scholarly journals Growth Model and Forecasting Prices of Some Agricultural Products in Bangladesh

2021 ◽  
Vol 26 (2) ◽  
pp. 64-71
Author(s):  
Md Hossain
Keyword(s):  
Time Series ◽  
Growth Model ◽  
Selection Criteria ◽  
Time Series Data ◽  
Linear Trend ◽  
Series Data ◽  
Percentage Error ◽  
Absolute Deviation ◽  
Trend Model ◽  
Exponential Method

The aim of this paper was to explore the appropriate deterministic time series model using the latest selection criteria considering the price pattern of onion, garlic and potato products in Bangladesh (January 2000 to December 2016). It appeared from our analysis that the time series data for the prices of potato was first order homogenous stationary but onion and garlic were found to be the second order stationary. Four different forecasting models namely, linear trend model, quadratic trend model, exponential growth model, and S-curve trend model were used to find the best fitted model for the prices of above mentioned products in the Bangladesh. Three accuracy measures such as mean absolute percentage error (MAPE), mean absolute deviation (MAD) and mean squared deviation (MSD) were used for the selection of the best fitted model based on lowest value of forecasting error. Lowest values of these errors indicated a best fitted model. After choosing the best growth model by the latest model selection criteria, prices of selected agricultural commodities were forecasted using the following time-series analysis methods: Simple Exponential Method, Double Exponential Method using the time period from January 2017 to December 2021. The findings of this study would be useful for policy makers, researchers, businessmen as well as producers in order to forecast future prices of these commodities.

scholarly journals Penanganan Pencilan pada Peramalan Data Deret Waktu Menggunakan Metode Pemulusan Holt dan Robust Holt

2021 ◽  
Vol 10 (2) ◽  
pp. 112-128
Author(s):  
Septanti Kusuma Dwi Arini ◽  
Farit Mochamad Afendi ◽  
Pika Silvianti
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Linear Trend ◽  
Smoothing Method ◽  
Training Data ◽  
Series Data ◽  
Data Series ◽  
Absolute Deviation ◽  
Trend Data ◽  
Time Series Simulation

The time series data used is time series data following the LLTM (local linear trend model) model with four different error conditions. These conditions are Clean Data (CD), Symmetric Outliers (SO), Asymmetric Outliers (AO) and Fat-tailed data (FT). The time series data contains symmetric and asymmetric outliers that can affect forecasting. The forecasting method used for the trend data pattern is the Holt smoothing method. The forecasting of the data series when it is spinning using the Holt smoothing method is not good enough so that it requires a handler with the smoothing method of Holt robustness. The Holt robustness smoothing method that is carried out on time series simulation data is better used for the condition of scattered data compared to the Holt smoothing method. This is indicated by the value of evaluating the goodness of the method, namely the value of MAD (Mean Absolute Deviation) produced. The smaller MAD value for CD condition training data is the Holt smoothing method, while the data testing method for Holt and robust Holt smoothing is almost comparable. SO's condition for training data and data testing for smaller MAD values is the smoothing method of robust Holt. The condition of AO for training data and data testing for smaller MAD values is the smoothing method of robust Holt. In addition, the MAD value in FT conditions for training data and data testing found almost comparable results between the two methods.

scholarly journals Research of Forecasting on Tourist Arrivals to Malaysia

2019 ◽  
Vol 8 (12S2) ◽  
pp. 686-689
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Linear Trend ◽  
Study Data ◽  
Series Data ◽  
Data Series ◽  
Suitable Model ◽  
Absolute Deviation ◽  
Double Exponential Smoothing ◽  
High Level

Tourists get attracted towards Malaysia because of our culture and geography. Apart from heritage and culture, the tourists from all over the world visit here for various purpose. Therefore, forecasting tourist arrivals with high level of accuracy becomes important because it can ensure the development of tourism industries. So, this study focuses on tourist arrivals to Malaysia. This paper attempts to define the component of patterns exist in the time series data, to determine the most suitable model best fits in data series by using the error measure that are Mean Square Error (MSE) and Mean Absolute Deviation (MAD) and to forecast the one-step ahead forecast on the best model. In this study, data of tourist arrivals to Malaysia has been obtained from January 2000 until December 2018. All 228 monthly data were analyzed by using selected Univariate Modeling. The result found that tourist arrivals to Malaysia has a linear trend model and Double Exponential Smoothing with α = 0.17 was the best model for this time series.

scholarly journals Distance variable improvement of time-series big data stream evaluation

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ari Wibisono ◽  
Petrus Mursanto ◽  
Jihan Adibah ◽  
Wendy D. W. T. Bayu ◽  
May Iffah Rizki ◽  
...  
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Standard Method ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Traffic Demand ◽  
Incremental Model ◽  
Chernoff Bound ◽  
The Mean

