scholarly journals Comparison of Weighted Lag Adaptive LASSO with Autometrics for Covariate Selection and Forecasting Using Time-Series Data

Complexity ◽  
2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Sara Muhammadullah ◽  
Amena Urooj ◽  
Faridoon Khan ◽  
Mohammed N Alshahrani ◽  
Mohammed Alqawba ◽  
...  
Keyword(s):  
Time Series ◽  
Trade Balance ◽  
Time Series Data ◽  
Adaptive Lasso ◽  
Series Data ◽  
Covariate Selection ◽  
Linear Dependency ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Regularization Techniques

In order to reduce the dimensionality of parameter space and enhance out-of-sample forecasting performance, this research compares regularization techniques with Autometrics in time-series modeling. We mainly focus on comparing weighted lag adaptive LASSO (WLAdaLASSO) with Autometrics, but as a benchmark, we estimate other popular regularization methods LASSO, AdaLASSO, SCAD, and MCP. For analytical comparison, we implement Monte Carlo simulation and assess the performance of these techniques in terms of out-of-sample Root Mean Square Error, Gauge, and Potency. The comparison is assessed with varying autocorrelation coefficients and sample sizes. The simulation experiment indicates that, compared to Autometrics and other regularization approaches, the WLAdaLASSO outperforms the others in covariate selection and forecasting, especially when there is a greater linear dependency between predictors. In contrast, the computational efficiency of Autometrics decreases with a strong linear dependency between predictors. However, under the large sample and weak linear dependency between predictors, the Autometrics potency ⟶ 1 and gauge ⟶ α. In contrast, LASSO, AdaLASSO, SCAD, and MCP select more covariates and possess higher RMSE than Autometrics and WLAdaLASSO. To compare the considered techniques, we made the Generalized Unidentified Model for covariate selection and out-of-sample forecasting for the trade balance of Pakistan. We train the model on 1985–2015 observations and 2016–2020 observations as test data for the out-of-sample forecast.

scholarly journals Multistep Forecasting for Highly Volatile Data using A New Box-Jenkins and GARCH Procedure

2020 ◽  
pp. 1-6
Author(s):  
Siti Roslindar Yaziz ◽  
Roslinazairimah Zakaria ◽  
John Boland
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Gold Price ◽  
Series Data ◽  
R Language ◽  
Price Series ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
One Step ◽  
Multiple Step

The study of the multistep ahead forecast is significant for practical application purposes using the proposed statistical model. This study proposes a new procedure of Box-Jenkins and GARCH (or BJ-G) in evaluating the multistep forecasting performance for a highly volatile time series data. The promising results from one-step ahead out-of-sample forecast series using the BJ-G model has motivated the extension to multiple step ahead forecast. In order to achieve the objective, the procedure of multistep ahead forecast for BJ-G model is proposed using R language. In evaluating the performance of the multistep ahead forecast, the proposed procedure is employed to daily world gold price series of 5-year data. Based on the empirical results, the proposed procedure of multistep ahead forecast enhances the existing procedure of BJ-G which is able to provide a promising procedure to assess the performance of the BJ-G model in forecasting a highly volatile time series data. The procedure adds the value of BJ-G model since it allows the model to describe efficiently the characteristics of the volatile series up to n-step ahead forecast. Keywords: Box-Jenkins, GARCH, highly volatile data, multistep forecast; gold price

scholarly journals WHICH VARIABLES PREDICT INDONESIA'S INFLATION?

2019 ◽  
Vol 22 (1) ◽  
pp. 87-102 ◽  
Author(s):  
Susan Sunila Sharma
Keyword(s):  
Time Series ◽  
Comparative Analysis ◽  
Inflation Rate ◽  
Time Series Data ◽  
Predictor Variables ◽  
Series Data ◽  
Macroeconomic Variables ◽  
Monthly Time Series ◽  
Out Of Sample ◽  
Exhaustive List

We use an exhaustive list of Indonesia’s macroeconomic variables in a comparative analysis to determine which predictor variables are most important in forecasting Indonesia’s inflation rate. We use monthly time-series data for 30 macroeconomic variables. Using both in-sample and out-of-sample predictability evaluations, we report consistent evidence of inflation rate predictability using 11 out of 30 macroeconomic variables.

scholarly journals Aggregation, asymmetry, and common factors for Bangladesh’s exchange rate–trade balance relation

