scholarly journals Statistical modelling of agrometeorological time series by exponential smoothing

2016 ◽  
Vol 30 (1) ◽  
pp. 57-65 ◽  
Author(s):  
Małgorzata Murat ◽  
Iwona Malinowska ◽  
Holger Hoffmann ◽  
Piotr Baranowski
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Statistical Modelling ◽  
Absolute Error ◽  
Exponential Smoothing ◽  
Growth And Yield ◽  
Climate Change Scenarios ◽  
Autocorrelation Functions ◽  
Using Data

Abstract Meteorological time series are used in modelling agrophysical processes of the soil-plant-atmosphere system which determine plant growth and yield. Additionally, long-term meteorological series are used in climate change scenarios. Such studies often require forecasting or projection of meteorological variables, eg the projection of occurrence of the extreme events. The aim of the article was to determine the most suitable exponential smoothing models to generate forecast using data on air temperature, wind speed, and precipitation time series in Jokioinen (Finland), Dikopshof (Germany), Lleida (Spain), and Lublin (Poland). These series exhibit regular additive seasonality or non-seasonality without any trend, which is confirmed by their autocorrelation functions and partial autocorrelation functions. The most suitable models were indicated by the smallest mean absolute error and the smallest root mean squared error.

Absolute and relative measures for evaluating the forecasting performance of time series models for daily streamflows

2006 ◽  
Vol 37 (3) ◽  
pp. 205-215 ◽  
Author(s):  
T. Astatkie
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Model Performance ◽  
Absolute Error ◽  
Time Series Models ◽  
Streamflow Forecasting ◽  
Daily Streamflow ◽  
Forecasting Performance ◽  
Competing Models

Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are widely used measures for evaluating the forecasting performance of time series models. Although these absolute measures can be used to compare the performance of competing models, one needs a reference to judge the goodness of the forecasts. In this paper, two relative measures, coefficient of efficiency (E) and index of agreement (d), and their modified versions (EM, EMP, dM and dMP) with desired values of closer to one are presented. These measures are illustrated by comparing the modeling ability and validation forecasting performance of a Nonlinear Additive Autoregressive with Exogenous variables (NAARX), Nested Threshold Autoregressive (NeTAR), and Multiple Nonlinear Inputs Transfer Function (MNITF) models developed for the Jökulsá eystri daily streamflow data. The results suggest that NeTAR describes the system best, and gives better 1- and 2-day ahead validation forecasts. MNITF gives better forecasts for 3-day ahead, and NeTAR and NAARX give comparable performance for 4- and 5-day ahead forecasting. The values of E and d were larger than those of the modified versions, giving a false sense of model performance, and unlike the modified versions, they decreased as forecast lead times increased. Differences among the values of these six relative measures can reveal the sensitiveness of competing models to outliers, and their potential for long-term forecasting. Accordingly, NeTAR was the least sensitive to outliers and NAARX was the most sensitive, with MNITF in between; and NAARX showed the most potential for long-term streamflow forecasting.

scholarly journals A forecasting the consumer price index using time series model

2021 ◽  
Vol 107 ◽  
pp. 10002
Author(s):  
Volodymyr Shinkarenko ◽  
Alexey Hostryk ◽  
Larysa Shynkarenko ◽  
Leonid Dolinskyi
Keyword(s):  
Time Series ◽  
Price Index ◽  
Consumer Price Index ◽  
Exponential Smoothing ◽  
Time Series Model ◽  
Initial Time ◽  
Autocorrelation Functions ◽  
Minimum Error ◽  
Initial Time Series

This article examines the behavior of the consumer price index in Ukraine for the period from January 2010 to September 2020. The characteristics of the initial time series, the analysis of autocorrelation functions made it possible to reveal the tendency of their development and the presence of annual seasonality. To model the behavior of the consumer price index and forecast for the next months, two types of models were used: the additive ARIMA*ARIMAS model, better known as the model of Box-Jenkins and the exponential smoothing model with the seasonality estimate of Holt-Winters. As a result of using the STATISTICA package, the most adequate models were built, reflecting the monthly dynamics of the consumer price index in Ukraine. The inflation forecast was carried out on the basis of the Holt-Winters model, which has a minimum error.

scholarly journals Continuous partial trends and low-frequency oscillations of time series

