A Data Organization Method for LSTM and Transformer When Predicting Chinese Banking Stock Prices

The accurate prediction of stock prices is not an easy task. The long short-term memory (LSTM) neural network and the transformer are good machine learning models for times series forecasting. In this paper, we use LSTM and transformer to predict prices of banking stocks in China’s A-share market. It is shown that organizing the input data can help get accurate outcomes of the models. In this paper, we first introduce some basic knowledge about LSTM and present prediction results using a standard LSTM model. Then, we show how to organize the input data during the training period and give the comparison results for not only LSTM but also the transformer model. The numerical results show that the prediction results of LSTM and transformer can be improved after the input data are organized when training.

A Review of the Fractal Market Hypothesis for Trading and Market Price Prediction

This paper provides a review of the Fractal Market Hypothesis (FMH) focusing on financial times series analysis. In order to put the FMH into a broader perspective, the Random Walk and Efficient Market Hypotheses are considered together with the basic principles of fractal geometry. After exploring the historical developments associated with different financial hypotheses, an overview of the basic mathematical modelling is provided. The principal goal of this paper is to consider the intrinsic scaling properties that are characteristic for each hypothesis. In regard to the FMH, it is explained why a financial time series can be taken to be characterised by a 1/t1−1/γ scaling law, where γ>0 is the Lévy index, which is able to quantify the likelihood of extreme changes in price differences occurring (or otherwise). In this context, the paper explores how the Lévy index, coupled with other metrics, such as the Lyapunov Exponent and the Volatility, can be combined to provide long-term forecasts. Using these forecasts as a quantification for risk assessment, short-term price predictions are considered using a machine learning approach to evolve a nonlinear formula that simulates price values. A short case study is presented which reports on the use of this approach to forecast Bitcoin exchange rate values.

Pengaruh Kualitas Sumberdaya Manusia Dan Implementasi Kebijakan Dinas PUPR Terhadap Pembentukan Pola Ruang Di Kecamatan Bissappu Kabupaten Bantaeng

Penelitian ini bertujuan mengidentifikasi dan menganalisis bagaimana pengaruh kualitas sumberdaya manusia dan implementasi kebijakan Dinas PUPR Kabupaten Bantaeng secara bersama-sama terhadap pembentukan pola ruang di Kecamatan Bissappu Kabupatren Bantaeng. Metode analisis yang digunakan dalam penelitian ini yaitu pengujian data-data yang diperoleh berdasarkan data times series atau waktu ke waktu berdasarkan data yang dikumpulkan selama interval waktu lima (5) tahun. Pengujian dalam penelitian ini dilakukan dengan model korelasi. Korelasi yang digunakan antara lain metode korelasi liner sederhana kemudian untuk dilakukan pengujian korelasi berganda. Korelasi berganda yaitu metode statistika yang digunakan untuk mengetahui pola umum hubungan yang mempengaruhi antara variabel. yang diterima kemudian dianalisis manual sesuai prosedur analisis dalam penelitian. Keberhasilan penerapan implementasi kebijakan pelayanan IMB maka semakin kuat pengaruhnya terhadap pembentukan pola ruang dimana rhitung = 0,9805 lebih besar dari r tabel= 0.878 pada taraf signifikansi 5 % dimana H0 dtolak dan H1 diterima. Hasil pengujian dan perhitungan analisis korelasi terhadap Variabel peningkatan Kualitas sumberdaya manusia(pegawai)(X1) dan Implementasi Kebijakan(X2) secara bersama-sama memiliki keterhubungan sangat kuat terhadap Pembentukan Pola ruang sebesar 0.9801. Dimana jika Rhitung lebih besar dari Rtabel sebesar 0,878, pada taraf signifikansi alpha adalah 0.05, maka H0 di tolak dan H1 diterima. This study aims to identify and analyze how the influence of the quality of human resources and the implementation of the policies of the PUPR Office of Bantaeng Regency together on the formation of spatial patterns in Bissappu District, Bantaeng Regency. The analytical method used in this study is testing the data obtained based on times series data or time to time based on data collected over a five (5) year time interval. Tests in this study were conducted with the correlation model. The correlation used includes a simple linear correlation method and then multiple correlation tests are carried out. Multiple correlation is a statistical method used to determine the general pattern of relationships that affect between variables. received is then analyzed manually according to the analytical procedures in the study. Where in this study has more than one independent variable. Looking at the results of the above calculation, the influence of human resources (X1) on the formation of spatial patterns (Y) has a moderate correlation, namely 0.421 where rcount rtable = 0.878 at a significance level of alpha 0.05 then H0 is accepted and H1 is rejected. This proves that improving the quality of human resources (employees) does not have a significant relationship with the formation of spatial patterns. The results of the analysis of correlation calculations in chapter IV show that policy implementation (X2) has a very strong relationship with the formation of spatial patterns (Y). The successful implementation of the IMB service policy implementation, the stronger the influence on the formation of spatial patterns where rcount = 0.9805 is greater than rtable = 0.878 at a significance level of 5% where H0 is rejected and H1 is accepted. The results of testing and calculation of correlation analysis on the Variable of improving the quality of human resources (employees) (X1) and Policy Implementation (X2) together have a very strong relationship to the formation of spatial patterns of 0.9801. Where if Rcount is greater than Rtable of 0.878, at the alpha significance level is 0.05, then H0 is rejected and H1 is accepted

Estimation of AR (2) Model with Dependent Errors for Unbounded Stationary and Nonstationary Time Series

In this paper the GLS and the ML estimators, the variance-covariance matrix, the unbiased for the GLS and the ML estimators of parameters of AR (2) model with constant in case of dependent errors have been derived, the simulation results shown that the values of MSE and Thiel’s U in case of unbounded stationary time series for all sample size T are less than the values of MSE and Thiel’s U in case of unbounded nonstationary time series which approved that the results for unbounded stationary times series are better than the results for unbounded nonstationary times series, and the simulation results for unbounded nonstationary time series shown that by using the measurement of MSE the best case among of all cases of nonstationary which gives the smallest values of MSE is case four when the first and the second conditions of stationary conditions for AR (2) model are exists, while by using the measurement of Thiel’s U the best case among of all cases of nonstationary which gives the smallest values of Thiel’s U is case six when the second and the third conditions of stationary conditions for AR (2) model are exists.

