Predicting Time Series from Short-Term High-Dimensional Data

The prediction of future values of time series is a challenging task in many fields. In particular, making prediction based on short-term data is believed to be difficult. Here, we propose a method to predict systems' low-dimensional dynamics from high-dimensional but short-term data. Intuitively, it can be considered as a transformation from the inter-variable information of the observed high-dimensional data into the corresponding low-dimensional but long-term data, thereby equivalent to prediction of time series data. Technically, this method can be viewed as an inverse implementation of delayed embedding reconstruction. Both methods and algorithms are developed. To demonstrate the effectiveness of the theoretical result, benchmark examples and real-world problems from various fields are studied.

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LSTM-Guided Coaching Assistant for Table Tennis Practice

Sensors ◽

10.3390/s18124112 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4112 ◽

Cited By ~ 6

Author(s):

Se-Min Lim ◽

Hyeong-Cheol Oh ◽

Jaein Kim ◽

Juwon Lee ◽

Jooyoung Park

Keyword(s):

Time Series ◽

State Space ◽

Time Series Data ◽

State Space Model ◽

Skill Assessment ◽

Series Data ◽

High Dimensional ◽

Table Tennis ◽

Space Model ◽

Low Dimensional

Recently, wearable devices have become a prominent health care application domain by incorporating a growing number of sensors and adopting smart machine learning technologies. One closely related topic is the strategy of combining the wearable device technology with skill assessment, which can be used in wearable device apps for coaching and/or personal training. Particularly pertinent to skill assessment based on high-dimensional time series data from wearable sensors is classifying whether a player is an expert or a beginner, which skills the player is exercising, and extracting some low-dimensional representations useful for coaching. In this paper, we present a deep learning-based coaching assistant method, which can provide useful information in supporting table tennis practice. Our method uses a combination of LSTM (Long short-term memory) with a deep state space model and probabilistic inference. More precisely, we use the expressive power of LSTM when handling high-dimensional time series data, and state space model and probabilistic inference to extract low-dimensional latent representations useful for coaching. Experimental results show that our method can yield promising results for characterizing high-dimensional time series patterns and for providing useful information when working with wearable IMU (Inertial measurement unit) sensors for table tennis coaching.

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Track Irregularity Time Series Analysis and Trend Forecasting

Discrete Dynamics in Nature and Society ◽

10.1155/2012/387857 ◽

2012 ◽

Vol 2012 ◽

pp. 1-15 ◽

Cited By ~ 17

Author(s):

Jia Chaolong ◽

Xu Weixiang ◽

Wang Futian ◽

Wang Hanning

Keyword(s):

Time Series ◽

Time Series Data ◽

Series Data ◽

Short Term ◽

Measuring Point ◽

Trend Forecasting ◽

Changing Trend ◽

Track Irregularity ◽

Linear Differential

The combination of linear and nonlinear methods is widely used in the prediction of time series data. This paper analyzes track irregularity time series data by using gray incidence degree models and methods of data transformation, trying to find the connotative relationship between the time series data. In this paper, GM(1,1)is based on first-order, single variable linear differential equations; after an adaptive improvement and error correction, it is used to predict the long-term changing trend of track irregularity at a fixed measuring point; the stochastic linear AR, Kalman filtering model, and artificial neural network model are applied to predict the short-term changing trend of track irregularity at unit section. Both long-term and short-term changes prove that the model is effective and can achieve the expected accuracy.

