On the volatility of sensex

The present study seeks to analyse Volatility of popular stock index SENSEX. The present study is based on the closing time series data of SENSEX covering the period from 3rd January 2000, to 30th June 2011. The year 2008 has recorded higher Volatility compared to the other years of the study. Volatility fell in the year 2009 from the high of 2008. The years after were comparatively calmer. In the year 2000, the Volatility was higher signifying enhance market activity. The overall daily Volatility for SENSEX was approximately 1.70 % while the annualized value was approximately 25%-26%. Events Reported around Daily Returns in Excess of +/-5%have also been identified.

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Producers' Response to Changes in Prices and Marketing Policies: A Case Study of Sugarcane and Paddy in Eastern U.P. by S. C. Gupta and A. Majid. New York: Asia Publishing House, 1965.

The Pakistan Development Review ◽

10.30541/v8i2pp.308-309 ◽

1968 ◽

Vol 8 (2) ◽

pp. 308-309

Author(s):

Mohammad Irshad Khan

Keyword(s):

New York ◽

Time Series ◽

Time Series Data ◽

Publishing House ◽

Economic Incentives ◽

The Other ◽

Series Data ◽

Poor Countries ◽

Northern India

It is alleged that the agricultural output in poor countries responds very little to movements in prices and costs because of subsistence-oriented produc¬tion and self-produced inputs. The work of Gupta and Majid is concerned with the empirical verification of the responsiveness of farmers to prices and marketing policies in a backward region. The authors' analysis of the respon¬siveness of farmers to economic incentives is based on two sets of data (concern¬ing sugarcane, cash crop, and paddy, subsistence crop) collected from the district of Deoria in Eastern U.P. (Utter Pradesh) a chronically foodgrain deficit region in northern India. In one set, they have aggregate time-series data at district level and, in the other, they have obtained data from a survey of five villages selected from 170 villages around Padrauna town in Deoria.

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Intra-domain and cross-domain transfer learning for time series

10.5194/egusphere-egu21-12142 ◽

2021 ◽

Author(s):

Erik Otović ◽

Marko Njirjak ◽

Dario Jozinović ◽

Goran Mauša ◽

Alberto Michelini ◽

...

Keyword(s):

Machine Learning ◽

Time Series ◽

Transfer Learning ◽

Time Series Data ◽

The Other ◽

Series Data ◽

Sound Recognition ◽

Transfer Of Knowledge ◽

Learning Models ◽

Machine Learning Models

In this study, we compared the performance of machine learning models trained using transfer learning and those that were trained from scratch - on time series data. Four machine learning models were used for the experiment. Two models were taken from the field of seismology, and the other two are general-purpose models for working with time series data. The accuracy of selected models was systematically observed and analyzed when switching within the same domain of application (seismology), as well as between mutually different domains of application (seismology, speech, medicine, finance). In seismology, we used two databases of local earthquakes (one in counts, and the other with the instrument response removed) and a database of global earthquakes for predicting earthquake magnitude; other datasets targeted classifying spoken words (speech), predicting stock prices (finance) and classifying muscle movement from EMG signals (medicine). In practice, it is very demanding and sometimes impossible to collect datasets of tagged data large enough to successfully train a machine learning model. Therefore, in our experiment, we use reduced data sets of 1,500 and 9,000 data instances to mimic such conditions. Using the same scaled-down datasets, we trained two sets of machine learning models: those that used transfer learning for training and those that were trained from scratch. We compared the performances between pairs of models in order to draw conclusions about the utility of transfer learning. In order to confirm the validity of the obtained results, we repeated the experiments several times and applied statistical tests to confirm the significance of the results. The study shows when, within the set experimental framework, the transfer of knowledge brought improvements in terms of model accuracy and in terms of model convergence rate. Our results show that it is possible to achieve better performance and faster convergence by transferring knowledge from the domain of global earthquakes to the domain of local earthquakes; sometimes also vice versa. However, improvements in seismology can sometimes also be achieved by transferring knowledge from medical and audio domains. The results show that the transfer of knowledge between other domains brought even more significant improvements, compared to those within the field of seismology. For example, it has been shown that models in the field of sound recognition have achieved much better performance compared to classical models and that the domain of sound recognition is very compatible with knowledge from other domains. We came to similar conclusions for the domains of medicine and finance. Ultimately, the paper offers suggestions when transfer learning is useful, and the explanations offered can provide a good starting point for knowledge transfer using time series data.