2005 ◽  
Vol 12 (4) ◽  
pp. 451-460 ◽  
Author(s):  
A. R. Tomé ◽  
P. M. A. Miranda
Keyword(s):  
Time Series ◽  
Low Frequency ◽  
Distributed Data ◽  
Spatially Distributed ◽  
Geophysical Time Series ◽  
Slow Evolution ◽  
Using Data ◽  
The Times ◽  
Data Subject

Abstract. This paper presents a recent methodology developed for the analysis of the slow evolution of geophysical time series. The method is based on least-squares fitting of continuous line segments to the data, subject to flexible conditions, and is able to objectively locate the times of significant change in the series tendencies. The time distribution of these breakpoints may be an important set of parameters for the analysis of the long term evolution of some geophysical data, simplifying the intercomparison between datasets and offering a new way for the analysis of time varying spatially distributed data. Several application examples, using data that is important in the context of global warming studies, are presented and briefly discussed.

scholarly journals Novel FTLRNN with Gamma Memory for Short-Term and Long-Term Predictions of Chaotic Time Series

2009 ◽  
Vol 2009 ◽  
pp. 1-21
Author(s):  
Sanjay L. Badjate ◽  
Sanjay V. Dudul
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mean Squared Error ◽  
Chaotic Time Series ◽  
Nonstationary Time Series ◽  
Medium Term ◽  
Short Term ◽  
Squared Error ◽  
Wide Range

Multistep ahead prediction of a chaotic time series is a difficult task that has attracted increasing interest in the recent years. The interest in this work is the development of nonlinear neural network models for the purpose of building multistep chaotic time series prediction. In the literature there is a wide range of different approaches but their success depends on the predicting performance of the individual methods. Also the most popular neural models are based on the statistical and traditional feed forward neural networks. But it is seen that this kind of neural model may present some disadvantages when long-term prediction is required. In this paper focused time-lagged recurrent neural network (FTLRNN) model with gamma memory is developed for different prediction horizons. It is observed that this predictor performs remarkably well for short-term predictions as well as medium-term predictions. For coupled partial differential equations generated chaotic time series such as Mackey Glass and Duffing, FTLRNN-based predictor performs consistently well for different depths of predictions ranging from short term to long term, with only slight deterioration after k is increased beyond 50. For real-world highly complex and nonstationary time series like Sunspots and Laser, though the proposed predictor does perform reasonably for short term and medium-term predictions, its prediction ability drops for long term ahead prediction. However, still this is the best possible prediction results considering the facts that these are nonstationary time series. As a matter of fact, no other NN configuration can match the performance of FTLRNN model. The authors experimented the performance of this FTLRNN model on predicting the dynamic behavior of typical Chaotic Mackey-Glass time series, Duffing time series, and two real-time chaotic time series such as monthly sunspots and laser. Static multi layer perceptron (MLP) model is also attempted and compared against the proposed model on the performance measures like mean squared error (MSE), Normalized mean squared error (NMSE), and Correlation Coefficient (r). The standard back-propagation algorithm with momentum term has been used for both the models.

scholarly journals Direction of Causality between Financial Development and Economic Growth in Turkey

2021 ◽  
pp. 22-37
Author(s):  
Cengiz Yılmaz ◽  
Banu Demirhan
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Gross Domestic Product ◽  
Financial Development ◽  
The Other ◽  
Development Indicators ◽  
Short Run ◽  
Causality Relationship ◽  
Using Data

This paper has investigated the causality relationship between financial development and economic growth in Turkey, using data from 2005:04 to 2020:03. We construct a time-series model to explore causality relationships between the variables. In the study, two indicators were used as financial development indicators: banking loans to the private sector and money supply to GDP (Gross Domestic Product). The empirical results have represented a bi-directional relationship between financial development and economic growth in the short run. On the other hand, we have not found a causality relationship in the long term.

scholarly journals Peramalan Penjualan dengan Metode Exponential Smoothing (Studi Kasus : Penjualan Bakso Kemasaan/Kiloan Rumah Bakso Bang Ipul)

2019 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Hisyam Ihsan ◽  
Rahmat Syam ◽  
Fahrul Ahmad
Keyword(s):  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Exponential Smoothing ◽  
Trial And Error ◽  
Squared Error ◽  
Forecasting Method ◽  
Operational Activities ◽  
Exponential Smoothing Method ◽  
Single Exponential

