Time Series Analysis: Univariate and Multivariate Methods.

Hillmer, Steven C.; Wei, William W. S.

doi:10.2307/2289741

Cited by 67 publications

(118 citation statements)

References 2 publications

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“…If the significance of ACF or PACF plots decays very slowly then the data is non-stationary in mean and differencing process must be applied. When the data has satisfied the stationary condition, both in variance and mean, model identification can be done by looking at the plot of ACF and PACF to determine the ARIMA order as described by Wei (2006). The next process is model estimation.…”

Section: Arima Modelmentioning

confidence: 99%

“…the Autoregressive Integrated Moving Average (ARIMA) method (Box & Jenkins, 1976) and multivariate method, i.e. the Transfer Function (Montgomery & Weatherby, 1980;Box et al 1994;Wei, 2006) and Vector Autoregressive (VAR) method (Sims, 1980;Lutkepohl, 1993). By using multivariate methods, the forecasting accuracy for all three macroeconomic variables is expected to be improved as a result of Thomakos & Guerard (2004) and Stephani et al (2015) that performance of multivariate methods, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Arima, Transfer Function and Var Models for Forecasting Cpi, Stock Prices, and Indonesian Exchange Rate: Accuracy vs. Explainability

Purwa

Nafngiyana

Suhartono

2020

Medstat

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The Consumer Price Index (CPI), stock prices and the rupiah exchange rate to the US dollar are important macroeconomic variables which their movements show the economic performance and can affect the monetary and fiscal policies of Indonesia. This makes forecasting effort of these variables become important for policy planning. While many previous studies only focus on examining the effect among macroeconomic variables, this study uses ARIMA (univariate method), transfer function and VAR (multivariate methods) to measure the forecasting accuracy and also observing the effect between these macroeconomic variables. The results showed that the multivariate methods gave better explanation about the relationship between variables than the simple one. Otherwise, the results of accuracy comparison showed that the multivariate methods did not always yield better forecast than the simple one, and these conditions in line with the results and conclusions of M3 and M4 competition.

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Section: Arima Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of Arima, Transfer Function and Var Models for Forecasting Cpi, Stock Prices, and Indonesian Exchange Rate: Accuracy vs. Explainability

Purwa

Nafngiyana

Suhartono

2020

Medstat

View full text Add to dashboard Cite

show abstract

“…Multivariate time-series models may be expected to generate accurate forecasts. However, the univariate forecasting models may considerably outperform the multivariate models in certain conditions [ 38 ], such as scenarios when the prediction steps were small [ 10 ]. However, some researchers think that the univariate models performed similarly to the multivariate models [ 39 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Where y i is the observed value, and y r is the mean. The lag value is better taken when AIC is small [ 38 ]. The model comprising the time series of the EV sales and all other variables established as follows.…”

Section: Time Series Models For Sales Forecastmentioning

confidence: 99%

Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China

et al. 2017

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The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry.

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“…Many research teams have worked on influenza forecasting for a long time. Among the models used by researchers, the autoregressive integrated moving average (ARIMA) model is a methodology that is often chosen for seasonal influenza forecasts because of its advantage in dealing with time-series data [ 8 , 9 ], its satisfactory performance using data that are time dependent for short-term projection, and its widespread use in other health-related forecasting tasks [ 9 - 14 ]. Decision tree–based machine learning algorithms such as random forest and extreme gradient boosting also have their strengths in predictive analysis and forecasting, which has been shown in data science competitions such as Kaggle [ 15 ], influenza outbreaks [ 14 ], and foodborne disease trends [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Applying Machine Learning Models with An Ensemble Approach for Accurate Real-Time Influenza Forecasting in Taiwan: Development and Validation Study

Cheng¹,

Lin³

et al. 2020

J Med Internet Res

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Background Changeful seasonal influenza activity in subtropical areas such as Taiwan causes problems in epidemic preparedness. The Taiwan Centers for Disease Control has maintained real-time national influenza surveillance systems since 2004. Except for timely monitoring, epidemic forecasting using the national influenza surveillance data can provide pivotal information for public health response. Objective We aimed to develop predictive models using machine learning to provide real-time influenza-like illness forecasts. Methods Using surveillance data of influenza-like illness visits from emergency departments (from the Real-Time Outbreak and Disease Surveillance System), outpatient departments (from the National Health Insurance database), and the records of patients with severe influenza with complications (from the National Notifiable Disease Surveillance System), we developed 4 machine learning models (autoregressive integrated moving average, random forest, support vector regression, and extreme gradient boosting) to produce weekly influenza-like illness predictions for a given week and 3 subsequent weeks. We established a framework of the machine learning models and used an ensemble approach called stacking to integrate these predictions. We trained the models using historical data from 2008-2014. We evaluated their predictive ability during 2015-2017 for each of the 4-week time periods using Pearson correlation, mean absolute percentage error (MAPE), and hit rate of trend prediction. A dashboard website was built to visualize the forecasts, and the results of real-world implementation of this forecasting framework in 2018 were evaluated using the same metrics. Results All models could accurately predict the timing and magnitudes of the seasonal peaks in the then-current week (nowcast) (ρ=0.802-0.965; MAPE: 5.2%-9.2%; hit rate: 0.577-0.756), 1-week (ρ=0.803-0.918; MAPE: 8.3%-11.8%; hit rate: 0.643-0.747), 2-week (ρ=0.783-0.867; MAPE: 10.1%-15.3%; hit rate: 0.669-0.734), and 3-week forecasts (ρ=0.676-0.801; MAPE: 12.0%-18.9%; hit rate: 0.643-0.786), especially the ensemble model. In real-world implementation in 2018, the forecasting performance was still accurate in nowcasts (ρ=0.875-0.969; MAPE: 5.3%-8.0%; hit rate: 0.582-0.782) and remained satisfactory in 3-week forecasts (ρ=0.721-0.908; MAPE: 7.6%-13.5%; hit rate: 0.596-0.904). Conclusions This machine learning and ensemble approach can make accurate, real-time influenza-like illness forecasts for a 4-week period, and thus, facilitate decision making.

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Time Series Analysis: Univariate and Multivariate Methods.

Cited by 67 publications

References 2 publications

Comparison of Arima, Transfer Function and Var Models for Forecasting Cpi, Stock Prices, and Indonesian Exchange Rate: Accuracy vs. Explainability

Comparison of Arima, Transfer Function and Var Models for Forecasting Cpi, Stock Prices, and Indonesian Exchange Rate: Accuracy vs. Explainability

Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China

Applying Machine Learning Models with An Ensemble Approach for Accurate Real-Time Influenza Forecasting in Taiwan: Development and Validation Study

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