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52 articles for “Moving Average”
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The Comparison in Time Series Forecasting of Air Traffic Data by Autoregressive Integrated Moving Average Model, Radial Basis Function and Elman Recurrent Neural Networks
Abstract: Nowadays, nonlinear time series and artificial neural networks (ANN) models are used for forecasting in the field of business, agriculture and soon. Recent studies have shown, ANN have been successfully used for forecasting of financial and agriculture data series The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. ANN have more advantages that can approximate to …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 3, 2018 · pp. 75–90 Read article
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Improving Energy Management Systems with SARIMA-Based Forecasting of Household Energy Consumption
Abstract: Household energy consumption is a dynamic and multifaceted domain influenced by various factors, including seasonality, weather conditions, and individual consumption habits. For homeowners looking to control expenses, lessen their impact on the environment, and contribute to a sustainable future, accurate forecasting is essential. Precise forecasting is also essential for utility firms to maximize energy production, distribution, and demand control. In a time when resource efficiency and environmental awareness are paramount, …
Published in Journal of Alternate Energy Sources & Technologies · Vol. 15, Issue 1, 2024 Read article
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Second Wave COVID-19 Predictions and Forecasting of Confirmed Cases in West Bengal Using ARIMA Model
Abstract: Infection and death rates surged drastically during the second wave of the COVID-19 (called delta variant) in India, owing to the destructive virus. As our country's economic load makes it more difficult to control the measures and it is critical for states such as West Bengal to forecast future cases. The present study introduced a time series forecasting model aimed at predicting and forecasting the number of confirmed and active …
Published in Research and Reviews : A Journal of Immunology · Vol. 13, Issue 1, 2023 · pp. 1–8 Read article
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Using Elliot Wave Theory and Fibonacci Retracement and in Algorithmic Trading
Abstract: The analysis technique used in predicting stock prices consists of Fundamental analysis and Technical analysis. These analyses are complex in nature and usually unreliable. The algorithmic trading systems offered today consists of primitive technical analysis techniques such as, simple moving averages, exponential moving average, moving averages convergence and diversions and volume weighted average price. The primitive nature of this techniques makes them very unreliable and has a low success rate. …
Published in Trends in Machine design · Vol. 7, Issue 3, 2020 · pp. 22–29 Read article
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Stock Trading Algorithm with Technical Indicators Using Python for Indian Stocks
Abstract: In the financial markets, algorithmic trading has become a potent instrument that enables traders to automate their tactics and profit from market inefficiencies. In this paper, we describe a Python-based algorithmic trading model that makes use of technical indicators and evaluate its performance in comparison to the well-known buy-and-hold approach. By analysing historical price data, these indicators provide insights into the market trends and potential entry or exit points for …
Published in E-Commerce for Future & Trends · Vol. 10, Issue 2, 2023 · pp. 8–17 Read article
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Time Series Model for National Stock Price Prediction
Abstract: The national stock exchange is widest and fully automatic trading system in India. Analysis and prediction of stock market time series data have involved considerable interest from the researchers over the last decade. In this paper, the Nifty 50 closing stock market prices were computed and predicted the trend of stock market fluctuations using time series modeling techniques, like exponential smoothing and autoregressive integrated moving average. The forecasted values of …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 1, 2018 · pp. 85s–90s Read article
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Exploring the Efficiency of Leading and Lagging Indicators in Algorithmic Trading
Abstract: This paper details a comparison of the overall performance of leading and lagging technical indicators used in algorithmic trading over an extended period. While much of the prior research focuses on index price forecasting and some on statistical arbitrage derived from these predictive techniques, there is a scarcity of studies that assess and evaluate trading strategies. The strategies considered for the study were tested on historical data of the 50 …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 2, Issue 2, 2024 · pp. 8–18 Read article
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Time Series Forecasting of Electricity Consumption: A Comparative Analysis of ARIMA and SARIMA Models
Abstract: Accurate electricity demand forecasting plays a vital role in energy planning, efficient power system operation, and sustainable resource management. This study conducts a comparative evaluation of the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models using ten years of monthly electricity consumption data collected from a national electricity regulatory authority. The performance of both models is assessed using forecasting accuracy metrics, including Mean Absolute Error …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 2, 2026 · pp. 43–53 Read article
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Digital Front-End Architecture of ECG Acquisition System with 0.5V Supply
Abstract: AbstractDue to the popularity of portable electronic products, low power systems have attracted more attention in recent years. Dynamic power consumption remains to be the biggest contributor to the total power consumption of a hardware design. A new power efficient electrocardiogram acquisition system that uses a fully digital architecture to reduce the power consumption and chip area has been proposed in this paper. The design of analog-to-digital converters (ADCs) is …
Published in Journal of Semiconductor Devices and Circuits · Vol. 3, Issue 2, 2016 · pp. 11–20 Read article
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Modeling and Forecasting Rice Production in Bangladesh: An Econometric Analysis
Abstract: The aim of this research is to find time series model and to forecast the rice production of Bangladesh. That is achieved by finding the tentative Autoregressive Integrated Moving Average (ARIMA) models that fit and forecast well for rice production in Bangladesh. There are mainly three types of rice that is cultivated in Bangladesh throughout the year as Aus, Aman and Boro. Season covering the whole country we have included …
Published in Research & Reviews : Journal of Statistics · Vol. 8, Issue 2, 2019 · pp. 10–28 Read article
