Network ◽  
Series Data ◽  
Factors Affecting ◽  
Evolutionary Changes

In communities with bacterial viruses (phage) and bacteria, the phage–bacteria infection network establishes which virus types infect which host types. The structure of the infection network is a key element in understanding community dynamics. Yet, this infection network is often difficult to ascertain. Introduced over 60 years ago, the plaque assay remains the gold standard for establishing who infects whom in a community. This culture-based approach does not scale to environmental samples with increased levels of phage and bacterial diversity, much of which is currently unculturable. Here, we propose an alternative method of inferring phage–bacteria infection networks. This method uses time-series data of fluctuating population densities to estimate the complete interaction network without having to test each phage–bacteria pair individually. We use in silico experiments to analyse the factors affecting the quality of network reconstruction and find robust regimes where accurate reconstructions are possible. In addition, we present a multi-experiment approach where time series from different experiments are combined to improve estimates of the infection network. This approach also mitigates against the possibility of evolutionary changes to relevant phenotypes during the time course of measurement.

ASSESSMENT OF THE QUALITY OF TIME-SERIES DATA

Epidemiology ◽  
1996 ◽  
Vol 7 (Supplement) ◽  
pp. S69
Author(s):  
S TONG
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data

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%.

scholarly journals FORECASTING OF TEMPERATURE IN BANGLADESH BY USING SLICED FUNCTIONAL TIME SERIES (SFTS) APPROACH

2020 ◽  
Vol 5 (10) ◽  
Author(s):  
FARHANA AKTER BINA
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Prediction Intervals ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Temperature Forecast ◽  
Functional Time Series ◽  
Climate Time Series

Climate is a paradigm of a complex system and its changes are global in nature. It is an exciting challenge to predict these changes over the period of different time scales. Time series analysis is one of the most important and major tools to analyze the climate time series data. Temperature is one of the most important climatic parameter. In this research, our main aim is to conduct a study across the country to forecast temperature through a relatively new method of forecasting approach named as sliced functional time series (SFTS). The monthly forecasts were obtained along with prediction intervals. These forecasts were compared with the forecasts obtained from autoregressive integrated moving average (ARIMA) and exponential smoothing state-space (ETS) models based on the accuracy measures and the length of prediction intervals to evaluate the performance of SFTS approach. Keywords: Climate,Functional Time Series,Sliced Functional Time Series, Temperature, Forecast, Forecast Accuracy

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