Abstrak. Peramalan penjualan memungkinkan sebuah perusahan memilih kebijakan yang optimal untuk membuat keputusan yang sesuai dan mempertahankan efisiensi dari kegiatan operasional. Rumah Bakso Bang Ipul adalah salah satu usaha yang melakukan penjualan yakni penjualan bakso kemasaan/kiloan. Oleh sebab itu,. Rumah Bakso Bang Ipul sangat memerlukan peramalan penjualan untuk meningkatkan keuntungan dan menghindari terjadinya kelebihan atau kekurangan persedian bakso kemasaan/kiloan. Penelitian ini dilakukan peramalan dengan metode exponential smoothing. Adapun parameter atau a yang digunakan dalam meramalkan penjualan adalah a = 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8, dan 0.9. Singel exponential smoothing melakukan perbandingan dalam menentukan nilai a, dengan mencari nilai a tersebut secara trial and error sampai menemukan a yang memiliki error minimum dengan pencarian menggunakan metode mean absolute error (MAE) dan metode Mean Squaered error (MSE). Sehingga dipilih a = 0.1 dengan nilai MAE = 6.23 dan nilai MSE = 58.32. berdasarkan hasil ini, dengan menggunakan metode singel exponential smoothing dan a =0.1 diperoleh hasil peramalan penjualan bakso bang ipul pada bulan juni 2018 sebanyak 48 kilogram.Kata Kunci: Peramalan, Metode Exponential Smoothing, Metode Singel Exponential SmoothingAbstract. Sales forecasting enables an optimal policy of the company had to make the appropriate decision and maintain the efficiency of operational activities. Rumah Bakso Bang Ipul is a business that sells packaged meatballs. Therefore, Rumah Bakso Bang Ipul is in need of sales forecasting to increase profit and avoid the occurrence or lack of supply of packaged meatballs. This research was conducted by the method of exponential smoothing forecasting. As for parameter or a used predicting sales is a = 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8, and 0.9. single exponential smoothing do a comparison in determining the value of a, by searching for the value of such a trial and error to find a that has minimum error with search method using the mean absolute error (MAE) and mean squared error (MSE). So that selected a = 0.1 with MAE value = 6.23 and MSE Value = 58.32. Based on  these results, using the method of single exponential smoothing and retrieved results forecasting Rumah Bakso Bang Ipul in July 2018 as much as 48 kilograms.Keywords: Forecasting, Method of exponential smoothing, Method of single exponential smoothing.

scholarly journals Forecasting incidence of tuberculosis cases in Brazil based on various univariate time-series models

2019 ◽  
Vol 7 (10) ◽  
pp. 894-909
Author(s):  
Rhuan Carlos Martins Ribeiro ◽  
Thaynara Araújo Quadros ◽  
John Jairo Saldarriaga Ausique ◽  
Otavio Andre Chase ◽  
Pedro Silvestre da Silva Campos ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series ◽  
Mean Square Error ◽  
High Performance ◽  
Moving Average ◽  
Arima Model ◽  
Public Health Problem ◽  
Absolute Error ◽  
Exponential Smoothing ◽  
Mean Square

Tuberculosis (TB) remains the world's deadliest infectious disease and is a serious public health problem. Control for this disease still presents several difficulties, requiring strategies for the execution of immediate combat and intervention actions. Given that changes through the decision-making process are guided by current information and future prognoses, it is critical that a country's public health managers rely on accurate predictions that can detect the evolving incidence phenomena. of TB. Thus, this study aims to analyze the accuracy of predictions of three univariate models based on time series of diagnosed TB cases in Brazil, from January 2001 to June 2018, in order to establish which model presents better performance. For the second half of 2018. From this, data were collected from the Department of Informatics of the Unified Health System (DATASUS), which were submitted to the methods of Simple Exponential Smoothing (SES), Holt-Winters Exponential Smoothing (HWES) and the Integrated Autoregressive Moving Average (ARIMA) model. In the performance analysis and model selection, six criteria based on precision errors were established: Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE) and Theil's U statistic (U1 and U2). According to the results obtained, the HWES (0.2, 0.1, 0.1) presented a high performance in relation to the error metrics, consisting of the best model compared to the other two methodologies compared here.

