scholarly journals FORECAST OF TOURIST DEMAND IN UKRAINE ON A FAST-FUTURE PROSPECTS

World Science ◽  
2018 ◽  
Vol 1 (8(36)) ◽  
pp. 11-16
Author(s):  
Viktor Krylov ◽  
Christina Lipyanina
Keyword(s):  
Time Series ◽  
Future Prospects ◽  
Good Match ◽  
Auto Correlation ◽  
Tourist Demand ◽  
Bic Criterion

The process of formation of tourist demand was studied and autocorrelation and partial auto-correlation were calculated. Valued behavior of selective ACF and partial PACF, showing the hypothesis about the values of the parameters p and q. Due to the lack of data, several competing ARMA (1.1) and ARMA (2.0) models have been selected. Both models showed a good match with the data, the models are adequate and the errors are random, so the best model is chosen according to the AIC and BIC criterion. The remains of the selected model are checked for the absence of auto-correlation using the Lew Box test. For the selected best model, forecasts were projected for 5 periods ahead. From the forecast of the time series it is clear that the tourist demand in the next 5 years will decline.

An Experimental Study of the Forced Response of Pre- and Post-Critical Plates

1995 ◽  
Author(s):  
Kevin D. Murphy ◽  
Lawrence N. Virgin ◽  
Stephen A. Rizzi
Keyword(s):  
Experimental Study ◽  
Time Series ◽  
Lyapunov Exponent ◽  
Small Amplitude ◽  
Narrow Band ◽  
Power Spectra ◽  
Forced Response ◽  
Acoustic Excitation ◽  
Rectangular Plates ◽  
Auto Correlation

Abstract Experimental results are presented which characterize the dynamic response of homogeneous, fully clamped, rectangular plates to narrow band acoustic excitation and uniform thermal loads. Using time series, pseudo-phase projections, power spectra and auto-correlation functions, small amplitude vibrations are considered about both the pre- and post-critical states. These techniques are then employed to investigate the snap-through response. The results for snap-through suggest that the motion is temporally complex and a Lyapunov exponent calculation confirms that the motion is chaotic. Finally, a snap-through boundary is mapped in the (ω, SPL) parameter space separating the regions of snap-through and no snap-through.

scholarly journals Spatial long memory

2019 ◽  
Vol 3 (1) ◽  
pp. 243-256
Author(s):  
Peter M. Robinson
Keyword(s):  
Time Series ◽  
Statistical Modeling ◽  
Spatial Data ◽  
Long Memory ◽  
The Other ◽  
Future Prospects ◽  
Memory Time ◽  
Short Memory ◽  
The One ◽  
Relevant Work

AbstractWe discuss developments and future prospects for statistical modeling and inference for spatial data that have long memory. While a number of contributons have been made, the literature is relatively small and scattered, compared to the literatures on long memory time series on the one hand, and spatial data with short memory on the other. Thus, over several topics, our discussions frequently begin by surveying relevant work in these areas that might be extended in a long memory spatial setting.

Short-term wind speed forecasting using ARIMA model

Energetika ◽  
2016 ◽  
Vol 62 (1-2) ◽  
Author(s):  
Ernesta Grigonytė ◽  
Eglė Butkevičiūtė
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Moving Average ◽  
Arima Model ◽  
Model Structure ◽  
Optimal Model ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Auto Correlation ◽  
Auto Correlation Function

The massive integration of wind power into the power system increasingly calls for better short-term wind speed forecasting which helps transmission system operators to balance the power systems with less reserve capacities. The  time series analysis methods are often used to analyze the  wind speed variability. The  time series are defined as a sequence of observations ordered in time. Statistical methods described in this paper are based on the prediction of future wind speed data depending on the historical observations. This allows us to find a sufficiently good model for the wind speed prediction. The paper addresses a short-term wind speed forecasting ARIMA (Autoregressive Integrated Moving Average) model. This method was applied for a number of different prediction problems, including the short term wind speed forecasts. It is seen as an early time series methodology with well-known limitations in wind speed forecasting, mainly because of insufficient accuracies of the hourly forecasts for the second half of the day-ahead forecasting period. The authors attempt to find the maximum effectiveness of the model aiming to find: (1) how the identification of the optimal model structure improves the forecasting results and (2) what accuracy increase can be gained by reidentification of the structure for a new wind weather season. Both historical and synthetic wind speed data representing the sample locality in the Baltic region were used to run the model. The model structure is defined by rows p, d, q and length of retrospective data period. The structure parameters p (Autoregressive component, AR) and q (Moving Average component, MA) were determined by the Partial Auto-Correlation Function (PACF) and Auto-Correlation Function (ACF), respectively. The model’s forecasting accuracy is based on the root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE). The results allowed to establish the optimal model structure and the length of the input/retrospective period. The  quantitative study revealed that identification of the  optimal model structure gives significant accuracy improvement against casual structures for 6–8 h forecast lead time, but a season-specific structure is not appropriate for the entire year period. Based on the conducted calculations, we propose to couple the ARIMA model with any more effective method into a hybrid model.

