Forecasting short-term subway passenger flow using Wi-Fi data: comparative analysis of advanced time-series methods

One of the most important challenges when analyzing and forecasting the time series is the stability of the series and determining components of the time series such as trend and seasonal. Exponential Smoothing methods can be thought of as peers and alternatives to Box-Jenkins ARIMA class of time series forecasting methods, but the most important aspect of the exponential smoothing approach is that the time series does not have to be stable. The study introduces reviewing and comparing a variety of Exponential Smoothing models; Simple Exponential Smoothing (SES), Holt’s Linear Exponential Smoothing or Double Exponential Smoothing (DES) and Holt Winters Algorithm or Triple Exponential Smoothing (TES). Additionally; creation temporal patterns to forecast the monthly stock returns of the Saudi Stock Index by using a variety of Exponential Smoothing models. The results of the study concluded that the Holt Winters Algorithm or triple exponential smoothing model is the best model since it produces the lowest Mean Absolute Percentage error (MAPE), Mean Absolute Deviation (MAD), and Mean Squared Deviation (MSD) values which are 4,380, 244783 compared to 4, 385, 246837 for SES, and 5, 410, 270734 for DES, and thus can be used to predict the monthly stock returns of the Saudi Stock Index.

In this study, we explore and compare various time series models to forecast OPEC oil prices, aiming to identify the most accurate model for predicting future trends. The methodology involves the application of multiple time series forecasting models, including Random Walk, Linear Trend, Quadratic Trend, Exponential Trend, S-Curve Trend, Simple Moving Average, Simple Exponential Smoothing, Brown's Linear Exponential Smoothing, Holt's Linear Exponential Smoothing, Brown's Quadratic Exponential Smoothing, and ARIMA. Each model is evaluated based on performance metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Error (ME), Mean Percentage Error (MPE), Akaike Information Criterion (AIC), Hannan-Quinn Criterion (HQC), and Bayesian Information Criterion (BIC) to determine its accuracy and reliability. Among the models tested, the ARIMA(1,1,0) model emerged as the best-performing model, providing the most accurate predictions. Using this model, we forecast OPEC oil prices for a 12-month period from July 2024 to May 2025. Our findings include detailed forecasts for each month within this period, offering valuable insights for policymakers and market participants. These results highlight the importance of selecting an appropriate time series model to improve forecasting accuracy and inform decision-making processes in the energy sector.

The main purpose of modeling time series data is for forecasting. Time series modeling can be categorized into the univariate and multivariate approach. In univariate time series modeling, there are several methods of forecasting; one of them is ARIMA. Time series modeling can also consider the exogenous variables to improve the accuracy of forecasting or to obtain a more meaningful model. Recently, machine learning methods are widely employed in various field, including time series forecasting, particularly for solving nonlinear relationship among the variables. One of the most popular machine learning methods is Support Vector Machine (SVM). SVM can solve classification and regression problem. The SVM method used in time series is called Support Vector Regression (SVR). In SVR method, one of the most important things to improve the accuracy of forecasting is input selection. One of the approaches to select input in SVR is by choosing the significant lag obtained from the classical time series analysis and expand the ARIMA model to get multiplicative lag. In addition, the data on daily sales contains seasonal and calendar variation. The models are evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The best method is the SVR method using multiplicative lag from the best ARIMA model without dummy variable.

Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. However, there have been few attempts to use these to model future snow cover dynamics. In this study, we, therefore, explore the potential of such time series to forecast the Snow Line Elevation (SLE) in the European Alps. We generate monthly SLE time series from the entire Landsat archive (1985–2021) in 43 Alpine catchments. Positive long-term SLE change rates are detected, with the highest rates (5–8 m/y) in the Western and Central Alps. We utilize this SLE dataset to implement and evaluate seven uni-variate time series modeling and forecasting approaches. The best results were achieved by Random Forests, with a Nash–Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolute Error (MAE) of 258 m, Telescope (0.76, 268 m), and seasonal ARIMA (0.75, 270 m). Since the model performance varies strongly with the input data, we developed a combined forecast based on the best-performing methods in each catchment. This approach was then used to forecast the SLE for the years 2022–2029. In the majority of the catchments, the shift of the forecast median SLE level retained the sign of the long-term trend. In cases where a deviating SLE dynamic is forecast, a discussion based on the unique properties of the catchment and past SLE dynamics is required. In the future, we expect major improvements in our SLE forecasting efforts by including external predictor variables in a multi-variate modeling approach.

