Accuracy Of Time Series Models Research Articles

Profit Forecast Accuracy of Time Series Model - A Case Study of Associated British Foods

Accurate earnings per share (EPS) forecasting is crucial for financial decision-making. This study explores the potential of improving EPS forecasting accuracy by integrating economic lead indicators into time-series models. By incorporating macroeconomic factors like GDP growth and interest rates, the models capture the influence of the broader economic environment on a company’s financial performance. Results demonstrate that including economic lead indicators significantly enhances EPS predictability beyond traditional time-series models. This integration offers a forward-looking perspective, comprehensive analysis, and context to the forecasting process, enabling stakeholders to make more informed investment decisions and develop better strategies. Further research can investigate additional lead indicators, assess their impact in different industries, and develop hybrid forecasting models for refined EPS predictions.

Research on Temperature Prediction Based on LSTM

Temperature change has an important influence on human health and the development of all walks of life, so accurate temperature forecast is becoming more and more important. With the dramatic increase in the scale and dimension of meteorological data, it poses a new challenge to temperature prediction. In the era of big data, traditional time series forecasting methods have been difficult to deal with massive meteorological data. Temperature data is a kind of time series data with obvious non-stationary fluctuation characteristics. Modeling the temperature data can analyze the temperature change. Aiming at the problem that the prediction accuracy of time series model is not high, this paper proposes to build a temperature prediction model based on long-term and short-term memory network LSTM deep learning network, and divide the training set prediction set with Jena climate data set to evaluate the accuracy of the model. The experimental results show that the long-term memory network LSTM deep learning network performs well in the temperature prediction model.

Predictive accuracy of time series models applied to economic data: the European countries retail trade

Modeling and accurately forecasting trend and seasonal patterns of a time series is a crucial activity in economics. The main propose of this study is to evaluate and compare the performance of three traditional forecasting methods, namely the ARIMA models and their extensions, the classical decomposition time series associated with multiple linear regression models with correlated errors, and the Holt–Winters method. These methodologies are applied to retail time series from seven different European countries that present strong trend and seasonal fluctuations. In general, the results indicate that all the forecasting models somehow follow the seasonal pattern exhibited in the data. Based on mean squared error (MSE), root mean squared error (RMSE), mean absolute percentage error (MAPE), mean absolute scaled error (MASE) and U-Theil statistic, the results demonstrate the superiority of the ARIMA model over the other two forecasting approaches. Holt–Winters method also produces accurate forecasts, so it is considered a viable alternative to ARIMA. The performance of the forecasting methods in terms of coverage rates matches the results for accuracy measures.

Nowcasting domestic liquidity in the Philippines using machine learning algorithms

This study utilizes a number of algorithms used in machine learning to nowcast domestic liquidity growth in the Philippines. It employs regularization (i.e., Ridge Regression, Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (ENET)) and tree-based (i.e., Random Forest, Gradient Boosted Trees) methods in order to support the BSP’s current suite of macroeconomic models used to forecast and analyze liquidity. Hence, this study evaluates the accuracy of time series models (e.g., Autoregressive, Dynamic Factor), regularization, and tree-based methods through an expanding window. The results indicate that Ridge Regression, LASSO, ENET, Random Forest, and Gradient Boosted Trees provide better estimates than the traditional time series models, with month-ahead nowcasts yielding lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Furthermore, regularization and tree-based methods facilitate the identification of macroeconomic indicators that are significant to specify parsimonious nowcasting models.

Predictive accuracy of time series model using COVID-19 cases in Eastern Visayas

SARS-Cov-2 is a novel coronavirus strain that has not previously been associated with human infection. COVID-19 is the name given to the disease caused by SARS-Cov-2. The World Health Organization declared it a Public Health Emergency of International Concern on January 30, 2020, and was later declared a pandemic on March 11, 2020. In this paper, we used MAD, MAPE, and RMSE to measure the predictive accuracy of the forecasting models for the COVID-19 cases of the provinces/cities in the region, namely, Biliran, Eastern Samar, Northern Samar, Samar, Leyte, Southern Leyte, Tacloban City, and Ormoc City. The data for this study comes from an official COVID-19 cases dataset provided by the Regional Epidemiology & Surveillance Unit of Department of Health (DOH) – Eastern Visayas Center for Health and Development from March 1, 2020 to October 30, 2021. The dataset contains confirmed cases on a weekly basis for 87 weeks (79 weeks were used for forecasting and 8 weeks were used to determine the predictive accuracy). From the results, it has been found out that ARIMA models can be highly reliable in forecasting the cases in Eastern Visayas.

Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China

BackgroundMany studies have compared the performance of time series models in predicting pulmonary tuberculosis (PTB), but few have considered the role of meteorological factors in their prediction models. This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting PTB.MethodsWe collected the monthly reported number of PTB cases and records of six meteorological factors in three cities of China from 2005 to 2018. Based on this data, we constructed three time series models, including an autoregressive integrated moving average (ARIMA) model, the ARIMA with exogenous variables (ARIMAX) model, and a recurrent neural network (RNN) model. The ARIMAX and RNN models incorporated meteorological factors, while the ARIMA model did not. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to evaluate the performance of the models in predicting PTB cases in 2018.ResultsBoth the cross-correlation analysis and Spearman rank correlation test showed that PTB cases reported in the study areas were related to meteorological factors. The predictive performance of both the ARIMA and RNN models was improved after incorporating meteorological factors. The MAPEs of the ARIMA, ARIMAX, and RNN models were 12.54%, 11.96%, and 12.36% in Xuzhou, 15.57%, 11.16%, and 14.09% in Nantong, and 9.70%, 9.66%, and 12.50% in Wuxi, respectively. The RMSEs of the three models were 36.194, 33.956, and 34.785 in Xuzhou, 34.073, 25.884, and 31.828 in Nantong, and 19.545, 19.026, and 26.019 in Wuxi, respectively.ConclusionsOur study revealed a possible link between PTB and meteorological factors. Taking meteorological factors into consideration increased the accuracy of time series models in predicting PTB, and the ARIMAX model was superior to the ARIMA and RNN models in study settings.

Exploring the Role of Meteorological Factors in Predicting Incident Pulmonary Tuberculosis: A Time-Series Study in Eastern China

To explore whether incorporating meteorological factors can improve the performance of time series models in predicting pulmonary tuberculosis (PTB), we collected the monthly number of PTB cases and data of six meteorological factors in three cities of China from 2005 to 2018. We constructed three time-series models, including theautoregressive integrated moving average (ARIMA) model, the ARIMA with exogenous variables (ARIMAX) model, and the recurrent neural network (RNN) model. The construction of the ARIMA model did not incorporate meteorological factors, while the construction of ARIMAX and RNN models incorporated meteorological factors. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to compare the performance of models in predicting the monthly number of PTB cases in 2018. Both the cross-correlation analysis and spearman rank correlation test showed that PTB was related to meteorological factors in the three cities. Taking meteorological factors into consideration may increase the accuracy of time series models in predicting PTB. Funding Statement: This study was funded by the National Natural Science Foundation of China (81973103), National Key R&D Program of China (2017YFC0907000), Qing Lan Project of Jiangsu Province (2019), and Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). Declaration of Interests: The authors declare no conflict of interest Ethics Approval Statement: This study was approved by the ethics committee of Nanjing Medical University. The study does not include any identifiable personal information. So, there was no requirement for informed consent.

A hybrid modelling method for time series forecasting based on a linear regression model and deep learning

Time series forecasting has important theoretical significance and engineering application value. A number of studies have shown that hybrid modelling is very successful in various modelling applications, and both theoretical and empirical findings have shown that hybrid modelling is an effective method to improve the accuracy of time series models. This paper proposes a hybrid model that combines a linear regression (LR) model and deep belief network (DBN) model for the prediction of time series data. In the hybrid model, the linear AR (auto-regression) LR model or ARIMA (auto-regressive integrated moving average) model and the nonlinear DBN model are explored to capture the linear and nonlinear behaviours of a time series, respectively. We first use an LR model to fit the original data and obtain the LR model residuals between the original data and the predicted data of the LR model. Then, the residuals are regarded as the nonlinear component and are used as inputs into the DBN model. The LR model prediction and the output of the DBN model are the final forecasting value for the time series, which takes full advantage of the two models for predicting time series. The proposed hybrid model and other existing models are applied to four well-known time series for comparison, and the results show that the proposed hybrid model has a high prediction accuracy and may be a useful tool for time series forecasting.