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Non-Invasive Glucose Monitoring Device Using Max30102 Sensor
Abstract: Diabetes mellitus is a chronic metabolic disorder affecting millions globally, requiring continuous blood glucose monitoring to prevent complications such as cardiovascular disease, kidney failure, neuropathy, and retinopathy. Conventional invasive finger- prick techniques result in pain, skin irritation, and an increased risk of infection, which lowers patient compliance, particularly in young patients and the elderly. This paper presents a non-invasive glucose monitoring prototype using the MAX30102 optical biosensor interfaced with the …
Published in Journal of Instrumentation Technology & Innovations · Vol. 16, Issue 1, 2026 · pp. 36–44 Read article
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Seasonal ARIMA for Forecasting Sea Surface Temperature of the North Zone of the Bay of Bengal
Abstract: The behavior of the sea surface temperature (SST) of the north zone of the Bay of Bengal plays an important role for understanding climate changes over Bangladesh. The monthly average of SST of this zone is used in this study which is obtained from January 1900 to December 2009. Box and Jenkins method is used to fit a seasonal auto regressive integrated moving average (ARIMA) model used in forecasting SST …
Published in Research & Reviews : Journal of Statistics · Vol. 2, Issue 2, 2013 · pp. 23–31 Read article
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Stock Market Analysis Using Data Science
Abstract: Stock market prediction using data science has become a popular area of research and application in recent years. This is because the stock market is a complex system with many variables and factors that affect its behavior, making it difficult to predict with certainty. The stock market has always been the aggression of buyers and sellers of stocks, therefore in the global finance market, stock trading is one of the …
Published in E-Commerce for Future & Trends · Vol. 11, Issue 1, 2024 · pp. 1–4 Read article
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Time Series Sales Forecasting Using ARIMA Model
Abstract: Sales forecasting is a critical application in various industries and presents one of the most challenging problems worldwide. One method of prediction involves identifying patterns in historical data, where the outcome is known in advance and can be validated using more recent data. If a pattern consistently leads to the same outcome, it can be considered a genuine relationship. This method is highly flexible and can be utilized with diverse …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 2, Issue 1, 2024 · pp. 17–27 Read article
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Time Series Methods in Meteorology: A Review of Predictive Models and Applications
Abstract: The accurate prediction of time series data holds substantial significance in various fields, enabling informed decision-making and resource optimization. In this study, temperature variations over time are predicted using the Autoregressive Integrated Moving Average (ARIMA) model. Reliable temperature projections are more important now than ever because of climate change and its effects. For time series prediction problems, the ARIMA model—which is well-known for its ability to capture temporal dependencies in …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 13, Issue 2, 2024 · pp. 35–46 Read article
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Study of Social Trends Prediction Using AI
Abstract: AI (Artificial Intelligence) has fundamentally changed the ability to analyze social trends by using large datasets to develop predictions about human behavior, public sentiment, and global events. Using methodologies such as Natural Language Processing (NLP), Time-Series Forecasting, and Graph-Based Social Network Analysis, AI is able to find hidden correlations in a variety of available datasets, from social media to economic indicators to public records, and fundamentally changes decision-making based on …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 3, 2025 · pp. 19–29 Read article
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Real-Time Edge Detection Camera Module Using Discrete Taylor Transform and Heat Equation (PDE): An Applied Mathematical Approach
Abstract: In modern digital signal processing, the capability for denoising and smoothing in real time is very important in scientific, engineering, and industrial applications. This paper presents an efficient hybrid framework that merges two mathematically sound methods, namely, DTT and PDE defined as the Heat Equation, to robustly denoise a signal with minimal distortion. The model addresses one of the most challenging tasks in signal restoration, which maintains the fidelity of …
Published in Current Trends in Signal Processing · Vol. 16, Issue 2, 2026 · pp. 9–14 Read article
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Stationary Wavelet and Recursive Least Square Filtering Based Fetal ECG Data Extraction from Composite Abdominal Signal
Abstract: This paper introduces a fast methodology for fetal ECG extraction based on stationary wavelet and noise canceler adaptive filter with the recursive least square filter. Firstly, signals are preprocessed by moving averaging filter for removing baseline wander. The stationary wavelet and the recursive least square filter is applied on preprocessed signal that effectively separates the maternal ECG and extracts fetal ECG (fECG) from the abdominal ECG. Finally, fetal ECG(fECG) is …
Published in Current Trends in Signal Processing · Vol. 7, Issue 2, 2017 · pp. 46–58 Read article
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Implementation of K-Means clustering algorithm with technical indicators to identify Profitable stocks
Abstract: In the ever-changing world of stock market trading, accurately predicting price movements is key to maximizing profits. Technical analysis, which looks at past price data to predict future trends, provides valuable insights for investors. This paper delves into using machine learning methods, particularly the K-Means clustering algorithm, along with moving average data, to categorize daily trading patterns. By breaking down the market into clusters and examining the main patterns within …
Published in Current Trends in Signal Processing · Vol. 13, Issue 3, 2023 · pp. 8–14 Read article
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Reducing the Bullwhip Effect in a Supply Chain using Artificial Intelligence Technique
Abstract: The bullwhip effect in a supply chain leads to various inefficiencies like excessive inventory, quality problems, higher raw material costs, and poor customer service, which can be reduced by coordination and collaboration among partners of a supply chain with time bound information sharing; it requires full integration. However, full integration of the organizations of a supply chain is not possible in real case scenario due to existing differences of functions, …
Published in Journal of Production Research & Management · Vol. 4, Issue 2, 2014 · pp. 31–42 Read article