Pipe material and labor costs constitute about 70% of pipeline construction costs. Pipe and labor costs are subject to considerable fluctuations over time. These fluctuations are problematic for cost estimation and bid preparation in pipeline projects, which are mostly large and long-term projects. The accurate prediction of pipe and labor costs is invaluable for cost estimators to prepare accurate bids and manage the cost contingencies. However, the existing literature does not take advantage of the leading indicators of pipeline construction cost time series to accurately forecast cost fluctuations in pipeline projects. The objective of this research is to identify the leading indicators of pipeline construction costs and develop multivariate time series models for forecasting cost fluctuations in pipeline projects. Nineteen potential leading indicators of pipe and labor costs were initially selected based on a comprehensive review of construction cost forecasting literature. The leading indicators were identified from this pool of potential leading indicators based on unit root tests and Granger causality tests. Multivariate time series models were developed based on the results of cointegration tests. Vector error correction (VEC) models were developed for the cointegrated variables, while vector autoregressive (VAR) models were developed for the non-cointegrated variables. Since multivariate time series models include information from the identified leading indicators, multivariate time series models are often expected to deliver more accurate forecasts than univariate time series models. The forecasting accuracies of multivariate time series models were compared with those of univariate time series models based on three common error measures: mean absolute prediction error (MAPE), root-mean-squared error (RMSE), and mean average error (MAE). The results show that multivariate time series models outperform univariate models for forecasting cost fluctuations in pipeline projects. The findings of this research contribute to the state of knowledge by identifying leading indicators of pipe and labor costs and developing multivariate time series models to forecast them. The multivariate time series models with leading indicators are more accurate than univariate models for forecasting cost fluctuations in pipeline projects. It is expected that the proposed multivariate time series forecasting models contribute to the enhancement of the theory and practice of pipeline construction cost forecasting and help cost engineers and investment planners to prepare more accurate bids, cost estimates, and budgets for pipeline projects.

Machine learning algorithms such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) are used for machine trading. The problem with SVM and ANN is that it is difficult to determine appropriated features for both models; moreover, it is also time consuming to perform backpropagation for ANN when a number of both features and data increase. However, machine trading is not confined only these two algorithms, but also time series models. In this study, we will employ time series models namely Autoregressive Integrated Moving Average (ARIMA) and Holt – Winters’ Exponential Smoothing (HW) as the guidance for trading since time series models only require time series as an input. We will perform time series analysis to snatch the trading opportunity in the Stock Exchange of Thailand (SET). There are fifty companies on the list of SET50 index; we will choose five amongst them to invest measured by Sharpe Ratio; the top five from this measurement will be selected as invested assets in simulated portfolio. Furthermore, the well-known portfolio optimization framework by Harry Markowitz will be used to ensure that the combination of the invested assets is located on the efficient frontier; the result from this study is favorable as the return generated by these activities outperforms market return; furthermore, manipulating time series into different time lags yields higher return and as well as combining both ARIMA and HW models to help predict stock prices also improves power of prediction of time series models. This study will help retail investor who has limited resource overthrow bias and intuition throughout investment decision process ranging from finding the stocks for investment to capturing market movement for trading opportunity.

We develop a semi‐structural model for forecasting inflation in the UK in which the New Keynesian Phillips curve (NKPC) is augmented with a time series model for marginal cost. By combining structural and time series elements we hope to reap the benefits of both approaches, namely the relatively better forecasting performance of time series models in the short run and a theory‐consistent economic interpretation of the forecast coming from the structural model. In our model we consider the hybrid version of the NKPC and use an open‐economy measure of marginal cost. The results suggest that our semi‐structural model performs better than a random‐walk forecast and most of the competing models (conventional time series models and strictly structural models) only in the short run (one quarter ahead) but it is outperformed by some of the competing models at medium and long forecast horizons (four and eight quarters ahead). In addition, the open‐economy specification of our semi‐structural model delivers more accurate forecasts than its closed‐economy alternative at all horizons. Copyright © 2014 John Wiley & Sons, Ltd.

Comparative Analysis of Time Series Models for the Prediction of Conjunctivitis Disease

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

Fuzzy time series models that have been developed have been widely applied to many applications of forecasting future stock prices or weighted indexes in the financial field. Three interesting problems have been identified in relation to the associated time series methods, as follows: (1) conventional time series models that consider single variables on associated problems only, (2) fuzzy time series models that determine the interval length of the linguistic values subjectively, and (3) selected variables that depend on personal experience and opinion subjectively. In light of the above limitations, this study constitutes a hybrid seven-step procedure that proposes three integrated fuzzy time series models that are based on fitting functions to forecast weighted indexes of the stock market. First, the proposed models employ Pearson correlation coefficients to objectively select important technical indicators. Second, this study utilizes an objective algorithm to determine the lower bound and upper bound of the universe of discourse automatically. Third, the proposed models use the spread-partition algorithm to automatically determine linguistic intervals. Finally, they combine the transformed variables to build three fuzzy time series models using the criterion of the minimal root mean square error (RMSE). Furthermore, this study provides all of the necessary justifying information for using a linear process to select the inputs for the given non-linear data. To further evaluate the performance of the proposed models, the transaction records of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) and HSI (Hang Seng Indexes) from 1998/01/03 to 2006/12/31 are used to illustrate the methodology with two experimental data sets. Chen's (Fuzzy Sets Syst. 81:311---319, 1996) model, Yu's (Physica A 349:609---624, 2005) model, support vector regression (SVR), and partial least square regression (PLSR) are used as models to be compared with the proposed model when given the same data sets. The analytical results show that the proposed models outperform the listed models under the evaluation criteria of the RMSE (in contrast to the forecasting accuracy) for forecasting a weighted stock index in both the Taiwan and Hong Kong stock markets.