scholarly journals Time series forecasting for the adobe software company’s stock prices using ARIMA (BOX-JENKIN’) model

2021 ◽  
Vol 2115 (1) ◽  
pp. 012044
Author(s):  
R. Vaibhava Lakshmi ◽  
S. Radha
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Time Series Data ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Series Data ◽  
Auto Correlation ◽  
Correlation Plot ◽  
Best Fitting ◽  
Fitting Model

Abstract The time series forecasting strategy, Auto-Regressive Integrated Moving Average (ARIMA) model, is applied on the time series data consisting of Adobe stock prices, in order to forecast the future prices for a period of one year. ARIMA model is used due to its simple and flexible implementation for short term predictions of future stock prices. In order to achieve stationarity, the time series data requires second-order differencing. The comparison and parameterization of the ARIMA model has been done using auto-correlation plot, partial auto-correlation plot and auto.arima() function provided in R (which automatically finds the best fitting model based on the AIC and BIC values). The ARIMA (0, 2, 1) (0, 0, 2) [12] is chosen as the best fitting model, with a very less MAPE (Mean Absolute Percentage Error) of 3.854958%.

scholarly journals White Noise Tests on the LSTM Model Trained with Double Pendulum

2021 ◽  
Vol 2137 (1) ◽  
pp. 012032
Author(s):  
Xisen Wang
Keyword(s):  
Time Series ◽  
White Noise ◽  
Time Series Data ◽  
Initial Conditions ◽  
Correlation Coefficients ◽  
Series Data ◽  
Double Pendulum ◽  
Auto Correlation ◽  
State Dependent ◽  
Auto Regressive

Abstract This paper describes the intrinsic qualities of a simple double pendulum (DP), with a visual representation, a rigorous deduction of the Lagrangian equation, and a concrete factor analysis. LSTM model was utilized to simulate the double pendulum’s periodic and chaotic behaviors and evaluates the effectiveness of the model. The auto-correlation coefficients was calculated. Meanwhile, Box-Pierce test and Ljung-Box tests for various state-dependent time series were conducted to give various initial conditions to explore the DP system’s random characteristics. The research results are as follows: 1) Chaos did not lead to direct randomness; 2) seasonality could coexist with chaos; 3) the highly auto-regressive nature of DP’s time series data are found. Therefore, it can be concluded that the chaos in a double pendulum has particular patterns (such as the positive relationship with the likelihood of being a random white noise series) that could be further explored.

Detrended Correlogram Method for Non-Stationary Time-Series Analysis

2021 ◽  
pp. 2250012
Author(s):  
G. F. Zebende ◽  
E. F. Guedes
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Correlation Coefficient ◽  
Memory Effect ◽  
Cross Correlation ◽  
Time Lag ◽  
Stationary Time Series ◽  
Time Lags ◽  
Statistical Tool ◽  
Auto Correlation

A correlogram is a statistical tool that is used to check time-series memory by computing the auto-correlation coefficient as a function of the time lag. If the time-series has no memory, then the auto-correlation must be close to zero for any time lag, otherwise if there is a memory, then the auto-correlations must be significantly different from zero. Therefore, based on the robust detrended cross-correlation coefficient, [Formula: see text], we propose the detrended correlogram method in this paper, which will be tested for some time-series (simulated and empirical). This new statistical tool is able to visualize a complete map of the auto-correlation for many time lags and time-scales, and can therefore analyze the memory effect for any time-series.

Time series analysis of variation in abundance of selected cocoa insects and fitting of simple linear predictive models

1993 ◽  
Vol 83 (2) ◽  
pp. 153-169 ◽  
Author(s):  
M Bigger
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Linear Models ◽  
Extension Growth ◽  
Atmospheric Moisture ◽  
Series Analysis ◽  
Auto Correlation ◽  
Autoregressive Parameter ◽  
Cyclical Behaviour ◽  
Analysis Of Variation

AbstractA plot of cocoa trees at the Cocoa Research Institute, Tafo, Ghana, was inspected weekly for the presence on each tree of 18 species of insects. The 306 consecutive weekly records of percentage of trees occupied by each species were subjected to time series analysis. Auto-correlation and partial auto-correlation functions were calculated for each series and used to identify simple autoregressive linear models to account for the serial correlation. It was found that all species needed a stabilizing autoregressive parameter of lag 1 and all but four a second autoregressive parameter of lag 2. Seasonal autoregressive parameters at lags 3, 4, 5, 7, 9, or 13 were needed for over half the species, either in addition to the parameter at lag 2 or in place of it. It is postulated that these seasonal parameters mimic generation cycles. Runs of the models using random inputs produced series which were close to the originals in general form. The models could be further refined by adjusting the fixed mean levels assumed by the models to take into account the effects due to the abundance of young extension growth on the trees and atmospheric moisture, as measured by afternoon relative humidity readings. Although the production of extension growth is cyclical it would seem that it does not induce the cyclical behaviour observed in some of the insect series. The peaks in the latter cycles are, however, reinforced when they coincide with peaks in extension growth.

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