Climate Factors Improve Accuracy of Time Series Model for Dengue Hemorrhagic Fever Forecasting in Pasuruan Regency, Indonesia

Climate Factors Improve Accuracy of Time Series Model for Dengue Hemorrhagic Fever Forecasting in Pasuruan Regency, Indonesia

Investigation of the performance and accuracy of multivariate timeseries models in predicting EC and TDS values of the rivers of Urmia Lake Basin

Considering the complexity of hydrological processes, it seems that multivariate methods may enhance the accuracy of time series models and the results obtained from them by taking more influential factors into account. Indeed, the results of multivariate models can improve the results of description, modeling, and prediction of different parameters by involving other influential factors. In this study, univariate models (ARMA) and auto-correlated multivariate models with the simultaneous autoregressive moving average model (CARMA) were evaluated for modeling Electrical Conductivity and Total Dissolved Solid parameters of the western stations of Urmia Lake Basin. To use the CARMA models, annual flow rate time series, EC, TDS, SAR, and pH values measured across seventeen hydrometric stations between 1992 and 2013 were used. In the studied statistical period, the river flow in the west of Urmia Lake Basin decreased and experienced an incremental increase compared to the EC and TDS values in river flow. By applying influential parameters in CARMA models, the mean error value of the model in training and experimental stages reduces by 32% and 44% for EC values and 34% and 36% for TDS values, respectively.

The effect of temporal aggregation on the estimation accuracy of time series models

ABSTRACTIn time series analysis, Autoregressive Moving Average (ARMA) models play a central role. Because of the importance of parameter estimation in ARMA modeling and since it is based on aggregate time series so often, we analyze the effect of temporal aggregation on estimation accuracy. We derive the relationships between the aggregate and the basic parameters and compute the actual values of the former from those of the latter in order to measure and compare their estimation accuracy. We run a simulation experiment that shows that aggregation seriously worsens estimation accuracy and that the impact increases with the order of aggregation.

A Review of Soft Computing Techniques for Time Series Forecasting

Objectives: This paper gives a brief survey analysis on time series forecasting models developed so far and how soft computing techniques are becoming popular for time series forecasting. Statistical Analysis: This paper gives an introductory study on time series models develop in literature so far. First all traditional models were developed among them ARIMA became popular but only for linear data. Then after Soft Computing models came into picture like ANN and it was proved to be best for nonlinear forecasting. But none of the individual models are capable to handle all types of datasets as real data is a mixture of linear and nonlinear both. So there arises a need for hybridization. Findings: Many traditional methods have been developed since last few decades for time series forecasting, however their performance is not up to the mark till today. Recent trends have proven that soft computing techniques like neural network, support vector machine are good alternatives to conventional methods. The accuracy of time series model is very important and difficult task to achieve, and individual models are not able to perform well for all types of time series data. So, hybridization of models is better to achieve good accurate forecasting results. Application/Improvements: Implementation of a hybrid model using soft computing techniques which can give better accurate results as real data are made linear and nonlinear both. This hybrid model can be used in applications like weather forecasting, exchange rate forecasting, etc.

Power-weighted densities for time series data

While time series prediction is an important, actively studied problem, the predictive accuracy of time series models is complicated by nonstationarity. We develop a fast and effective approach to allow for nonstationarity in the parameters of a chosen time series model. In our power-weighted density (PWD) approach, observations in the distant past are down-weighted in the likelihood function relative to more recent observations, while still giving the practitioner control over the choice of data model. One of the most popular nonstationary techniques in the academic finance community, rolling window estimation, is a special case of our PWD approach. Our PWD framework is a simpler alternative compared to popular state–space methods that explicitly model the evolution of an underlying state vector. We demonstrate the benefits of our PWD approach in terms of predictive performance compared to both stationary models and alternative nonstationary methods. In a financial application to thirty industry portfolios, our PWD method has a significantly favorable predictive performance and draws a number of substantive conclusions about the evolution of the coefficients and the importance of market factors over time.

The Benefits of Bagging for Forecast Models of Realized Volatility

This article shows that bagging can improve the forecast accuracy of time series models for realized volatility. We consider 23 stocks from the Dow Jones Industrial Average over the sample period 1995 to 2005 and employ two different forecast models, a log-linear specification in the spirit of the heterogeneous autoregressive model and a nonlinear specification with logistic transitions. Both forecast model types benefit from bagging, in particular in the 1990s part of our sample. The log-linear specification shows larger improvements than the nonlinear model. Bagging the log-linear model yields the highest forecast accuracy on our sample.

Automatic prediction of platelet utilization by time series analysis in a large tertiary care hospital.

The authors investigated the potential of various time series models to aid in the prediction of platelet utilization patterns at the authors' institution. During a seven-week feasibility study, prediction accuracy of time series models was slightly inferior to that of an experienced platelet transfusion coordinator. The models that adjusted for day-of-the-week variability were superior to simpler models in a prospective evaluation over a three-month period. Conversion to a time series model for platelet utilization prediction has resulted in significant savings in personnel costs for management of platelet concentrate inventory in the blood bank. Outdate rates have been lower than those achieved by the platelet transfusion coordinator, with no sacrifice in availability occurring during the evaluation period.