classical time-series methods
Error magnitude and directional accuracy for time series forecasting evaluation
... on classical time series ...modern methods such as neural networks and fuzzy time series can produce better forecast results as compared to the classical time ...
40
Profiling and forecasting air pollutant index for Malaysia
... the classical time series methods that were applied in the monthly data were Box-Jenkins method, time series regression method and winter’s exponential smoothing ...modern ...
46
The Doubly Adaptive LASSO Methods for Time Series Analysis
... a classical variable selection procedure sets a coe ffi cient in a model to zero if it is marginally insignificant, ...shrinkage methods at large remain ...
187
Do we need time series econometrics
... the time series based cointegration approach is very similar, it has shown that if the variables are non-stationary in their levels their means and variances violate the classical assumptions that ...
9
Simulation-based Estimation Methods for Financial Time Series Models
... The two algorithms discussed in Section ?? are the classical ML methods. We now discuss how the Laplace approximation can be employed to perform a Bayesian MCMC analysis. The goal of MCMC methods is ...
28
Improved Statistical Methods for Time-series and Lifetime Data
... failure time of the system can be observed. Methods for estimating parameters of component lifetime dis- tributions based on observed system lifetime data have been discussed in the literature (Balakrishnan ...
119
Comparison of Methods of Estimating Missing Values in Time Series
... Using the Bode-Shannon representation of random processes and the “state-transition” method of analysis of dynamic systems, Kalman [4] worked on classical filtering and prediction in relation to missing values in ...
10
Evaluation and comparison of machine learning and classical econometric AR model on financial time series data
... and time series forecasting, it is always in the best interest of prediction to provide training samples which have an equal amount of examples for each class, meaning if predicting price moves up or down, ...
40
Artificial neural network forecasting performance with missing value imputations
... important classical method is the autoregressive integrated moving average (ARIMA) model because of its flexibility in modelling different types of dataset [1, ...the series, then the model is known as ...
7
A Survey of Methods to Interpolate, Distribute and Extra polate Time Series
... In the extension to dynamic regression models, the ML approach was again used in Palm and Nijman’s works. [79] considered a simultaneous equations model, not completely specified, about the Dutch labor market with some ...
15
Vertical deformation monitoring of the suspension bridge tower using GNSS: a case study of the Forth Road Bridge in the UK
... of time considering the complicated meteorological change and the high structural construction of the ...of time are selected: 1 January 2016 to 14 January 2016 and 1 March 2016 to 14 March ...same ...
19
Meta-heuristic Methods for Outliers Detection in Multivariate Time Series
... univariate time series. Among these methods we can distinguish those based on an explicit model (parametric approach) from those that use non-explicit models (nonparametric ...
11
On nonparametric likelihood methods for weakly and strongly dependent time series
... This paper develops a new blockwise empirical likelihood (BEL) method for stationary, weakly dependent time processes, called the progressive block empirical likelihood (PBEL). In contrast to the standard version ...
149
Simulation-based Estimation Methods for Financial Time Series Models
... The general idea of the Bayesian approach is to perform posterior computa- tions, given the likelihood function and the prior distribution. MCMC is a class of algorthims which enables one to obtain a correlated sample ...
37
Monte Carlo methods for adaptive sparse approximations of time series
... In many applications we want to learn the features a k to best model a set of observations. The statistical framework introduced above allows us to develop algorithms to adapt the features a k for any particular set of ...
14
Stock market time series forecasting with data mining methods
... forecasting methods used in studies fall into two categories: statistical/econometric and data mining/machine learning ...VAR methods. These methods return good forecasting results if the financial ...
21
Comparison of Two Time Series Decomposition Methods: Least Squares and Buys Ballot Methods
... MSE, MAE and MAPE) are equal in all the simulations as in the additive model. How- ever, as the value of b increased from 0.02 to 2.0 the results show that the values of the summary statistics (ME, RMSE, MSE, MAE and ...
15
New significance test methods for Fourier analysis of geophysical time series
... Figure 5 shows the Fourier and modified Fourier power spec- tra, and we find a significant peak at about 12 yr period in a test against red noise. This peak is not significant in tradi- tional tests. The main reason for ...
10
Modeling and trend analysis of leukemia in children using time series methods
... There were 259 diagnosed patients with blood cancers over 2003-2013, out of whom there were 220 patients with ALL and 39 patients with AML, 162 males and 97 females. The age mean was 6 years with a standard deviation ...
6
Subsequence-Based Time Series Clustering Utilizing Stochastic Selection Methods
... To test Assertion 1, 5 categories of products are chosen from the grocer: Beer, Soft Drinks, Cheeses, Cereals, and Toothpastes. In each of these categories, 3 high selling products, or SKUs (store keeping units), are ...
191