scholarly journals Adaptive Synapse Arrangement in Cortical Learning Algorithm

2021 ◽  
Vol 25 (4) ◽  
pp. 450-466
Author(s):  
Takeru Aoki ◽  
◽  
Keiki Takadama ◽  
Hiroyuki Sato
Keyword(s):  
New York ◽  
Time Series ◽  
Prediction Accuracy ◽  
Input Data ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Prediction Method ◽  
Series Data ◽  
Load Prediction ◽  
Data Prediction

The cortical learning algorithm (CLA) is a time-series data prediction method that is designed based on the human neocortex. The CLA has multiple columns that are associated with the input data bits by synapses. The input data is then converted into an internal column representation based on the synapse relation. Because the synapse relation between the columns and input data bits is fixed during the entire prediction process in the conventional CLA, it cannot adapt to input data biases. Consequently, columns not used for internal representations arise, resulting in a low prediction accuracy in the conventional CLA. To improve the prediction accuracy of the CLA, we propose a CLA that self-adaptively arranges the column synapses according to the input data tendencies and verify its effectiveness with several artificial time-series data and real-world electricity load prediction data from New York City. Experimental results show that the proposed CLA achieves higher prediction accuracy than the conventional CLA and LSTMs with different network optimization algorithms by arranging column synapses according to the input data tendency.

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.

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.

scholarly journals Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests

2020 ◽  
Vol 12 (11) ◽  
pp. 1876 ◽  
Author(s):  
Katsuto Shimizu ◽  
Tetsuji Ota ◽  
Nobuya Mizoue ◽  
Hideki Saito
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Forest Monitoring ◽  
Series Data ◽  
Landsat 8 ◽  
Multi Temporal ◽  
Sentinel 2

Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.

scholarly journals ANALISIS DAMPAK KEPEMILIKAN SURAT BERHARGA OLEH BPD TERHADAP PERTUMBUHAN EKONOMI DAERAH

2018 ◽  
Vol 14 (1) ◽  
pp. 32-47
Author(s):  
Khairur dan Telisa Aulia Falian Raziqiin ◽  
Telisa Aulia Falian
Keyword(s):  
Economic Growth ◽  
New York ◽  
Time Series ◽  
Regional Development ◽  
Time Series Data ◽  
Financial Institution ◽  
Positive Influence ◽  
Regional Economic Growth ◽  
Series Data ◽  
Regional Economic

Local government-owned banks (BPD), was established in order to help accelerate the development of the area where the BPD located. The expected goals of this study are: To measure the effect of the placement of funds by BPD on regional economic growth, to measure investment lending by BPD to regional economic growth. Population was all the existing Regional Development Bank in Indonesia. Based on data from Bank Indonesia, the number of regional development banks perDesember 2013 as many as 26 banks. The type of data that will be used in this research is time series data (time series) from January 2009 until December 2013 The model that will be used in this research is the use of panel data. Results of research on Analysis of Impact of Ownership of Securities by BPD Against Regional Development, government capital spending, credit productive, ownership of securities by BPD positive effect on GDP, and significantly affect GDP, labor force have a positive influence on the GDP, but the effect was not significant workforce to GDP.Badan Pusat Statistik. Berbagai tahun. Data Realisasi APBD. Badan PusatStatistik, Jakarta. Bank Indonesia. Berbagai tahun. Laporan Publikasi Bank Umum. Bank Indonesia,Jakarta. Budiono. (2001). Ekonomi Moneter Edisi 3. Yogyakarta : BPFE Djojosubroto, Dono Iskandar. (2004). Koordinasi Kebijakan Fiskal dan Moneter di Indonesia Pasca Undang – undang Bank Indonesia 1999. Jakarta : Kompas Dornbusch, Rudiger, Stanley Fischer, Richard Startz. (2004). Makroekonomi. (Yusuf Wibisono, Roy Indra Mirazudin, terjemahan). Jakarta :MediaGlobal Edukasi. Gujarati, Damodar. (1997). Ekonometrika Dasar. (Sumarno Zein, terjemahan).Jakarta : Erlangga. Gultom, Lukdir. (2013). Tantangan Meningkatkan Efisiensi dan Efektifitas BPD sebagai Regional Champion Dalam Pengembangan Usaha Mikro, Kecil dan Menengah di Indonesia, Makalah SESPIBI Angkatan XXXI (Tidak Dipublikasikan). Bank Indonesia. Husnan, Suad. (2003). Dasar – dasar Teori Portofolio dan Analisis Sekuritas.Yogyakarta : UPP AMP YKPN. Kasmir. (2002). Dasar – Dasar Perbankan. Jakarta : PT. Raja Grafindo Persada. Kuncoro, Mudrajad. (2001) Metode Kuantitatif : Teori dan Aplikasi untuk Bisnis dan Ekonomi. Yogyakarta : AMP YKPN. Latumaerissa dan Julius R. (1999). Mengenal Aspek-aspek Operasi Bank Umum. Jakarta : Bumi Aksara. Lipsey, Richard G, et al. (1997). Pengantar Makro Ekonomi. ( Jaka Wasana danKibrandoko, terjemahan). Jakarta :Binarupa Aksara. Mankiw, Gregory. (2000). Macroeconomics Theory. New York : Worth PublisherInc. Nachrowi, Nachrowi D., Hardius Usman. (2006). Pendekatan Populer dan Praktis EKONOMETRIKA untuk Analisis Ekonomi dan Keuangan.Jakarta : Lembaga Penerbit FEUI. Rahmany, A. Fuad. (2004). Era Baru Kebijakan Fiskal : Pemikiran, Konsep dan Implementasi. Jakarta : Penerbit Buku Kompas, hal. 445 – 462. Rivai, Veithzal, Andria Permata Veithzal, Ferry N. Idroes. (2007). Bank and Financial Institution Management : Conventional & Sharia System, Jakarta : RajaGrafindo Persada. Sunarsip. (2008). Relasi Bank Pembangunan Daerah dan Perekonomian Daerah, dimuat dalam Republika, Rabu, 9 Januari 2008. Rubrik Pareto hal.16 Sunarsip. (2011). Transformasi BPD. Dimuat Infobank Edisi Januari 2011. Republik Indonesia, Kementrian Keuangan (2010), Potensi Bank Pembangunan Daerah Sebagai Pendiri Dana Pensiun Lembaga Keuangan,Tim Studi Potensi Bank Pembangunan Daerah Sebagai Pendiri Dana Pensiun. Jakarta.Waluyanto, Rahmat. (2004). Era Baru Kebijakan Fiskal : Pemikiran, Konsep dan Implementasi. Jakarta : Penerbit Buku Kompas, hal. 463 – 508. Wuryandari, Gantiah. (2013). Mengusung Bank Pembangunan Daerah (BPD) Sebagai Bank Fokus Sektor Strategis Dalam Mendukung Pembangunan Nasional, Makalah SESPIBI Angkatan XXXI (Tidak Dipublikasikan). Bank Indonesia.