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Long Short Term Memory Recurrent Network for Standard and Poor’s 500 Index Modelling

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.15.21365 ◽

2018 ◽

Vol 7 (4.15) ◽

pp. 25 ◽

Cited By ~ 3

Author(s):

Said Jadid Abdulkadir ◽

Hitham Alhussian ◽

Muhammad Nazmi ◽

Asim A Elsheikh

Keyword(s):

Neural Network ◽

Time Series ◽

Recurrent Neural Network ◽

Time Series Data ◽

Short Term Memory ◽

Series Data ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Forecasting time-series data are imperative especially when planning is required through modelling using uncertain knowledge of future events. Recurrent neural network models have been applied in the industry and outperform standard artificial neural networks in forecasting, but fail in long term time-series forecasting due to the vanishing gradient problem. This study offers a robust solution that can be implemented for long-term forecasting using a special architecture of recurrent neural network known as Long Short Term Memory (LSTM) model to overcome the vanishing gradient problem. LSTM is specially designed to avoid the long-term dependency problem as their default behavior. Empirical analysis is performed using quantitative forecasting metrics and comparative model performance on the forecasted outputs. An evaluation analysis is performed to validate that the LSTM model provides better forecasted outputs on Standard & Poor’s 500 Index (S&P 500) in terms of error metrics as compared to other forecasting models.

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Information-Theoretic Characterization of Bifurcations in Nonlinear Dynamical Systems With Noise

Volume 7B: 9th International Conference on Multibody Systems, Nonlinear Dynamics, and Control ◽

10.1115/detc2013-13519 ◽

2013 ◽

Author(s):

Navendu S. Patil ◽

Joseph P. Cusumano

Keyword(s):

Time Series ◽

Time Series Data ◽

Nonlinear Dynamical Systems ◽

Period Doubling ◽

Entropy Rate ◽

Series Data ◽

High Dimensional ◽

Dimensional System ◽

Dimensional Manifold ◽

Low Dimensional

Detecting bifurcations in noisy and/or high-dimensional physical systems is an important problem in nonlinear dynamics. Near bifurcations, the dynamics of even a high dimensional system is typically dominated by its behavior on a low dimensional manifold. Since the system is sensitive to perturbations near bifurcations, they can be detected by looking at the apparent deterministic structure generated by the interaction between the noise and low-dimensional dynamics. We use minimal hidden Markov models built from the noisy time series to quantify this deterministic structure at the period-doubling bifurcations in the two-well forced Duffing oscillator perturbed by noise. The apparent randomness in the system is characterized using the entropy rate of the discrete stochastic process generated by partitioning time series data. We show that as the bifurcation parameter is varied, sharp changes in the statistical complexity and the entropy rate can be used to locate incipient bifurcations.

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ANALISIS PENGARUH VARIABEL MAKROEKONOMI DAN COUNTRY RISK TERHADAP INVESTASI PORTOFOLIO ASING DI INDONESIA

Ecosains: Jurnal Ilmiah Ekonomi dan Pembangunan ◽

10.24036/ecosains.11066657.00 ◽

2018 ◽

Vol 7 (2) ◽

pp. 135

Author(s):

Halifah Hadi ◽

Hasdi Aimon ◽

Dewi Zaini Putri

Keyword(s):

Economic Growth ◽

Time Series ◽

Time Series Data ◽

Country Risk ◽

Growth Effect ◽

Series Data ◽

Short Term ◽

Correction Model ◽

The Stability

The reseach aims to explain the effect of country risk and variabels macroeconomics to the foreign portofolio invesment in Indonesia in short term and long term. The analysis takes time series time series data from 2006 quarter 1 through 2016 quarter 4by using Error Correction Model (ECM). The source of data are Badan Pusat Statistik, Bank Indonesia, FX Sauder and World Bank. The result are in the short term the exchange rate and economic growth effect the shock that will influence the foreign portofolio invesment. In the long trem the inflation, interst rate, money supply and country risk influence on foreign portofolio invesment significanly. The suggestion in this research is, the goverment sould keep the stability balance of payment in Indonesia .Any change, the condition of balance of payments effect appreciation and depreciation to Rupiah. To increase the economic growth in Indonesia, goverment could increasing the fiscal income and PMDN realization that will increase the enterprises productivity.