Abstract Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. The standard FIMT-DD algorithm uses the Hoeffding bound as its splitting criterion. We propose the further use of the mean distance and standard deviation, which are used to split a tree more accurately than the standard method. We verify our proposed method using the large Traffic Demand Dataset, which consists of 4,000,000 instances; Tennet’s big wind power plant dataset, which consists of 435,268 instances; and a road weather dataset, which consists of 30,000,000 instances. The results show that our proposed FIMT-DD algorithm improves the accuracy compared to the standard method and Chernoff bound approach. The measured errors demonstrate that our approach results in a lower Mean Absolute Percentage Error (MAPE) in every stage of learning by approximately 2.49% compared with the Chernoff Bound method and 19.65% compared with the standard method.

scholarly journals An improved framework to predict river flow time series data

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7183 ◽  
Author(s):  
Hafiza Mamona Nazir ◽  
Ijaz Hussain ◽  
Ishfaq Ahmad ◽  
Muhammad Faisal ◽  
Ibrahim M. Almanjahie
Keyword(s):  
Time Series ◽  
Water Resource Management ◽  
River Flow ◽  
Time Series Data ◽  
Flow Time ◽  
Indus Basin ◽  
Series Data ◽  
Percentage Error ◽  
Empirical Bayesian ◽  
Noise Characteristics

Due to non-stationary and noise characteristics of river flow time series data, some pre-processing methods are adopted to address the multi-scale and noise complexity. In this paper, we proposed an improved framework comprising Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Empirical Bayesian Threshold (CEEMDAN-EBT). The CEEMDAN-EBT is employed to decompose non-stationary river flow time series data into Intrinsic Mode Functions (IMFs). The derived IMFs are divided into two parts; noise-dominant IMFs and noise-free IMFs. Firstly, the noise-dominant IMFs are denoised using empirical Bayesian threshold to integrate the noises and sparsities of IMFs. Secondly, the denoised IMF’s and noise free IMF’s are further used as inputs in data-driven and simple stochastic models respectively to predict the river flow time series data. Finally, the predicted IMF’s are aggregated to get the final prediction. The proposed framework is illustrated by using four rivers of the Indus Basin System. The prediction performance is compared with Mean Square Error, Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Our proposed method, CEEMDAN-EBT-MM, produced the smallest MAPE for all four case studies as compared with other methods. This suggests that our proposed hybrid model can be used as an efficient tool for providing the reliable prediction of non-stationary and noisy time series data to policymakers such as for planning power generation and water resource management.

scholarly journals STL-ATTLSTM: Vegetable Price Forecasting Using STL and Attention Mechanism-Based LSTM

Agriculture ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 612
Author(s):  
Helin Yin ◽  
Dong Jin ◽  
Yeong Hyeon Gu ◽  
Chang Jin Park ◽  
Sang Keun Han ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Crop Production ◽  
Time Series Data ◽  
Short Term Memory ◽  
Attention Mechanism ◽  
Series Data ◽  
Percentage Error ◽  
Hot Pepper ◽  
Types Of Information

It is difficult to forecast vegetable prices because they are affected by numerous factors, such as weather and crop production, and the time-series data have strong non-linear and non-stationary characteristics. To address these issues, we propose the STL-ATTLSTM (STL-Attention-based LSTM) model, which integrates the seasonal trend decomposition using the Loess (STL) preprocessing method and attention mechanism based on long short-term memory (LSTM). The proposed STL-ATTLSTM forecasts monthly vegetable prices using various types of information, such as vegetable prices, weather information of the main production areas, and market trading volumes. The STL method decomposes time-series vegetable price data into trend, seasonality, and remainder components. It uses the remainder component by removing the trend and seasonality components. In the model training process, attention weights are assigned to all input variables; thus, the model’s prediction performance is improved by focusing on the variables that affect the prediction results. The proposed STL-ATTLSTM was applied to five crops, namely cabbage, radish, onion, hot pepper, and garlic, and its performance was compared to three benchmark models (i.e., LSTM, attention LSTM, and STL-LSTM). The performance results show that the LSTM model combined with the STL method (STL-LSTM) achieved a 12% higher prediction accuracy than the attention LSTM model that did not use the STL method and solved the prediction lag arising from high seasonality. The attention LSTM model improved the prediction accuracy by approximately 4% to 5% compared to the LSTM model. The STL-ATTLSTM model achieved the best performance, with an average root mean square error (RMSE) of 380, and an average mean absolute percentage error (MAPE) of 7%.

scholarly journals Assessing the Impact of Satellite Revisit Rate on Estimation of Corn Phenological Transition Timing through Shape Model Fitting

2019 ◽  
Vol 11 (21) ◽  
pp. 2558 ◽  
Author(s):  
Emily Myers ◽  
John Kerekes ◽  
Craig Daughtry ◽  
Andrew Russ
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Model Fitting ◽  
Series Data ◽  
Sigmoid Function ◽  
Absolute Deviation ◽  
Shape Model ◽  
Area Index ◽  
The Impact

