Nonlinear Setups Research Articles

Estimation of Farmgate Rice Price Using Temporal Causal Modeling and Kalman Filtering

Objectives: To develop a forecasting model for the farmgate prices of rice crop in the Philippines and to improve the derived model forecasts by applying Kalman filters. Methods: The researcher’s utilized monthly rice farmgate price and inflation rate from 1990 to 2015 as training information in building the temporal-causal model. On the other hand, the dataset for rice farmgate price from 2016 to 2020 acted as the testing set, allowing the researchers to determine model accuracy using mean square error, mean absolute error, and mean absolute percentage error. Findings: Results indicate that applying Kalman filters to the derived temporal-causal model indeed improves prediction performance, as evidenced by the lower MSE values. In particular, applying Kalman filter to the derived Temporal-Causal model 15% (without inflation as control input) and 3% (with inflation as control input) decrease in the MSE. In terms of the Temporal-Causal-Kalman filter with no control input, a decrease of 1.8% is observed for the MAE as well as a decrease of 3% in the MAPE, indicating a substantial improvement in the accuracy of the base model. Interestingly though, adding a control-input variable in the Kalman filter generated gave an increase of 4.4% and 3.8% in the MAE and MAPE respectively. This might be due to the not-so-strong correlation between the farmgate price and control input (inflation). Seeking conditions when will external inputs be helpful in enhancing Kalman filters as well as combining the models with other data analytics techniques may be valuable in future research. As for the comparison with nonlinear setups, results for unscaled, partially scaled, and fully scaled artificial neural networks show that Kalman filtering can attain almost on-par prediction performance with such methods. Novelty: This research presented a new scheme of predicting farmgate price. Compared to typical time series models, the derived Temporal-Causal model included inflation as a factor. Moreover, the combination of Temporal-Causal and Kalman filter is a new method for improving forecasts. Keywords: Kalman Filter; Temporal Causal Model Modeling; Price Estimation; Rice Price

Wind-induced vibration of a square cylinder suspended by parallel elastic strips with nonlinear stiffness

A series of wind tunnel tests is conducted to investigate the wind-induced vibration of a square cylinder suspended by four parallel elastic strips. The elastic strip-suspended experimental setup, which provides the square cylinder with a cubic hardening nonlinear stiffness, is more convenient than existing nonlinear setups (e.g., the numerical computer-controlled feedback nonlinear setup or the physical magnet-induced nonlinear setup) and can be readily achieved in any wind tunnel. A theoretical formula is deduced for the nonlinear restoring force provided by parallel elastic strips, and the theoretical formula is validated against experimental measurements. The considered square cylinder exhibits vortex-induced vibrations (VIVs) at low wind velocities while galloping vibrations occur as the wind velocity becomes higher. The critical wind velocity for galloping instability can be remarkably reduced by adding two fins at the trailing-edge corners of the square cylinder. The influences of the pretension force of the elastic strips and the configuration of the trailing-edge fins on the vibration amplitude and kinetic energy of the square cylinder are also investigated.

Market efficiency and volatility persistence of cryptocurrency during pre‐ and post‐crash periods of Bitcoin: Evidence based on fractional integration

AbstractThis article investigates both market efficiency and volatility persistence in 12 cryptocurrencies during pre‐crash and post‐crash periods. The article contributes to the debate on the market efficiency of cryptocurrencies in the presence of volatility, considering robust fractional integration methods in both linear and nonlinear setups. We find that markets of Bitcoin and most altcoins considered in our study can be dubbed as efficient, and are also highly volatile, particularly, in the post‐crash period that we are experiencing now. The volatilities are more likely to persist for a shorter period than volatilities in the pre‐crash period. Our work, therefore, renders important information to cryptocurrency market participants and portfolio managers.