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A Study of Intercept Adjusted Markov Switching Vector Autoregressive Model in Economic Time Series Data

Information Management and Business Review ◽

10.22610/imbr.v5i8.1065 ◽

2013 ◽

Vol 5 (8) ◽

pp. 379-384

Author(s):

Seuk Wai ◽

Mohd Tahir Ismail . ◽

Siok Kun Sek .

Keyword(s):

Time Series ◽

Time Series Data ◽

Markov Switching ◽

Stock Index ◽

Smooth Transition ◽

Series Data ◽

Var Model ◽

Economic Time Series ◽

Vector Autoregressive ◽

Economic Time

Commodity price always related to the movement of stock market index. However real economic time series data always exhibit nonlinear properties such as structural change, jumps or break in the series through time. Therefore, linear time series models are no longer suitable and Markov Switching Vector Autoregressive models which able to study the asymmetry and regime switching behavior of the data are used in the study. Intercept adjusted Markov Switching Vector Autoregressive (MSI-VAR) model is discuss and applied in the study to capture the smooth transition of the stock index changes from recession state to growth state. Results found that the dramatically changes from one state to another state are continuous smooth transition in both regimes. In addition, the 1-step prediction probability for the two regime Markov Switching model which act as the filtered probability to the actual probability of the variables is converged to the actual probability when undergo an intercept adjusted after a shift. This prove that MSI-VAR model is suitable to use in examine the changes of the economic model and able to provide significance, valid and reliable results. While oil price and gold price also proved that as a factor in affecting the stock exchange.

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Inference and Validation of The Structure of Lotka-Volterra Models

10.21203/rs.3.rs-850869/v1 ◽

2021 ◽

Author(s):

Eberhard Voit ◽

Jacob Davis ◽

Daniel Olivenca

Keyword(s):

Time Series ◽

Time Series Data ◽

The Other ◽

Microbial Populations ◽

Series Data ◽

Observation Data ◽

Volterra Models ◽

Rich Data ◽

Over Time ◽

Mixed Community

Abstract For close to a century, Lotka-Volterra (LV) models have been used to investigate interactions among populations of different species. For a few species, these investigations are straightforward. However, with the arrival of large and complex microbiomes, unprecedently rich data have become available and await analysis. In particular, these data require us to ask which microbial populations of a mixed community affect other populations, whether these influences are activating or inhibiting and how the interactions change over time. Here we present two new inference strategies for interaction parameters that are based on a new algebraic LV inference (ALVI) method. One strategy uses different survivor profiles of communities grown under similar conditions, while the other pertains to time series data. In addition, we address the question of whether observation data are compliant with the LV structure or require a richer modeling format.

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A Refined Approach for Forecasting Based on Neutrosophic Time Series

Symmetry ◽

10.3390/sym11040457 ◽

2019 ◽

Vol 11 (4) ◽

pp. 457 ◽

Cited By ~ 4

Author(s):

Mohamed Abdel-Basset ◽

Victor Chang ◽

Mai Mohamed ◽

Florentin Smarandche

Keyword(s):

Time Series ◽

Time Series Data ◽

Historical Data ◽

High Order ◽

The Other ◽

Series Data ◽

Logical Relationship ◽

Suggested Approach ◽

Intuitionistic Fuzzy

This research introduces a neutrosophic forecasting approach based on neutrosophic time series (NTS). Historical data can be transformed into neutrosophic time series data to determine their truth, indeterminacy and falsity functions. The basis for the neutrosophication process is the score and accuracy functions of historical data. In addition, neutrosophic logical relationship groups (NLRGs) are determined and a deneutrosophication method for NTS is presented. The objective of this research is to suggest an idea of first-and high-order NTS. By comparing our approach with other approaches, we conclude that the suggested approach of forecasting gets better results compared to the other existing approaches of fuzzy, intuitionistic fuzzy, and neutrosophic time series.

