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Time series forecasting

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Evaluating and comparing time series forecasting models for weekly fluctuations of salinity intrusion: The case of Dai estuary, Ben Tre province (Southern Vietnam)

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EVALUATING AND COMPARING TIME SERIES FORECASTING MODELS FOR WEEKLY FLUCTUATIONS OF SALINITY INTRUSION:. Based on long-time database of salinity concentration collected in Dai estuary (belonging Mekong estuary systems), a number of models were comparated in order to selecting an adequate predictive model for prediction of salinity intrusion in the study area. Our propose that ARIMA (0,1,1)x(0,1,1)23 can be applied in a part of early-warning of salinity intrusion systems..

A new hybrid method for predicting univariate and multivariate time series based on pattern forecasting

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time series based on pattern forecasting. Time series forecasting Machine learning Hybrid model. Time series forecasting has become indispensable for multiple applications and industrial processes. Abbreviation: ACF, Autocorrelation function of the Time Series. FTS, Fuzzy Time Series.

Forecasting of biodiesel prices in Thailand using time series decomposition method for long term from 2017 to 2036

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To make a long-term forecast (i.e., a 25 year-forecast from 2017 to 2036), the Time Series Decomposition Method was used based on data from the earlier period of 2007 to 2016. Time series forecasting is done based on time series data.

Empirical mode decomposition based on theta method for forecasting daily stock price

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The challenging task of time series forecasting is a very active as well as an important research area. Nowadays, there are many statistical as well as machine learning models for time series forecasting. Choosing or finding the best model for a particular or similar type of time series data is a challenging but essential consideration.

SAS/ETS 9.22 User's Guide 262

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Haver Analytics (2009), Data Link Express, Time Series Data Base Management System, New York [http://www.haver.com/]. Many people have been instrumental in the development of the ETS Interface engine. Time Series Forecasting System. Overview of the Time Series Forecasting System. 2607 Using the Time Series Forecasting System.

SAS/ETS 9.22 User's Guide 3

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ARIMA (Box-Jenkins) and ARIMAX (Box-Tiao) Modeling and Forecasting . 31 Vector Time Series Analysis. State Space Modeling and Forecasting. Structural Time Series Modeling and Forecasting. Time Series Cross-Sectional Regression Analysis. Automatic Time Series Forecasting. Time Series Interpolation and Frequency Conversion. Time Series Forecasting System. Enterprise Miner—Time Series nodes.

SAS/ETS 9.22 User's Guide 279

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If the FORECAST command or %FORECAST macro is issued without arguments, the Time Series Forecasting window appears. This is equivalent to selecting “Time Series Forecasting System” from the Analysis submenu of the Solutions menu.. Using the arguments, it is possible to do the following:. Bring up the system with information already filled into some of the fields. Bring up the system starting at a different window than the default Time Series Forecasting window.

SAS/ETS 9.22 User's Guide 266

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This returns you to the Time Series Forecasting window.. The Project field is now set to WORK.TEST.TESTPROJ, and the description is the description you previously gave to TESTPROJ, as shown in Figure 39.28.. 2644 F Chapter 39: Getting Started with Time Series Forecasting. Figure 39.28 Time Series Forecasting Window after Loading Project. If you now select the Manage Projects button, you will see the list of series and forecasting models you created in the previous forecasting session..

SAS/ETS 9.22 User's Guide 264

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2622 F Chapter 39: Getting Started with Time Series Forecasting. Figure 39.7 CITIQTR Data Set Selected. Note that the Time ID field is now set to DATE and the Interval field is set to QTR. These fields are explained in the following section.. Now select the OK button to complete selection of the CITIQTR data set. This closes the Data Set Selection window and returns to the Time Series Forecasting window, as shown in Figure 39.8.. Figure 39.8 Time Series Forecasting Window.

SAS/ETS 9.22 User's Guide 273

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The remainder of the chapter shows you how to compare models by using a variety of statistics and by controlling the fit and evaluation time ranges. The Time Series Viewer is a graphical tool for viewing and analyzing time series. It can be used separately from the Time Series Forecasting System by using the TSVIEW command or by selecting Time Series Viewer from the Analysis pull-down menu under Solutions.

