- Predicting the Price of Bitcoin Using Machine Learning (LSTM) network. The LSTM achieves the highest classification accuracy of 52% and a RMSE of 8%. The popular ARIMA model for time series forecasting is implemented as a comparison to the deep learning models. As expected, the non-linear deep learning methods outperform the ARIMA forecast which performs poorly. Finally, both deep learning.
- 6. Predict the price of cryptocurrency using LSTM neural network (deep learning) This is the model-building stage. Finding the right model is an art, and it will take several tweaks and attempts to find the right layers and hyperparameters for each one. The model building is quite simple and standard for this type of problem
- Line 21: train the model using inputs x_train and labels y_train. #4. Predict price. Finally we came to the long-awaited moment of predicting the price. We have 2 steps: predict the price and plot it to compare with the real results. Predict the price for the next month; As you already saw, Keras makes everything so easy. Here the case remains the same
- Saxena A, Sukumar TR (2018) Predicting bitcoin Price using lstm and compare its predictability with Arima model. Int J Pure Appl Math 2591-2600 Google Scholar 11. Paresh Kumar N, Narayan S, Rahman RE, Setiawan I (2019) Bitcoin price growth and Indonesia's monetary system
- By comparing the two forecasting plots, we can see that the ARIMA model has predicted the closing prices very lower to the actual prices. This large variation in prediction can be seen at the majority of the places across the plot. But in the case of the LSTM model, the same prediction of closing prices can be seen higher than the actual value. But this variation can be observed at few places in the plot and majority of the time, the predicted value seems to be nearby the actual.
- In , the ARIMA model is compared with the Prophet, multi-layered perceptron, and LSTM to predict the cash flow. The other models outperformed the ARIMA model in the long-term forecast. The seasonal ARIMA underperformed them due to squaring errors for seasonal and holiday effects. The ARIMA model is efficient for short-term prediction if data had a consistent pattern. Since ARIMA models are.
- The study involves the time series forecasting of the bitcoin prices with improved efficiency using long short-term memory techniques (LSTM) and compares its predictability with the traditional method (ARIMA).The RMSE of ARIMA Model is 700.69 whereas for the LSTM is 456.78 which proves that tradition (ARIMA) model outperforms the machine learning algorithms in our case LSTM model

- A Comparison of ARIMA and LSTM in Forecasting Time Series Abstract: In particular, ARIMA model has demonstrated its outperformance in precision and accuracy of predicting the next lags of time series. With the recent advancement in computational power of computers and more importantly development of more advanced machine learning algorithms and approaches such as deep learning, new.
- As an extreme case, I had a chance to study on Forex (Foreign Exchange Rate) forecast and intensively compared performances of LSTM, windowed-MLP and ARIMA. As many articles say, Forex time series is close to the random walk series (it is completely non-stationary). None of these algorithms can predict next day's spot rate. For example, if there is no (or little) change, then it will maintain current value and it looks fit. However, if there is a sudden (substantial) change in tomorrow's.
- utes time-interval data to predict the bitcoin price. McNally et al[6],Predicting the Price of Bitcoin Using Machine Learning they proposed two prediction models based on recurrent neural networks (RNNs) and long short-term memory (LSTM), and compared them with an autoregressive integrate