Application of Fuzzy Time Series Techniques in Wind and Wave Data Forecasting

2014 ◽  
Author(s):  
Christos N. Stefanakos ◽  
Orestis Schinas ◽  
Grim Eidnes
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Mean Squared Error ◽  
Offshore Structures ◽  
Fuzzy Time Series ◽  
Great Success ◽  
Peak Period ◽  
Wave Data ◽  
Forecasting Performance

This work explores the applicability of widely known fuzzy time series forecasting techniques for the prediction of wind and wave data. These techniques have extensively been used with great success to the forecasting of stock prices. In the present work, long-term time series of wind speed, significant wave height, and peak period are examined and used for the verification of the forecasting performance of the fuzzy models. To examine the forecasting accuracy, the root mean squared error (RMSE) is used as an evaluation criterion to compare the forecasting performance of the listing models. As the importance of quality of wind and wave data increases, effective forecasting could further benefit designers of offshore structures and environmental researchers.

Prediction of Apnea-Hypopnea Index Using Sound Data Collected by a Noncontact Device

2020 ◽  
Vol 162 (3) ◽  
pp. 392-399
Author(s):  
Jeong-Whun Kim ◽  
Taehoon Kim ◽  
Jaeyoung Shin ◽  
Kyogu Lee ◽  
Sunkyu Choi ◽  
...  
Keyword(s):  
Random Forest ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Sleep Stages ◽  
Support Vector ◽  
Apnea Hypopnea Index ◽  
Tertiary Referral Hospital ◽  
Obstructive Sleep ◽  
Using Data ◽  
Sound Features

Objective To predict the apnea-hypopnea index (AHI) in patients with obstructive sleep apnea (OSA) using data from breathing sounds recorded using a noncontact device during sleep. Study Design Prospective cohort study. Setting Tertiary referral hospital. Subject and Methods Audio recordings during sleep were performed using an air-conduction microphone during polysomnography. Breathing sounds recorded from all sleep stages were analyzed. After noise reduction preprocessing, the audio data were segmented into 5-second windows and sound features were extracted. Estimation of AHI by regression analysis was performed using a Gaussian process, support vector machine, random forest, and simple linear regression, along with 10-fold cross-validation. Results In total, 116 patients who underwent attended, in-laboratory, full-night polysomnography were included. Overall, random forest resulted in the highest performance with the highest correlation coefficient (0.83) and least mean absolute error (9.64 events/h) and root mean squared error (13.72 events/h). Other models resulted in somewhat lower but similar performances, with correlation coefficients ranging from 0.74 to 0.79. The estimated AHI tended to be underestimated as the severity of OSA increased. Regarding bias and precision, estimation performances in the severe OSA subgroup were the lowest, regardless of the model used. Among sound features, derivative of the area methods of moments of overall standard deviation demonstrated the highest correlation with AHI. Conclusion AHI was fairly predictable by using data from breathing sounds generated during sleep. The prediction model may be useful not only for prescreening but also for follow-up after treatment in patients with OSA.

scholarly journals Groundwater budget forecasting, using hybrid wavelet-ANN-GP modelling: a case study of Azarshahr Plain, East Azerbaijan, Iran

2016 ◽  
Vol 48 (2) ◽  
pp. 455-467 ◽  
Author(s):  
Alireza Docheshmeh Gorgij ◽  
Ozgur Kisi ◽  
Asghar Asghari Moghaddam
Keyword(s):  
Water Table ◽  
Mean Squared Error ◽  
Evaluation Criteria ◽  
Absolute Error ◽  
Management Plans ◽  
Thiessen Polygons ◽  
Development Plans ◽  
Groundwater Budget ◽  
East Azerbaijan

Meticulous prediction of hydrological processes, especially water budget, has an individual importance in environmental management plans. On the other hand, conservation of groundwater, a fundamental resource in arid and semi-arid areas, needs to be considered as a great priority in development plans. Prediction of a groundwater budget utilizing artificial intelligence was the scope of this study. For this aim, the Azarshahr Plain aquifer, East Azerbaijan, Iran, was selected because of its great dependence on groundwater and the necessity of cognizance of its budget in future programs. The long-term fluctuations of the water table in 13 piezometers were simulated by a wavelet-based artificial neural network (WANN) hybrid model, and their statistical gaps were covered. Then, the modelled water table was predicted for the next 12 months using genetic programming. The results of simulation and prediction were assessed by performance evaluation criteria such as R2, root mean squared error, mean absolute error and Nash–Sutcliffe efficiency. Thiessen polygons were then utilized, plotting the predicted unit hydrograph of the study area. The predicted water table from September 2012 to August 2013 revealed about 0.12 m depletion. Regarding the area of the Azarshahr Plain aquifer and its average storage coefficient, the aquifer budget will be reduced by about 0.3557 million cubic metres during this period.

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