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Analysis and Prediction of Alerts in Perimeter Intrusion Detection System

Defence Science Journal ◽

10.14429/dsj.70.15545 ◽

2020 ◽

Vol 70 (6) ◽

pp. 619-625

Author(s):

Rizul Aggarwal ◽

Anjali Goswami ◽

Jitender Kumar ◽

Gwyneth Abdiel Chullai

Keyword(s):

Time Series ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Time Series Data ◽

Detection System ◽

Series Data ◽

Support Vector ◽

Short Term ◽

Term Prediction

Perimeter surveillance systems play an important role in the safety and security of the armed forces. These systems tend to generate alerts in advent of anomalous situations, which require human intervention. The challenge is the generation of false alerts or alert flooding which makes these systems inefficient. In this paper, we focus on short-term as well as long-term prediction of alerts in the perimeter intrusion detection system. We have explored the dependent and independent aspects of the alert data generated over a period of time. Short-term prediction is realized by exploiting the independent aspect of data by narrowing it down to a time-series problem. Time-series analysis is performed by extracting the statistical information from the historical alert data. A dual-stage approach is employed for analyzing the time-series data and support vector regression is used as the regression technique. It is helpful to predict the number of alerts for the nth hour. Additionally, to understand the dependent aspect, we have investigated that the deployment environment has an impact on the alerts generated. Long-term predictions are made by extracting the features based on the deployment environment and training the dataset using different regression models. Also, we have compared the predicted and expected alerts to recognize anomalous behaviour. This will help in realizing the situations of alert flooding over the potential threat.

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GLOBALISASI EKONOMI DAN PENGANGGURAN: STUDI KASUS INDONESIA

Jurnal Ekonomi Bisnis dan Kewirausahaan ◽

10.26418/jebik.v10i3.49278 ◽

2021 ◽

Vol 10 (3) ◽

pp. 263

Author(s):

Ari Setyawan ◽

I Wayan Suparta ◽

Neli Aida

Keyword(s):

Economic Growth ◽

Time Series ◽

Unemployment Rate ◽

Time Series Data ◽

Economic Globalization ◽

Series Data ◽

Real Wages ◽

Short Term ◽

The Government

ABSTRACTThis study aims to examine the effect of economic globalization on the unemployment rate in Indonesia and the relationship of other macroeconomic variables such as economic growth, inflation rate, and real wage with unemployment. The data used is in the form of annual time series data from 1986 to 2018, whose research results are analyzed using the ARDL method. This study concludes that economic globalization can reduce the unemployment rate in Indonesia in the short term, although in the long term, it increases the unemployment rate. Economic growth and inflation in the short and long term have not been able to reduce the current unemployment rate, while the increase in real wages has reduced the unemployment rate in the short term, although not in the long term. By looking at these results, we need to be wary of economic globalization because economic globalization has a destructive impact in the long term. So that concrete and consistent efforts are needed from the government, the private sector, and other stakeholders so that Indonesia gets the maximum benefit from economic globalization, especially in job creation and reducing unemployment.JEL : B22, E22.Keywords : unemployment, economic globalization, economic growth, inflation, real wages. ABSTRAKPenelitian ini bertujuan melihat pengaruh tingkat globalisasi ekonomi terhadap tingkat pengangguran di Indonesia serta hubungan variabel makroekonomi lain seperti tingkat pertumbuhan ekonomi, tingkat inflasi dan tingkat upah riil dengan tingkat pengangguran. Data yang dipergunakan berupa data time series tahunan dari periode 1986 hingga 2018 yang hasil penelitiannya dianalisis menggunakan metode ARDL. Kesimpulan penelitian ini yaitu globalisasi ekonomi mampu mengurangi tingkat pengangguran di Indonesia dalam jangka pendek meskipun dalam jangka panjang malah meningkatkan tingkat pengangguran. Pertumbuhan ekonomi dan inflasi baik dalam jangka pendek dan jangka panjangnya belum mampu menurunkan tingkat pengangguran yang ada sedangkan naiknya upah riil mampu menurunkan tingkat pengangguran dalam jangka pendek meskipun tidak dalam jangka panjang. Dengan melihat hasil ini, kita perlu waspada terhadap globalisasi ekonomi karena globalisasi ekonomi ini memiliki dampak buruk dalam jangka panjang sehingga dibutuhkan upaya kongkrit dan konsisten baik dari pemerintah, swasta maupun para stakeholder lain agar Indonesia memperoleh manfaat yang sebesar-besarnya dari globalisasi ekonomi khusunya dalam upaya penciptaan lapangan kerja dan mengurangi pengangguran.