Validation of the altimetry-based water levels from Sentinel-3A and B in the Inner Niger Delta

The comprehension of water level fluctuations and the sustainability of the Inner Niger River Delta (IND) is a major concern for the scientific community, but also for the local population. Located in the centre of Mali, the heart of the Sahel, the delta is characterised by a floodable area of more than 32 000 km2 during the rainy season, which contributes very strongly to the vitality of local ecosystem, and is consequently classified as a Ramsar site under the international Convention for Wetlands. In addition, the Delta acts as an environmental and socio-economic development barometer for the entire sub-region. Nowadays, we can observe an increasing fragility of the delta due to climate change, desertification and human activities, and justifies the need for permanent monitoring. The present study is based on the recent successes of radar altimetry, originally designed to monitor the dynamics topography of the ocean, and now very frequently used to retrieve inland water levels, of lakes, rivers, and wetlands. Previous studies evaluated the performances of several radar altimetry missions including Low Resolution Mode (LRM) (Topex-Poseidon, Jason-1/2/3, ERS-2, ENVISAT, and SARAL, and Synthetic Aperture Radar (SAR) Sentinel-3A missions for water level retrievals over 1992–2017. More than 50 times series of water levels were build at the crossing between water bodies and Sentinel-3A and 3B over 2016–2020. Twenty-four comparisons between in-situ and altimetry-based time-series of water levels were achieved over the IND. RMSE generally lower than 0.7 m and r higher than 0.9 were obtained.

Impact of External Debt on Economic Growth in Nigeria

This study examined external borrowing and economic growth in Nigeria covering the period 1981 – 2019. The main objective of the study is to ascertain the impact of external borrowing on economic growth in Nigeria. Times series data on GDP, external debt, exchange rate, external debt servicing payments and inflation were extracted from the Central Bank of Nigeria (CBN) statistical bulletin 2018 was used for the study. The method of data analysis and evaluation were the unit-root test which was used to ascertain the stationary status of the variables, the linear regression with the application of Ordinary Least Squares (OLS) technique and the Granger causality analysis. The major findings of the study are that all the variables are stationary at first difference I(1), external debt has a negative and insignificant relationship with economic growth in Nigeria ( = -0004912, p-value = 0.6944 > 0.05) and there is no causality relationship existing between external debt and economic growth in Nigeria. The study therefore recommends that the federal government should acquire external debt largely for economic reasons rather than social or political reasons. This would increase the Gross Domestic Product (GDP) of the nation.

The Effect of Labor, Export, and Government Expenditure on Economic Growth

The purpose of this study is to explain the effect of labor on economic growth, the effect of exports on economic growth, the effect of government expenditure on economic growth, the effect of labor, exports, and government expenditure on economic growth. The variables used in this study are labor, exports, and government expenditure. The method used in this research is quantitative method. The type of data used in this study is secondary data in the form of times series data from 1990-2020 which is sourced from Badan Pusat Statistik (BPS). The results showed that (1) labor has a positive and significant effect on economic growth, (2) Exports has positive and significant effect on economic growth (3) Government expenditure has positive and significant on economic growth (4) Labor, exports, and government expenditure together affect on economic growth 96.1%.

Public Expenditure and Economic Growth of Nepal

This study intended to evaluate the trends of public expenditure and to show the relationship between public expenditure and economic growth in Nepal. In order to fulfill these objectives, the chart, correlation, and regression were employed by using time series data sets over the period of 1974/75 to 20108/19. Economic growth (RGDP) (proxied as the real GDP with rebasing 2009/10) as dependent variable and recurrent expenditure (RE), capital expenditure (CE), expenditure on education (EE), expenditure on health (HE), and expenditure on transportation and communication (TCE) were proxied as public expenditure. The study revealed that there is positive correlation between dependent and predictors. The results of regression also confirmed that there is positive relationship between public expenditure on economic growth of Nepal. Particularly, HE and TCE had negative relationship with RGDP. This study applied 45 annually observed times series data sets and mainly fitted regression model to examine the relationship between public expenditure and economic growth in Nepal. The main policy implication of this study is that government and concern body should give more concern about capital expenditure for enhancing productive activities and attention about recurrent expenditure. Also, education, health, and transportation and communication are the economic infrastructure, so government should most attention to increase expenditure on these sectors that may produce long-run impact on economy.

The Causality Between Industrial Development and Economic Growth: A Case of Pakistan

Industrial sector is the paramount factor in the development of economic growth. This study has analysed the industrial pantomime in case of Pakistan. Times series data has been used for 40 years, from 1980-2020. Initially the data has been checked for stationarity, by applying ADF, test which proved that, all the indicators are stationary at level. This confirms that, we are allowed to apply OLS regression analysis for the desired results. The study concluded that there has been positive and significant relationship between industrial construction and economy activity of Pakistan. Only factor that, induces negative impact on the economic growth, is the excessive consumption of energy resources; growth of electricity, gas and water supply. Foreign direct investment indicated negative association with the economy, but also gives the insignificant results.