- Saxena, A., & Sukumar, T. (2018). Predicting bitcoin price using LSTM and compare its predictability with ARIMA model. International Journal of Pure Applied Mathematics, 119(17), 2591-2600. Google Schola
- For the ARIMA model, only adjusted close price was used to ﬁt the model. We used summary statistics and functions such as moving average and autocorrelation function to identify data trends and the parameters (p, d, and q) of ARIMA model. Y t(p;d;q) = + P p p=1 (˚ p Y t p) P p q=1 ( q e t q) where Y t= Y t Y t d (1) RNN with Single/Stacked-LSTM: The main idea of RNN is to apply the.
- bitcoin prices with improved efficiency using long short -term memory techniques (LSTM) and compares its predictability with th e traditional method (ARIMA).TheRMSE of ARIMA Model is 700.69 whereas for the LSTM is 456.78 which proves that tradition (ARIMA) model
- Predicting and Sampling: Adding model, predicting and sampling feature, model structure is: [Andrew Ng, Sequential Models Course, Deep Learning Specialization] Music Inference Model is similar trained model and it is implemented with music_inference_model(LSTM_cell, densor, n_values = 78, n_a = 64, Ty = 100) function. Music is generated with redict_and_sample function. Finally, your generated music is saved in output/my_music.midi
- This notebook demonstrates the prediction of the bitcoin price by the neural network model. We are using 2-layers long short term memory (LSTM) as well as Gated Recurrent Unit (GRU) architecture.

Get the Data. We will build an LSTM model to predict the hourly Stock Prices. The analysis will be reproducible and you can follow along. First, we will need to load the data. We will take as an example the AMZN ticker, by taking into consideration the hourly close prices from ' 2019-06-01 ' to ' 2021-01-07 '. 1 ** Stanford Project: Predicting stock prices using a LSTM-Network**. Introduction: Artificial Intelligence is changing virtually every aspect of our lives. Today's algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how investment decisions are made on a broad scale. Models that explain the returns of individual stocks generally use company and stock.

Our LSTM model will use previous data (both bitcoin and eth) to predict the next day's closing price of a specific coin. We must decide how many previous days it will have access to. Again, it's rather arbitrary, but I'll opt for 10 days, as it's a nice round number. We build little data frames consisting of 10 consecutive days of data (called windows), so the first window will consist. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20. This module will be removed in 0.20., DeprecationWarning) In [2]: link. code. prices_dataset = pd.read_csv('../input/prices.csv', header=0) prices_dataset. Out [2]: date [22] A. Saxena and A. Sukumar, Predicting bitcoin price using lstm And Compare its predictability with arima model, International Journal of Pure and Applied Mathematics, vol. 119, no. 17, 2018, pp. 2591- 2600 ** In stage II, the potential predictors were fed into the LSTM to predict the Bitcoin exchange rate, regardless of the previous exchange rate**. In addition, four models, i.e., ARIMA, SVR, ANFIS, and LSTM, were employed to make predictions based on the previous exchange rate

It is not possible to predict the stock market behaviour using only its historical price. The LSTM prediction is far from acceptable. Even when using the historical price of several companies, the. One method for predicting stock prices is using a long short-term memory neural network (LSTM) for times series forecasting. LSTM: A Brief Explanation . LSTM diagram . L S TMs are an improved version of recurrent neural networks (RNNs). RNNs are analogous to human learning. When humans think, we don't start our thinking from scratch each second. For example, in the sentence Bob plays. In this tutorial, We will talk about how to develop an ARIMA model for time series forecasting in Python. An ARIMA model is a class of statistical models for analyzing and forecasting time series data. It is really simplified in terms of using it, Yet this model is really powerful. ARIMA stands for Auto-Regressive Integrated Moving Average They make predictions based on whether the past recent values were going up or going down (not the exact values). For example, they will say the next day price is likely to be lower, if the prices have been dropping for the past days, which sounds reasonable. However, you will use a more complex model: an LSTM model