This paper examines the feasibility of rule-based forecasting, a procedure that applies forecasting expertise and domain knowledge to produce forecasts according to features of the data. We developed a rule base to make annual extrapolation forecasts for economic and demographic time series. The development of the rule base drew upon protocol analyses of five experts on forecasting methods. This rule base, consisting of 99 rules, combined forecasts from four extrapolation methods (the random walk, regression, Brown's linear exponential smoothing, and Holt's exponential smoothing) according to rules using 18 features of time series. For one-year ahead ex ante forecasts of 90 annual series, the median absolute percentage error (MdAPE) for rule-based forecasting was 13% less than that from equally-weighted combined forecasts. For six-year ahead ex ante forecasts, rule-based forecasting had a MdAPE that was 42% less. The improvement in accuracy of the rule-based forecasts over equally-weighted combined forecasts was statistically significant. Rule-based forecasting was more accurate than equal-weights combining in situations involving significant trends, low uncertainty, stability, and good domain expertise.

The problem of time series classification has drawn intensive attention from the data mining community. Conventional time series model may be unsuitable for multivariate motion time series because of the large volume of the data, highly correlated dimensions and rapid growth nature. In this paper, we propose C3M, an effective classification model for motion time series classification, which consists of segmentation, dimension ranking and selection, and classification. We propose new segmentation and dimension selection scheme that reduce the storage volume but keep enough valuable information and correlation between different dimensions. Experimental results show that C3M achieves significant performance improvements in terms of both classification accuracy and execution time over conventional schemas.

Web-based predictions have an important role in economic, health and the environment issues. Prediction by using Fuzzy Time Series model has advantage in the use of historical data that classifies the set data into a blur limitation (fuzzy) compared to other conventional time series model which use data firm (crisp). As weather prediction that can give impact to humans, the prediction of pollutants PM10 concentration in the air also useful because the concentration of pollutant PM10 in the air is able to drive bad impact to humans. This research discusses about web-based prediction of PM10 pollutant concentration using Ruey Chyn Tsaur Fuzzy Time Series models. The pollutant PM10 concentrations data results obtained from the sensor will be sent to the web server and stored in an online database for further prediction using Fuzzy Time Series Ruey Chyn Tsaur models which implement re-divide the partition of interval after first partition of the universe of discourse. By using 30 data samples of pollutants PM10 concentrations in the prediction, we get the value of Mean Absolute Percentage Error (MAPE) is 0.0187%. It indicates that the Fuzzy Time Series Ruey Chyn Tsaur models is well proven to be used in predicting the pollutants PM10 concentration.

Online fuzzy time series analysis based on entropy discretization and a Fast Fourier Transform

A time series model of the monthly number of motor vehicle deaths in the United States is constructed. This model is based upon the monthly data from January 1960 to December 1973. The statistical analysis revealed a very strong seasonal variation throughout each year. This seasonality in number of deaths per month is incorporated into the forecasting model. Comparison of forecasts from the model to the actual mortality experience since January 1974 is shown to reveal a significant reduction in motor vehicle mortality. This downward shift occurred with the onset of the energy crisis and the imposition of the 55 m.p.h. highway speed limit. The mortality reduction averages 844 deaths per month for the nineteen months from January 1974 to July 1975. The seasonal variations in motor vehicle accident rates are quite substantial. The month-to-month changes are important to insurers because the liquidity requirements of auto insurers are critically important for solvency. In months of heavy loss experience, the cash flow problem must be anticipated and accommodated. Recognition of the inherent seasonality in auto insurance loss experience will ease the burden of anticipating these periods of unfavorable cash flows from underwriting. A common liquidity rule in property and liability insurance is that cash on hand should total at least ten percent of outstanding unpaid claims. When the unpaid claims vary seasonally, the cash demands must also do so. One of the aims of this study is to portray the nature of the annual growth and the seasonality of one aspect of motor vehicle loss experience. Data for the time series of the total number of motor vehicle deaths in the United States for the 168 months between January 1960 and December 1973 will be characterized with a univariate time series model. This model will be used to portray the annual growth and seasonality of this mortality experience. The monthly total number of motor vehicle deaths in the United States for this time period was obtained from the National Highway Traffic Safety Administration of the U.S. Department of Transportation, Washington, D.C. These data are presented in Table 1. The other aim of this study is to obtain comparisons between forecasted Peter M. Ellis, Ph.D., is Assistant Professor of Business Administration in Utah State University.