GRU-corr Neural Network Optimized by Improved PSO Algorithm for Time Series Prediction

2020 ◽  
Vol 29 (07n08) ◽  
pp. 2040010
Author(s):  
Shao-Pei Ji ◽  
Yu-Long Meng ◽  
Liang Yan ◽  
Gui-Shan Dong ◽  
Dong Liu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Pso Algorithm ◽  
Series Data ◽  
Multi Scale ◽  
Network Parameters ◽  
Angle Data

Time series data from real problems have nonlinear, non-smooth, and multi-scale composite characteristics. This paper first proposes a gated recurrent unit-correction (GRU-corr) network model, which adds a correction layer to the GRU neural network. Then, a adaptive staged variation PSO (ASPSO) is proposed. Finally, to overcome the drawbacks of the imprecise selection of the GRU-corr network parameters and obtain the high-precision global optimization of network parameters, weight parameters and the hidden nodes number of GRU-corr is optimized by ASPSO, and a time series prediction model (ASPSO-GRU-corr) is proposed based on the GRU-corr optimized by ASPSO. In the experiment, a comparative analysis of the optimization performance of ASPSO on a benchmark function was performed to verify its validity, and then the ASPSO-GRU-corr model is used to predict the ship motion cross-sway angle data. The results show that, ASPSO has better optimization performance and convergence speed compared with other algorithms, while the ASPSO-GRU-corr has higher generalization performance and lower architecture complexity. The ASPSO-GRU-corr can reveal the intrinsic multi-scale composite features of the time series, which is a reliable nonlinear and non-steady time series prediction method.

scholarly journals STL-ATTLSTM: Vegetable Price Forecasting Using STL and Attention Mechanism-Based LSTM

Agriculture ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 612
Author(s):  
Helin Yin ◽  
Dong Jin ◽  
Yeong Hyeon Gu ◽  
Chang Jin Park ◽  
Sang Keun Han ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Crop Production ◽  
Time Series Data ◽  
Short Term Memory ◽  
Attention Mechanism ◽  
Series Data ◽  
Percentage Error ◽  
Hot Pepper ◽  
Types Of Information

It is difficult to forecast vegetable prices because they are affected by numerous factors, such as weather and crop production, and the time-series data have strong non-linear and non-stationary characteristics. To address these issues, we propose the STL-ATTLSTM (STL-Attention-based LSTM) model, which integrates the seasonal trend decomposition using the Loess (STL) preprocessing method and attention mechanism based on long short-term memory (LSTM). The proposed STL-ATTLSTM forecasts monthly vegetable prices using various types of information, such as vegetable prices, weather information of the main production areas, and market trading volumes. The STL method decomposes time-series vegetable price data into trend, seasonality, and remainder components. It uses the remainder component by removing the trend and seasonality components. In the model training process, attention weights are assigned to all input variables; thus, the model’s prediction performance is improved by focusing on the variables that affect the prediction results. The proposed STL-ATTLSTM was applied to five crops, namely cabbage, radish, onion, hot pepper, and garlic, and its performance was compared to three benchmark models (i.e., LSTM, attention LSTM, and STL-LSTM). The performance results show that the LSTM model combined with the STL method (STL-LSTM) achieved a 12% higher prediction accuracy than the attention LSTM model that did not use the STL method and solved the prediction lag arising from high seasonality. The attention LSTM model improved the prediction accuracy by approximately 4% to 5% compared to the LSTM model. The STL-ATTLSTM model achieved the best performance, with an average root mean square error (RMSE) of 380, and an average mean absolute percentage error (MAPE) of 7%.

A Chaotic Prediction Method to Time Series Data

2010 ◽  
Vol 113-116 ◽  
pp. 1367-1370 ◽  
Author(s):  
Bin Sheng Liu ◽  
Ying Wang ◽  
Xue Ping Hu
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Time Series ◽  
Drinking Water ◽  
Time Series Data ◽  
Prediction Method ◽  
Drinking Water Quality ◽  
Series Data ◽  
Gray Model ◽  
Forecast Method

There are many ways to predict drinking water quality such as neural network, gray model, ARIMA. But the prediction precise is need to improve. This paper proposes a new forecast method according the characteristic of drinking water quality and the evidence showed that the prediction is effectively. So it is able to being used in actual prediction.

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