2021 ◽  
Author(s):  
Rabeya Khatoon ◽  
Md Emran Hasan ◽  
Md Wahid Ferdous Ibon ◽  
Shahidul Islam ◽  
Jeenat Mehareen ◽  
...  
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Trade Balance ◽  
Time Series Data ◽  
Government Support ◽  
Series Data ◽  
Aggregate Analysis ◽  
Long Run ◽  
J Curve ◽  
Rate Relationship

AbstractWe present an application of the recent CS-ARDL methodology in the context of a country’s trade balance–exchange rate relationship. The trade balance is expected to deteriorate first before improving in response to currency depreciation and vice versa, widely known as the J-curve effect satisfying the Marshall–Lerner condition in the long run. Combining bilateral and aggregate analysis in one setting by constructing specific panel data with one reference country, we find that aggregate analysis is sensitive to our allowance for heterogeneity. Estimates using the aggregate time series data show evidence favoring the J-curve relation, whereas the aggregate analysis resulting from the panel time series data shows that currency appreciation improves trade balance in Bangladesh in the long run, which goes against the Marshall–Lerner condition. With the reference of the existing commodity-level literature, we argue that this atypical scenario lines with the realities of a ‘small’ economy like Bangladesh, where her exporters attempt to maintain their market share with some government support. The study provides essential policy suggestions by identifying the significant contributors to Bangladesh’s trade balance–exchange rate relationship: China, Japan, and Singapore.

scholarly journals MODELING TIME SERIES DATA OF REAL SYSTEMS

2007 ◽  
Vol 18 (02) ◽  
pp. 235-252 ◽  
Author(s):  
DILIP P. AHALPARA ◽  
JITENDRA C. PARIKH
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Financial Time Series ◽  
Prediction Method ◽  
Series Data ◽  
Lorenz Equations ◽  
Linear Dynamics ◽  
Financial Time ◽  
Out Of Sample ◽  
Prediction Capability

Dynamics of complex systems is studied by first considering a chaotic time series generated by Lorenz equations and adding noise to it. The trend (smooth behavior) is separated from fluctuations at different scales using wavelet analysis and a prediction method proposed by Lorenz is applied to make out of sample predictions at different regions of the time series. The prediction capability of this method is studied by considering several improvements over this method. We then apply this approach to a real financial time series. The smooth time series is modeled using techniques of non linear dynamics. Our results for predictions suggest that the modified Lorenz method gives better predictions compared to those from the original Lorenz method. Fluctuations are analyzed using probabilistic considerations.

scholarly journals Generating Accurate Personalized Predictions of Future Behavior: A Smoking Exemplar

2019 ◽  
Author(s):  
Aaron Jason Fisher ◽  
Peter D. Soyster
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Time Series Data ◽  
Area Under The Curve ◽  
Time Of Day ◽  
Series Data ◽  
Future Behavior ◽  
Out Of Sample ◽  
Time Stamps ◽  
Time Lagged

The present study sought to apply statistical classification methods to idiographic time series data in order to make accurate future predictions of behavior. We recruited 70 individuals who presented as regular smokers; 52 completed experience sampling method (ESM) data collection and provided sufficient time series data. Time stamps from ESM surveys were used to calculate the time of day, day of the week, and continuous time—where the last datum was, in turn, used to calculate 12-hr and 24-hr cycles. Each individual’s time series was split into sequential training and testing sections, so that trained models could be tested on future observations. Prediction models were trained on the first 75% of the individual’s data and tested on the last 25%. Predictions of future behavior were made on a person by person basis. Two prediction algorithms were employed, elastic net regularization and naïve Bayes classification. Sample-wide area under the curve was nearly 80%, with some models demonstrating perfect prediction accuracies. Sensitivity and specificity were between 0.78 and 0.81 across the two approaches. Importantly, prediction models were based on a lagged data structure. Thus, in addition to supporting the prediction accuracy of our models with out-of-sample tests in time-forward data, the models themselves were time-lagged, such that each prediction was for the subsequent measurement. Such a system could be the basis for mobile, just-in-time interventions for substance use, as models that accurately predict future behavior could ostensibly be used for delivering personalized interventions at empirically-indicated moments of need.

scholarly journals REGIME CHANGE AND TREND PREDICTION FOR BITCOIN TIME SERIES DATA