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Identifikasi Model I-Garch (Integrated Generalized Autoregressive Conditionally Heterocedastic) Untuk Peramalan Value At Risk

Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika ◽

10.31316/j.derivat.v3i1.626 ◽

2020 ◽

Vol 3 (1) ◽

pp. 25-38

Author(s):

Nendra Mursetya Somasih Dwipa

Keyword(s):

Time Series ◽

Stock Returns ◽

Confidence Level ◽

Value At Risk ◽

Time Series Data ◽

Composite Index ◽

Information Criteria ◽

Stock Index ◽

Series Data ◽

Residual Variance

A stock returns data are one of type time series data who has a high volatility and different variance in every point of time. Such data are volatile, seting up a pattern of asymmetrical, having a nonstationary model, and that does not have a constant residual variance (heteroscedasticity). A time series ARCH and GARCH model can explain the heterocedasticity of data, but they are not always able to fully capture the asymmetric property of high frequency. Integrated Generalized Autoregresive Heteroskedascticity (IGARCH) model overcome GARCH weaknesses in capturing unit root. Furthermore IGARCH models were used to estimate the value of VaR as the maximum loss that will be obtained during a certain period at a certain confidence level. The aim of this study was to determine the best forecasting model of Jakarta Composite Index (JSI). The model had used in this study are ARCH, GARCH, and IGARCH. From the case studies were carried out, the result of forecasting volatility of stock index by using IGARCH(1,1) obtained log likelihood values that 3857,979 to the information criteria AIC = -6,3180; BIC = -6,3013; SIC = -6,3180; dan HQIC = -6,3117. Value of VaR movement of the JCI if it becomes greater the investment is Rp.500,000,000.00 with a confidence level of 95% on the date of July 2, 2015 using a model IGARCH (1,1) is Rp7.166.315,00.

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Multivariate Time Series Data Prediction Based on ATT-LSTM Network

Applied Sciences ◽

10.3390/app11209373 ◽

2021 ◽

Vol 11 (20) ◽

pp. 9373

Author(s):

Jie Ju ◽

Fang-Ai Liu

Keyword(s):

Time Series ◽

Deep Learning ◽

Time Series Data ◽

Mutual Influence ◽

Multivariate Time Series ◽

The Other ◽

Series Data ◽

Data Sets ◽

Learning Models ◽

Prediction Problems

Deep learning models have been widely used in prediction problems in various scenarios and have shown excellent prediction effects. As a deep learning model, the long short-term memory neural network (LSTM) is potent in predicting time series data. However, with the advancement of technology, data collection has become more accessible, and multivariate time series data have emerged. Multivariate time series data are often characterized by a large amount of data, tight timeline, and many related sequences. Especially in real data sets, the change rules of many sequences will be affected by the changes of other sequences. The interacting factors data, mutation information, and other issues seriously impact the prediction accuracy of deep learning models when predicting this type of data. On the other hand, we can also extract the mutual influence information between different sequences and simultaneously use the extracted information as part of the model input to make the prediction results more accurate. Therefore, we propose an ATT-LSTM model. The network applies the attention mechanism (attention) to the LSTM to filter the mutual influence information in the data when predicting the multivariate time series data, which makes up for the poor ability of the network to process data. Weaknesses have greatly improved the accuracy of the network in predicting multivariate time series data. To evaluate the model’s accuracy, we compare the ATT-LSTM model with the other six models on two real multivariate time series data sets based on two evaluation indicators: Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The experimental results show that the model has an excellent performance improvement compared with the other six models, proving the model’s effectiveness in predicting multivariate time series data.