Saxena A, Sukumar TR, Nadu T, Nadu T. **Predicting** **bitcoin** **price** **using** **LSTM** **and** **Compare** **its** **predictability** **with** **Arima** **model**. International Journal of Pure and Applied Mathematics. 2018;119(17):2591-2600. Ribeiro AH, Schön TB. Beyond exploding and vanishing gradients : analysing RNN training **using** attractors and smoothness. International Conference on Artificial Intelligence and Statistics. 2020;2370-2380 Forecasting Bitcoin closing price series using linear regression and neural networks models Nicola Uras*, Lodovica Marchesi*, Michele Marchesi and Roberto Tonelli Department of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy * These authors contributed equally to this work. ABSTRACT In this article we forecast daily closing price series of Bitcoin, Litecoin and. The Volterra-JGPC-LSTM model presented in this paper has a significantly higher accuracy for the closing price prediction of Bitcoin than the Volterra model. Compared with the Volterra model in one-step, two-step, and three-step predictions, the RMSE of the Volterra-JGPC-LSTM model reduced by 20.33 %, 22.89 %, and 31.27 % respectively, and the. Table 13 shows a comparison of our results and the benchmark results of McNally , who used the proximal methods including linear ARIMA model and LSTM and RNN machine learning models to predict the Bitcoin daily price. All of our results outperform the benchmark results in both accuracy and precision except for XGB. For linear methods, ARIMA has an overwhelming advantage, with a precision of. comparing the prediction accuracy of **lstm** **and** **arima** **models** for time-series with permanent fluctuation. download. comparing the prediction accuracy of **lstm** **and** **arima** **models** for time-series with permanent fluctuation. mohammad ardal. related papers. russian approach to ico regulation. by aleksandr alekseenko. violÊncia contra À mulher e perspectivas de aÇÕes programÁticas: o sentido.

* Predicting bitcoin price using lstm And Compare its predictability with arima model*. Anshu Saxena, T. Sukumar; 2018; 9. PDF. View 2 excerpts, references background and results; Save. Alert. Research Feed. A Comparison of ARIMA and LSTM in Forecasting Time Series. Sima Siami-Namini, Neda Tavakoli, A. Namin; Computer Science ; 2018 17th IEEE International Conference on Machine Learning and. predicting the price of bitcoin due to the high volatility of the bitcoin exchange rate. Measurement, estimation, and modeling of currency exchange rate volatility compose a significant research area. For this reason, a lot of studies done about bitcoin price prediction both Machine Learning (ML) and Statistical Methods. In comparison studies, ML methods perform better in general. This review.

** The problem that I am dealing with is predicting time series values**. I am looking at one time series at a time and based on for example 15% of the input data, I would like to predict its future values. So far I have come across two models: LSTM (long short term memory; a class of recurrent neural networks) ARIMA machine learning-based classiﬁcation and regression models for predicting Bitcoin price movements and prices in short and medium terms. In previous works, machine learning-based classiﬁcation has been studied for an only one-day time frame, while this work goes beyond that by using machine learning-based models for one, seven, thirty and ninety days. The developed models are feasible and. Predicting and Forecasting the Price of Constituents and Index of Cryptocurrency Using Machine Learning compared the models with each other to get the best output. We believe that our work will help reduce the challenges and difficulties faced by people, who invest in cryptocurrencies. Moreover, the obtained results can play a major role in cryptocurrency portfolio management and in. In this article we will try to forecast a time series data basically. We'll build three different model with Python and inspect their results. Models we will use are ARIMA (Autoregressive. We analyzed different multivariate models based on different RNN architectures like GRU and LSTM and compares them with their univariate models and also with each other. We have used a soft computing approach based on RNN and models has been developed to find the temporal dependencies and forecast stock values of a particular company from its past history of stocks. We propose a multivariate.

A new deep learning, i.e., LSTM and GRU-based hybrid model is proposed to predict the prices of Litecoin and Monero cryptocurrencies accurately within the stipulated window sizes, i.e., 1,3, and 7. Performance evaluation of the proposed hybrid model has been done using the evaluation matrices such as MSE, RMSE, MAE, and MAPE for Litecoin and Monero by comparing it with the traditional LSTM. We cannot use these types of networks for problems like Stock Price prediction and similar problems. This is the reason You can check this article that explains more about RNN and LSTM Comparison of RNN LSTM model with Arima Models for Forecasting Problem. Sentiment Analysis using LSTM. Let us first import the required libraries and data. You can import the data directly from Kaggle. We integrated potential predictors into the long short-term memory (LSTM) to obtain predictions and compared its performance with four models that use the previous exchange rate, i.e., ANFIS, SVR, ARIMA, and LSTM, in order to verify the validity of the determinants. The remainder of this paper is organized as follows

