Linear Regression vs LSTM for Time Series Data

Shah, Rakshit; Shah, Poojan; Joshi, Catherene; Jain, Rutuja; Nikam, Rushikesh

doi:10.1109/aic55036.2022.9848887

Cited by 4 publications

(2 citation statements)

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“…Rakshit mention the implementation of linear regression and Long Short-Term Memory (LSTM), both of which make predictions using the Indian stock market index Nifty 50. They come to the conclusion that the LSTM model can produce more precise findings and is best suited for time series data [9]. Singh et al presented three types of machine learning (ML) for predicting stock prices, including linear regression, LSTM and decision tree.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Ethereum Prices Using Linear Regression and Long Short-term Memory

Zhang¹

2023

BCPBM

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Contemporarily, the popularity and use of cryptocurrencies has risen along with their prices and Ethereum is the second most popular and largest cryptocurrency after Bitcoin. Cryptocurrencies are based on the blockchain, which is a decentralized technology that has the power to change any banking system. They have become an attractive investment for both traders and individuals looking to invest. The price of Ethereum fluctuates and is affected by various factors, e.g., the crypto trading exchange as well as supply and demand. Ethereum is so valuable because it can be used as cash and one also pay Ethereum in full or in part to someone in exchange. Besides, it is easily guaranteed by the blockchain. Unlike stocks, the price of Ethereum is much more variable because it is traded 24 hours a day and there are no closing times. On this basis, this paper compares the results of two different models, namely linear regression and Long Short-Term Memory networks (LSTM). The dataset comprised in the closing prices of the last 372 days for Ethereum. The performance of the obtained models is critically evaluated using statistical indicators Root Mean Squared Error (RMSE) and the study have drawn our conclusions based on the RMSE result. The paper demonstrates a technique for using time series data in both models and determining each model's RMSE. These results shed light on guiding further exploration of prediction Ethereum prices and trends.

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Section: Introductionmentioning

confidence: 99%

Prediction of Ethereum Prices Using Linear Regression and Long Short-term Memory

Zhang¹

2023

BCPBM

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“…The following is how this document is structured. Section 2 presents an overview of Amazon.com, the corporate background, and describes its stock price data for this year; Section 3 discusses data and model selection, performs data preprocessing, and describes forecasting methods and structural analysis to compare two forecasting models; Section 4 discusses and analyzes the conclusions obtained, comparing the advantages and disadvantages of the LR model and the LSTM model; the last section presents the conclusions of the paper [1].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of two models for predicting Amazon stock based on machine learning

You¹

2022

BCPBM

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With the fast growth of artificial intelligence and technology, the use of machine learning techniques in financial markets is gaining popularity. As a result, many opportunities arise, such as predicting future stock movements. Financial markets are complex and constantly evolving environments, so analyzing them can be challenging and interesting. There are no specific rules to predict or estimate the value of a stock in the stock market, so one can do stock price prediction by various methods. In this project, the stock price data of Amazon for the past five years, ‘Date’, ‘Starting price’, ‘Closing price’, ‘Highest price’, ‘Lowest price’, ‘Adjusted close price’, and 'Volume’ are all included. The data were obtained from Yahoo Finance to predict the future stock price. Two forecasting models, the Linear Regression model, and the Long short-term memory model were analyzed, and based on the comparison of Mean Absolute Error, it was concluded that the LSTM forecasting model was shown to be more effective for forecasting the time-series data category.

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