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Generation of Time-Series Working Patterns for Manufacturing High-Quality Products through Auxiliary Classifier Generative Adversarial Network

Sensors ◽

10.3390/s22010029 ◽

2021 ◽

Vol 22 (1) ◽

pp. 29

Author(s):

Manas Bazarbaev ◽

Tserenpurev Chuluunsaikhan ◽

Hyoseok Oh ◽

Ga-Ae Ryu ◽

Aziz Nasridinov ◽

...

Keyword(s):

Time Series ◽

Deep Learning ◽

Product Quality ◽

Time Series Data ◽

The Other ◽

Series Data ◽

Learning Models ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Proposed Model

Product quality is a major concern in manufacturing. In the metal processing industry, low-quality products must be remanufactured, which requires additional labor, money, and time. Therefore, user-controllable variables for machines and raw material compositions are key factors for ensuring product quality. In this study, we propose a method for generating the time-series working patterns of the control variables for metal-melting induction furnaces and continuous casting machines, thus improving product quality by aiding machine operators. We used an auxiliary classifier generative adversarial network (AC-GAN) model to generate time-series working patterns of two processes depending on product type and additional material data. To check accuracy, the difference between the generated time-series data of the model and the ground truth data was calculated. Specifically, the proposed model results were compared with those of other deep learning models: multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). It was demonstrated that the proposed model outperformed the other deep learning models. Moreover, the proposed method generated different time-series data for different inputs, whereas the other deep learning models generated the same time-series data.

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The Dynamics of Credit Procyclicality and Stability of Macroeconomics in Indonesia

Jurnal Ekonomi Kuantitatif Terapan ◽

10.24843/jekt.2021.v14.i02.p07 ◽

2021 ◽

pp. 351

Author(s):

Ni Putu Nina Eka Lestari ◽

Made Kembar Sri Budhi ◽

I Ketut Sudama ◽

Ni Nyoman Reni Suasih ◽

I Nyoman Taun

Keyword(s):

Economic Growth ◽

Time Series ◽

Time Series Data ◽

Financial System ◽

The Other ◽

Series Data ◽

Credit Growth ◽

Macroeconomic Variables ◽

Long Run ◽

The Relationship

Credit growth is one of the important indicators of the financial system that can drive the country economic growth, but on the other hand credit growth can also cause risks in the financial system due to the economic actors’ moral hazard. The purpose of this research is to analyze the credit procyclicality pattern and economic growth in Indonesia. In addition, this study aims to determine the relationship pattern between credit and some macroeconomic variables. The method used is VECM with quarterly time series data during 1998 until 2016. The analysis shows that credit growth and economic growth have positive causality. This shows pro-cyclicality between credit and economic growth in Indonesia. However, in the long run this pattern shows a downward trend although still positive and permanent, which means that excessive credit growth can also lead to a decline in economic growth.

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Inference and Validation of the Structure of Lotka-Volterra Models

10.1101/2021.08.14.456346 ◽

2021 ◽

Author(s):

Eberhard Voit ◽

Jacob Davis ◽

Daniel Olivenca

Keyword(s):

Time Series ◽

Time Series Data ◽

The Other ◽

Microbial Populations ◽

Series Data ◽

Observation Data ◽

Volterra Models ◽

Rich Data ◽

Over Time ◽

Mixed Community

For close to a century, Lotka-Volterra (LV) models have been used to investigate interactions among populations of different species. For a few species, these investigations are straightforward. However, with the arrival of large and complex microbiomes, unprecedently rich data have become available and await analysis. In particular, these data require us to ask which microbial populations of a mixed community affect other populations, whether these influences are activating or inhibiting and how the interactions change over time. Here we present two new inference strategies for interaction parameters that are based on a new algebraic LV inference (ALVI) method. One strategy uses different survivor profiles of communities grown under similar conditions, while the other pertains to time series data. In addition, we address the question of whether observation data are compliant with the LV structure or require a richer modeling format.

Download Full-text