Agricultural monitoring is an important application of earth-observing satellite systems. In particular, image time-series data are often fit to functions called shape models that are used to derive phenological transition dates or predict yield. This paper aimed to investigate the impact of imaging frequency on model fitting and estimation of corn phenological transition timing. Images (PlanetScope 4-band surface reflectance) and in situ measurements (Soil Plant Analysis Development (SPAD) and leaf area index (LAI)) were collected over a corn field in the mid-Atlantic during the 2018 growing season. Correlation was performed between candidate vegetation indices and SPAD and LAI measurements. The Normalized Difference Vegetation Index (NDVI) was chosen for shape model fitting based on the ground truth correlation and initial fitting results. Plot-average NDVI time-series were cleaned and fit to an asymmetric double sigmoid function, from which the day of year (DOY) of six different function parameters were extracted. These points were related to ground-measured phenological stages. New time-series were then created by removing images from the original time-series, so that average temporal spacing between images ranged from 3 to 24 days. Fitting was performed on the resampled time-series, and phenological transition dates were recalculated. Average range of estimated dates increased by 1 day and average absolute deviation between dates estimated from original and resampled time-series data increased by 1/3 of a day for every day of increase in average revisit interval. In the context of this study, higher imaging frequency led to greater precision in estimates of shape model fitting parameters used to estimate corn phenological transition timing.

PERBANDINGAN FUZZY TIME SERIES DENGAN METODE CHEN DAN METODE S. R. SINGH (Studi Kasus : Nilai Impor di Jawa Tengah Periode Januari 2014 – Desember 2019)

2020 ◽  
Vol 9 (3) ◽  
pp. 306-315
Author(s):  
Febyani Rachim ◽  
Tarno Tarno ◽  
Sugito Sugito
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Percentage Error ◽  
Series Method ◽  
Fuzzy Variables ◽  
Central Java ◽  
Time Series Method ◽  
Fuzzy Logical

Import is one of the efforts of an area to meet the needs of its population in order to stabilize prices and maintain stock availability. The value of imports in Central Java throughout 2016 amounted to 8811.05 Million US Dollars. The value of imports in Central Java is the top 10 in all provinces in Indonesia with a percentage of 6.50%. Import data in Central Java is included in the time series data category. To maintain the stability of imports in Central Java, it is deemed necessary to make a plan based on a statistical model. One of the time series models that can be applied is the fuzzy time series model with the Chen method approach and the S. R. Singh method because the method is suitable for cyclical patterned data with monthly time periods such as Import data in Central Java. Important concepts in the preparation of the model are fuzzy sets, membership functions, set basic operators, fuzzy variables, universe sets and domains. The fuzzy time series modeling procedure is carried out through several stages, namely the determination of universe discourse which is divided into several intervals, then defines the fuzzy set so that it can be performed fuzzification. After that the fuzzy logical relations and fuzzy logical group relations are determined. The accuracy calculation in both methods uses symmetric Mean Absolute Percentage Error (sMAPE). In this study the sMAPE value obtained in the Fuzzy Time Series Chen method of 10.95% means that it shows good forecasting ability. While the sMAPE value on the Fuzzy Time Series method of S. R. Singh method by 5.50% shows very good forecasting ability. It can be concluded that the sMAPE value in the S. R. Singh fuzzy time series method is better than the Chen method.Keywords: Import value, fuzzy time series , Chen, S. R. Singh, sMAPE

scholarly journals Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network

2017 ◽  
Vol 145 (6) ◽  
pp. 1118-1129 ◽  
Author(s):  
K. W. WANG ◽  
C. DENG ◽  
J. P. LI ◽  
Y. Y. ZHANG ◽  
X. Y. LI ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Prevention And Control ◽  
Time Series Data ◽  
Arima Model ◽  
Public Health Problem ◽  
Series Data ◽  
Percentage Error ◽  
And Control

SUMMARYTuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.

scholarly journals System for Prediction of Non Stationary Time Series based on the Wavelet Radial Bases Function Neural Network Model

2018 ◽  
Vol 8 (4) ◽  
pp. 2327 ◽  
Author(s):  
Heni Kusdarwati ◽  
Samingun Handoyo
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Linear Trend ◽  
Stationary Time Series ◽  
Series Data ◽  
Training Time ◽  
Stationary Time ◽  
Computation Process ◽  
Smooth Coefficients

This paper proposes and examines the performance of a hybrid model called the wavelet radial bases function neural networks (WRBFNN). The model will be compared its performance with the wavelet feed forward neural networks (WFFN model by developing a prediction or forecasting system that considers two types of input formats: input9 and input17, and also considers 4 types of non-stationary time series data. The MODWT transform is used to generate wavelet and smooth coefficients, in which several elements of both coefficients are chosen in a particular way to serve as inputs to the NN model in both RBFNN and FFNN models. The performance of both WRBFNN and WFFNN models is evaluated by using MAPE and MSE value indicators, while the computation process of the two models is compared using two indicators, many epoch, and length of training. In stationary benchmark data, all models have a performance with very high accuracy. The WRBFNN9 model is the most superior model in nonstationary data containing linear trend elements, while the WFFNN17 model performs best on non-stationary data with the non-linear trend and seasonal elements. In terms of speed in computing, the WRBFNN model is superior with a much smaller number of epochs and much shorter training time.

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