SAS/ETS 9.22 User's Guide 265

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You can now fit models for other series in this data set or change to a different data set and fit models for series in the new data set.. Select the Close button to return to the Time Series Forecasting window.. Produce Forecasts Window. Now that you have forecasting models for these three series, you are ready to produce forecasts. Select the Produce Forecasts button. This opens the Produce Forecasts window, as shown in Figure 39.17.. Figure 39.17 Produce Forecasts Window.

A hybrid least squares support vector machine with bat and CUCKOO search algorithms for time series forecasting

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Least Squares Support Vector Machine (LSSVM) has been known to be one of the effective forecasting models. BAT-LSSVM and CUCKOO-LSSVM generated lower MAPE and SMAPE, at the same time produced higher accuracy and fitness value compared to particle swarm optimization (PSO)- LSSVM and a non-optimized LSSVM. Miller, 1986) are examples of the multivariate approach. Different parameter settings will affect the results of the prediction model (Mustaffa et al., 2014).

SAS/ETS 9.22 User's Guide 290

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opens the Automatic Model Fitting window for applying the automatic model selection process to all series or to selected series in an input data set.. opens the Produce Forecasts window for producing forecasts for the series in the current input data set for which you have fit forecasting models.. closes the Time Series Forecasting system.. Time Series Simulation Window. Use the Time Series Simulation window to create a data set of simulated series generated by ARIMA processes.

Machine learning and time series models for VNQ market predictions

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BPNN outperforms the ARIMA time series model in predicting the VNQ market price. Referring to forecasting run charts, the VNQ price trend predicted by BPNN is also closer to the actual price. Stock price prediction using the ARIMA model. Time series analysis forecasting and control. Does REIT index hedge inflation risk? New evidence from the tail quantile dependences of the Markov-switching GRG copula.

Handbook of Economic Forecasting part 36

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“Time series analysis and simultaneous equation econometric models”. Journal of Econometrics 2, 17–54.. FORECASTING WITH UNOBSERVED COMPONENTS TIME SERIES MODELS. Structural time series models 335. Model selection in ARIMA, autoregressive and structural time series models 350. Structural time series models 385. 7: Forecasting with Unobserved Components Time Series Models 329.

Handbook of Economic Forecasting part 69

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“Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models”. Journal of Applied Statistics . “Some advances in non-linear and adaptive modelling in time-series”. Threshold Models in Non-Linear Time Series Analysis. Non-Linear Time Series. “Time-series model specification in the presence of outliers”. “Outliers, level shifts and variance changes in time series”. Journal of Forecasting 7, 1–20.. Journal of Forecasting 9, 315–.

Handbook of Economic Forecasting part 49

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Journal of Forecasting . Granger, C.W.J., Teräsvirta, T. “Modelling non-linear random vibrations using an amplitude-dependent au- toregressive time series model”. “A new approach to the economic analysis of nonstationary time series and the business cycle”. “Estimation, inference and forecasting of time series subject to changes in regime”.. Time Series Analysis. “Specification testing in Markov-switching time-series models”. Journal of Economet- rics . Journal of Economic Surveys .

Handbook of Economic Forecasting part 74

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Time Series Models. Forecasting, Structural Time Series Models and the Kalman Filter. Journal of Econometrics 69, 5–25.. “Seasonality in macroeconomic time series”. Journal of Economic Dynamics and Con- trol, 231–254.. “Time series with periodic structure”. Journal of Forecasting 23, 77–88.. “The impact of seasonal constants on forecasting seasonally cointegrated time series”. Journal of Econometrics 54, 1–49..

Handbook of Economic Forecasting part 35

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“Linear prediction by autoregressive model fitting in the time domain”. Time Series Analysis: Forecasting and Control. “Temporal aggregation and spurious instantaneous causality in multiple time series models”. Journal of Time Series Analysis . Time Series: Theory and Methods. Forecasting Economic Time Series. Forecasting Non-stationary Economic Time Series. International Journal of Forecasting . “Identifying multivariate time series models”.

Handbook of Economic Forecasting part 73

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Forecasting, seasonal adjustment and feedback. The greatest demand for forecasting seasonal time series is a direct consequence of removing seasonal components. The first subsection discusses how forecasting seasonal time series is deeply embedded in the process of seasonal adjustment. Seasonal adjustment and forecasting.