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PERAMALAN JUMLAH PENUMPANG KERETA API MENGGUNAKAN METODE ARIMA, INTERVENSI DAN ARFIMA (Studi Kasus : Penumpang Kereta Api Kelas Lokal EkonomiDAOP IV Semarang)

Jurnal Gaussian ◽

10.14710/j.gauss.v7i1.26639 ◽

2018 ◽

Vol 7 (1) ◽

pp. 96-109

Author(s):

Helmi Panjaitan ◽

Alan Prahutama ◽

Sudarno Sudarno

Keyword(s):

Time Series ◽

Long Memory ◽

Time Series Data ◽

Moving Average ◽

Step Function ◽

Local Economy ◽

Stationary Time Series ◽

Series Data ◽

Short Term

Autoregressive Integrated Moving Average (ARIMA) is stationary time series model after differentiation. Differentiation value of ARIMA method is an integer so it is only able to model in the short term. The best model using ARIMA method is ARIMA([13]; 1; 0) with an MSE value of 1,870844. The Intervention method is a model for time series data which in practice has extreme fluctuations both up and down. In the data plot the number of train passengers was found to be extreme fluctuation. The data used was from January 2009 to June 2017 where fluctuation up significantly in January 2016 (T=85 to T=102) so the intervention model that was suspected was a step function. The best model uses the Intervention step function is ARIMA ([13]; 1; 1) (b=0; s=18; r=0) with MSE of 1124. Autoregressive Fractionally Integrated Moving Average (ARFIMA) method is a development of the ARIMA method. The advantage of the ARFIMA method is the non-integer differentiation value so that it can overcome long memory effect that can not be solve with the ARIMA method. ARFIMA model is capable of modeling high changes in the long term (long term persistence) and explain long-term and short-term correlation structures at the same time. The number of local economy class train passengers in DAOP IV Semarang contains long memory effects, so the ARFIMA method is used to obtain the best model. The best model obtained is the ARMA(0; [1,13]) model with the differential value is 0,367546, then the model can be written into ARFIMA (0; d; [1,13]) with an MSE value of 0,00964. Based on the analysis of the three methods, the best method of analyzing the number of local economy class train passengers in DAOP IV Semarang is the ARFIMA method with the model is ARFIMA (0; 0,367546; [1,13]). Keywords: Train Passengers, ARIMA, Intervention, ARFIMA, Forecasting

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Analisis ekspor kopi Indonesia

Jurnal Paradigma Ekonomika ◽

10.22437/paradigma.v12i1.3929 ◽

2017 ◽

Vol 12 (1) ◽

pp. 1-10

Author(s):

Rexsi Nopriyandi ◽

Haryadi Haryadi

Keyword(s):

Time Series ◽

Exchange Rate ◽

Exchange Rates ◽

Time Series Data ◽

Series Data ◽

Government Agencies ◽

Short Term ◽

Correction Model ◽

Coffee Price

This study aims to analyze the factors that influence Indonesian coffee exports. The data in this study is time series data, which were obtained from various government agencies. The Error Correction Model (ECM) method is used to analyze the effect of coffee prices, GDP and the exchange rate on the volume of Indonesian coffee exports. The estimation results find that coffee prices, Indonesian GDP and exchange rates have a short-term relationship and a long-term balance of the volume of coffee exports. Based on the long-term estimation of the coffee price variable, GDP and exchange rates do not significantly affect the volume of coffee exports, while in the short term these three variables influence the volume of coffee exports

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