2017 ◽  
Vol 5 ◽  
pp. 384-388 ◽  
Author(s):  
Osamu Kodama ◽  
Lukáš Pichl ◽  
Taisei Kaizoji
Keyword(s):  
Time Series ◽  
Regime Change ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Series Data ◽  
Strong Correlations ◽  
Markov Switching Model ◽  
Trend Prediction ◽  
Time Series Dataset ◽  
Out Of Sample

Bitcoin time series dataset recording individual transactions denominated in Euro at the COINBASE market between April 23, 2015 and August 15, 2016 is analyzed. Markov switching model is applied to classify the regions of varying volatility represented by three hidden state regimes using univariate autoregressive model and dependent mixture model. Causality extraction and price prediction of daily BTCEUR exchange rates is performed by means of a recurrent neural network using the standard Elman model. Strong correlations is found between the normalized mean squared error of the Elman network (out-of-sample 5-day-ahead prediction) and the realized volatility (sum of minute returns squared throughout the trading day). The present approach is calibrated using simulated regime change in standard econometric models. Our results clearly demonstrate the applicability of recurrent neural networks to causality extraction even in the case of highly volatile cryptocurrency exchange rate time series data.

scholarly journals Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248597
Author(s):  
Guo-hua Ye ◽  
Mirxat Alim ◽  
Peng Guan ◽  
De-sheng Huang ◽  
Bao-sen Zhou ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Arima Model ◽  
Mainland China ◽  
Hemorrhagic Fever ◽  
Series Data ◽  
Percentage Error ◽  
Forecasting Performance ◽  
The Future

Objective Hemorrhagic fever with renal syndrome (HFRS), one of the main public health concerns in mainland China, is a group of clinically similar diseases caused by hantaviruses. Statistical approaches have always been leveraged to forecast the future incidence rates of certain infectious diseases to effectively control their prevalence and outbreak potential. Compared to the use of one base model, model stacking can often produce better forecasting results. In this study, we fitted the monthly reported cases of HFRS in mainland China with a model stacking approach and compared its forecasting performance with those of five base models. Method We fitted the monthly reported cases of HFRS ranging from January 2004 to June 2019 in mainland China with an autoregressive integrated moving average (ARIMA) model; the Holt-Winter (HW) method, seasonal decomposition of the time series by LOESS (STL); a neural network autoregressive (NNAR) model; and an exponential smoothing state space model with a Box-Cox transformation; ARMA errors; and trend and seasonal components (TBATS), and we combined the forecasting results with the inverse rank approach. The forecasting performance was estimated based on several accuracy criteria for model prediction, including the mean absolute percentage error (MAPE), root-mean-squared error (RMSE) and mean absolute error (MAE). Result There was a slight downward trend and obvious seasonal periodicity inherent in the time series data for HFRS in mainland China. The model stacking method was selected as the best approach with the best performance in terms of both fitting (RMSE 128.19, MAE 85.63, MAPE 8.18) and prediction (RMSE 151.86, MAE 118.28, MAPE 13.16). Conclusion The results showed that model stacking by using the optimal mean forecasting weight of the five abovementioned models achieved the best performance in terms of predicting HFRS one year into the future. This study has corroborated the conclusion that model stacking is an easy way to enhance prediction accuracy when modeling HFRS.

A New Approach for Forecasting the Price Range With Financial Interval-Valued Time Series Data

2015 ◽  
Vol 1 (2) ◽  
Author(s):  
Wei Yang ◽  
Ai Han
Keyword(s):  
Time Series ◽  
Linear Models ◽  
Time Series Data ◽  
Financial Time Series ◽  
Statistical Tests ◽  
Series Data ◽  
Daily Data ◽  
Price Range ◽  
Out Of Sample ◽  
Interval Time Series

This paper proposes an interval-based methodology to model and forecast the price range or range-based volatility process of financial asset prices. Comparing with the existing volatility models, the proposed model utilizes more information contained in the interval time series than using the range information only or modeling the high and low price processes separately. An empirical study of the U.S. stock market daily data shows that the proposed interval-based model produces more accurate range forecasts than the classic point-based linear models for range process, in terms of both in-sample and out-of-sample forecasts. The statistical tests show that the forecasting advantages of the interval-based model are statistically significant in most cases. In addition, some stability tests have been conducted for ascertaining the advantages of the interval-based model through different sample windows and forecasting periods, which reveals similar results. This study provides a new interval-based perspective for volatility modeling and forecasting of financial time series data.

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