Quantifying the Multiscale Predictability of Financial Time Series by an Information-Theoretic Approach

Direct left turns (DLTs) could cause traffic slowdown, delay, stops, and even accidents on intersections, especially on no-median roads. Channelization and signalization can significantly diminish negative impact of DLTs. In China, a total of 56 large and medium-sized cities, including 17 provincial capitals, have adopted vehicle restriction policies due to traffic congestion, vehicle energy conservation and emission reduction, which cause travel inconvenience for citizens. This paper mainly studies signalization and channelization selections at intersections based on an entropy method. Based on the commonly used three evaluation indexes, the number of vehicles, CO emissions and fuel consumption have been added. The entropy evaluation method (EEM) method is innovatively used to objectively calculate the weight of the six indexes, which carry out the optimal traffic volume combinations for intersections of present situation, channelization and signalization. A VISSIM simulation is also used to evaluate the operating status of three conditions. The results show that EEM could help enormously in choosing different methods at a certain intersection. With the EEM, six indexes decrease by 20–70% at most.

Section: Introductionmentioning

confidence: 99%

Evaluating Signalization and Channelization Selections at Intersections Based on an Entropy Method

Shao

Claudel

2019

“…In this study, we use two estimators which fall into the above two categories, entropy difference [ 18 ] belonging to the “plug in” class, and the new estimator we propose

belonging to the Lempel–Ziv class).…”

Section: Methodsmentioning

confidence: 99%

“…The paper is organized as follows. In the Methodology section, we introduce two predictability estimators which measure the magnitude of predictability of time series,

[ 18 ] and

proposed in this paper, and a prediction algorithm EEMD-FFH. In the Numerical Simulation section, we apply these two estimators to the artificial simulation numerical analysis, the logistic map.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Relationship among Predictability, Prediction Accuracy and Data Frequency of Financial Time Series

Lin

2020

In this paper, we aim to reveal the connection between the predictability and prediction accuracy of stock closing price changes with different data frequencies. To find out whether data frequency will affect its predictability, a new information-theoretic estimator Plz, which is derived from the Lempel–Ziv entropy, is proposed here to quantify the predictability of five-minute and daily price changes of the SSE 50 index from the Chinese stock market. Furthermore, the prediction method EEMD-FFH we proposed previously was applied to evaluate whether financial data with higher sampling frequency leads to higher prediction accuracy. It turns out that intraday five-minute data are more predictable and also have higher prediction accuracy than daily data, suggesting that the data frequency of stock returns affects its predictability and prediction accuracy, and that higher frequency data have higher predictability and higher prediction accuracy. We also perform linear regression for the two frequency data sets; the results show that predictability and prediction accuracy are positive related.

“…Zhao et al proposed the multiscale entropy difference (MED) to assess the predictability of nonlinear financial time series on several time scales [ 3 ]. MED quantifies the contributions of the past values by reducing the uncertainty of the forthcoming values in signals on several time scales.…”

Section: New Developments In Entropy-based Measuresmentioning

confidence: 99%

Multiscale Entropy Approaches and Their Applications

Humeau-Heurtier

2020