scholarly journals Forecasting Cycles in the Transportation Sector

2010 ◽  
Author(s):  
Vincent W. Yao ◽  
Brian W. Sloboda
Keyword(s):  
Time Series ◽  
Evaluation Criteria ◽  
Time Series Models ◽  
Leading Indicators ◽  
Modern Time ◽  
General State ◽  
Growth Cycles ◽  
Screening Techniques ◽  
Transportation Sector ◽  
Timely Fashion

This paper predicted fluctuations in the transportation sector using leading indicators. From 25 initial candidates, we selected seven leading indicators, using various screening techniques and modern time series models. A composite leading index was constructed and found to perform well in predicting transportation reference cycles. The leading index signals downturns in the transportation sector 10 months ahead and upturns six months ahead on average. The index predicted the latest recession in transportation with a lead of 20 months. The analysis also confirms the predictive contents of the composite leading index (CLI) in relation to transportation growth cycles. These evaluation criteria ensure accurate forecasts of the general state of the transportation sector in a timely fashion.

scholarly journals Forecasting Major Food Crops Production in Khyber Pakhtunkhwa,Pakistan

2017 ◽  
Vol 2 (1) ◽  
pp. 21
Author(s):  
Syed Asghar Ali Shah ◽  
Nagina Zeb ◽  
Alamgir Alamgir
Keyword(s):  
Time Series ◽  
Evaluation Criteria ◽  
Secondary Data ◽  
Random Model ◽  
Forecast Evaluation ◽  
Time Series Models ◽  
Food Crops ◽  
Khyber Pakhtunkhwa ◽  
Major Food ◽  
Arima Modeling

The present study was undertaken to investigate forecasting of major food crops production in Khyber Pakhtunkhwa. The study was based on secondary data covers a period of about 30 years i.e. starting from 1984-85 to 2013-14, whereas, ARIMA modeling has been employed to fit the best time series model for major food crops production i.e. wheat, maize, sugarcane and rice. It reveals through the results that for major food crops production, the time series models which were found to be most suitable are as ARIMA (0, 2, 1), ARIMA (1, 2, 3), ARIMA (0, 2, 1) and random model ARIMA (0, 1, 0) respectively based on forecast evaluation criteria. It was concluded from the results of analyzed data that time series models were found adequate for forecasting major food crops production in Khyber Pakhtunkhwa.    

scholarly journals Machine learning methods for modelling and analysis of time series signals in geoinformatics

2021 ◽  
Author(s):  
Μαρία Κασελίμη
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Energy Consumption ◽  
Real Life ◽  
Data Availability ◽  
Time Series Models ◽  
Electron Content ◽  
Modern Time ◽  
Energy Disaggregation

The analysis of experimental data that have been observed at different points in time leads to new and unique problems in statistical modeling and inference. The obvious correlation introduced by the sampling of adjacent points in time can severely restrict the applicability of the many conventional statistical methods traditionally dependent on the assumption that these adjacent observations are independent andidentically distributed. The systematic approach by which one goes about answering the mathematical and statistical questions posed by these time correlations is commonly referred to as time series analysis (TSA).Time series modeling (TSM) plays a key role in a wide range of real-life problems that have a temporal component. Modern time series problems often pose significant challenges for the existing techniques both in terms of their complexity, structure and size. While traditional methods have focused on parametric models informed by domain expertise, modern machine learning (ML) methods provide a means to learn temporal dynamics in a purely data-driven manner. With the increasing data availability and computing power in recent times, machine learning has become a vital part of the next generation of time series models. Thus, there is both a great need and an exciting opportunity for the machine learning community to develop theory, models and algorithms specifically for the purpose of processing and analyzing time series data.The impact of time series modelling and analysis on scientific applications can be partially documented by analysing problems of various diverse fields in which important time series problems may arise. Modern time series problems are characterized by complexity. Also, since real-world systems often evolve under transient conditions, the signals/time series tend to exhibit various forms of non-stationarity. As far as mathematical models are concerned, they can be categorized in many different ways. They can be linear or non-linear, static or dynamic, continuous distinct in time, deterministic or contemplative. The proper model selection to accurately describe a system depends on the system under study, on whether the operation of the system is a-priory known or not, as well as on the purpose of the implementation. This dissertation presents developments in nonlinear and non-static time series models under a machine learning framework, comparing their performance in real-life application scenarios related to geoinformatics as well as environmental applications.In this dissertation is provided a comparative analysis that evaluates the performance of several deep learning (DL) architectures on a large number of time series datasets of different nature and for different applications. Two main fruitful research fields are discussed here which were strategically chosen in order to address current cross-disciplinary research priorities attracting the interest of geoinformatics communities. The first problem is related to ionospheric Total Electron Content (TEC) modeling which is an important issue in many real-time Global Navigation System Satellites (GNSS) applications. Reliable and fast knowledge about ionospheric variations becomes increasingly important. GNSS users of single-frequency receivers and satellite navigation systems need accurate corrections to remove signal degradation effects caused by the ionosphere. Ionospheric modeling using signal-processing techniques is the subject of discussion in the present contribution. The next problem under discussion is energy disaggregation which is an important issue for energy efficiency and energy consumption awareness. Reliable and fast knowledge about residential energy consumption at appliance level becomes increasingly important nowadays and it is an important mitigation measure to prevent energy wastage. Energy disaggregation or Non-intrusive load monitoring (NILM) is a single channel blind source separation problem where the task is to estimate the consumption of each electrical appliance given the total energy consumption. For both problems various deep learning models (DL) are proposed that cover various aspects of the problem under study, whereas experimental results indicate the proposed methods' superiority compared to the current state of the art.

scholarly journals Leading Indicators of Regional Cotton Acreage Response: Structural and Time Series Modeling Results

1999 ◽  
Vol 31 (3) ◽  
pp. 507-517 ◽  
Author(s):  
Jack E. Houston ◽  
Christopher S. McIntosh ◽  
Paul A. Stavriotis ◽  
Steve C. Turner
Keyword(s):  
Time Series ◽  
Structural Models ◽  
Time Series Models ◽  
Leading Indicators ◽  
Time Series Modeling ◽  
Cotton Yield ◽  
Cotton Production ◽  
Loan Rate ◽  
Cotton Price ◽  
Response Indicators

AbstractResurgent cotton production compels better acreage forecasts for planning seed, chemical, and other input requirements. Structural models describe leading acreage response indicators, and forecasts are compared to time-series models. Cotton price, loan rate, deficiency payments, lagged corn acreage, the PIK program, and previous cotton yield significantly influence cotton acreage response.

Time-Series Models in Marketing: Some Recent Developments

Marketing ZFP ◽  
2010 ◽  
Vol 32 (JRM 1) ◽  
pp. 24-29
Author(s):  
Marnik G. Dekimpe ◽  
Dominique M. Hanssens
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Recent Developments

A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Peninsular Malaysia ◽  
Time Series Models ◽  
Crude Palm Oil ◽  
Univariate Time Series ◽  
Import And Export ◽  
Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

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