So, the demand for Bitcoin price prediction mechanism is high. This notebook demonstrates the prediction of the bitcoin price by the neural network model. We are using 2-layers long short term. Bitcoin has recently received a lot of attention from the media and the public due to its recent price surge and crash. Correspondingly, many researchers have investigated various factors that affect the Bitcoin price and the patterns behind its fluctuations, in particular, using various machine learning methods. In this paper, we study and compare various state-of-the-art deep learning. * But the bitcoin price can be predicted to some extent by the stock-to-flow ratio*. As you can see from the previous Halving cycle, the bitcoin price overshot through the stock-to-flow ratio before coming back down and averaging along the stock-to-flow ratio. Currently, the bitcoin stock-to-flow ratio indicates that bitcoin should hit a price of.

Thus, we analyzed the time series model prediction of bitcoin prices with greater efficiency using long short-term memory (LSTM) techniques and compared the predictability of bitcoin price and sentiment analysis of bitcoin tweets to the standard method (ARIMA) After scaling the data, we now will build an LSTM (Long short-term memory) model to predict the future stock price on the training data. We analyze the predictability of the bitcoin market across prediction horizons ranging from 1 to 60 min. In doing so, we test various machine learning models and find that, while all models outperform a random classifier, recurrent neural networks and gradient boosting classifiers are especially well-suited for the examined prediction tasks. We use a comprehensive feature set, including.

LSTM to predict four companies. Finally, we will use the LSTM model to predict the behaviour of all four companies together, A, B, C and D, and contrast with the single LSTM company results. The. In order to build the LSTM, we need to import a couple of modules from Keras: Sequential for initializing the neural network. Dense for adding a densely connected neural network layer. LSTM for adding the Long Short-Term Memory layer. Dropout for adding dropout layers that prevent overfitting machine learning-based classiÞcation and regression models for predicting Bitcoin price movements and prices in short and medium terms. In previous works, machine learning-based classiÞcation has been studied for an only one-day time frame, while this work goes beyond that by using machine learning-based models for one, seven, thirty and ninety days. The developed models are feasible and. Predicting Cryptocurrency Price With Tensorflow and Keras. Kung-Hsiang, Huang (Steeve) Dec 31, 2017 · 7 min read. Cryptocurrencies, especially Bitcoin, have been one of the top hit in social.

This study investigates methods to reduce risk and increase predictability of pricing for businesses utilizing Amazon Web Services (AWS) elastic compute cloud (EC2) Spot instance pricing tier by accurately predicting spot instance pricing over a specified time-frame using long short-term memory (LSTM) neural networks and comparing the results. * Stock price prediction using LSTM, RNN and CNN-sliding window model The existing forecasting methods make use of both linear (AR, MA, ARIMA) and non-linear algorithms (ARCH, GARCH, Neural Networks), but they focus on predicting the stock index movement or price forecasting for a single company using the daily closing price*. The proposed method is a model independent approach. Here we are. Connecting LSTM cells across time and space. Let's see how LSTM's [5] are connected in time and space. Let's start from the time perspective, by considering a single sequence of N timesteps and one cell, as it is easier to understand.. As in the first image, we connect the context vector and the hidden states vector, the so-called unrolling Using a Keras Long Short-Term Memory (LSTM) Model to Predict Stock Prices. Derrick Mwiti. Follow . Nov 9, 2018 · 5 min read. Editor's note: This tutorial illustrates how to get started forecasting time series with LSTM models. Stock market data is a great choice for this because it's quite regular and widely available to everyone. Please don't take this as financial advice or use it to. LSTM was introduced by S Hochreiter, J Schmidhuber in 1997. To learn more about LSTMs, read a great colah blog post , which offers a good explanation. The code below is an implementation of a stateful LSTM for time series prediction. It has an LSTMCell unit and a linear layer to model a sequence of a time series

Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model. imhgchoi/Corr_Prediction_ARIMA_LSTM_Hybrid • 5 Aug 2018. Predicting the price correlation of two assets for future time periods is important in portfolio optimization. Portfolio Optimization. 226. Paper Code Forecasting directional movements of stock prices for intraday trading using LSTM and random forests. When James first pointed out, I started looking at how can I use validation in other models (its simpler with LSTM). For ARIMA and PROPHET, the input can only be a univariate series so we can make prediction for one day, change the training set (add that day's value) after each prediction and retrain before predicting for the next day. For. When trying to predict the Bitcoin price, traders also try to identify important support and resistance levels, which can give an indication of when a downtrend is likely to slow down and when an uptrend is likely to stall. Bitcoin Price Prediction Indicators. Moving averages are among the most popular Bitcoin price prediction tools. As the name suggests, a moving average provides the average.

In this article we forecast daily closing price series of Bitcoin, Litecoin and Ethereum cryptocurrencies, using data on prices and volumes of prior days. Cryptocurrencies price behaviour is still largely unexplored, presenting new opportunities for researchers and economists to highlight similarities and differences with standard financial prices We also select the most relevant features from Blockchain information that is deeply involved in Bitcoin's supply and demand and use them to train models to improve the predictive performance of the latest Bitcoin pricing process. We conduct the empirical study that compares the Bayesian neural network with other linear and non-linear benchmark models on modeling and predicting the Bitcoin. seems the long short-term memory (LSTM) model or its hybridization [11-15]. The other popular deep-learning architecture for stock market forecasting is the gated recurrent unit (GRU) model or its hybridization [16-19]. Both models seemingly boast about producing the better performance in every new publication. However, these studies ware based on di erent designs, assumptions. Time series analysis refers to the analysis of change in the trend of the data over a period of time. Time series analysis has a variety of applications. One such application is the prediction of the future value of an item based on its past values. Future stock price prediction is probably the best example of such an application Chapter 8 ARIMA models. ARIMA models provide another approach to time series forecasting. Exponential smoothing and ARIMA models are the two most widely used approaches to time series forecasting, and provide complementary approaches to the problem. While exponential smoothing models are based on a description of the trend and seasonality in the data, ARIMA models aim to describe the.

Stock market prediction has been identified as a very important practical problem in the economic field. However, the timely prediction of the market is generally regarded as one of the most challenging problems due to the stock market's characteristics of noise and volatility. To address these challenges, we propose a deep learning-based stock market prediction model that considers. Forecasting cryptocurrency prices time series using machine learning approach . 7 1 0 1 Using RNNs, our model won't be able to predict the prices for these months accurately due to the long range memory deficiency. To solve this issue, a special kind of RNN called Long Short-Term Memory cell (LSTM) was developed. Deep learning — For experts, by experts. We're using our decades of experience to deliver the best deep learning resources to your inbox each week. What is a Long.

I am currently working on an LSTM model to predict the closing price of a stock based on other data. It is my first time working with RNNs. I am using tensorflow. The issue arises when I try to predict prices over the X train data (which is what the model was trained on). I get different dimensions when compared to the y train data developed N-BEATS model has promising predictive power compared to LSTM and ARIMA models. Keywords: Bitcoin, machine learning, N-BEATS. time series forecasting Thesis Supervisor: Dr. Haya El-Ghalayini Title: Professor, School of Applied Computing 3. 4. Acknowledgments. The author gratefully acknowledges Dr. Haya El Ghalayini for her e orts in guiding the research process and help in putting. * and a Long Short Term Memory (LSTM) network versus the ARIMA model*. Unlike in the previous study, the model output was designed to indicate whether the price of bitcoin will go up or down the next day (not to predict the exact value). The ARIMA model gave the correct prediction only 50.05% of times. Both RNN and LSTM scored better - 50.25%. To evaluate the performance of the mentioned hybrid algorithm, the obtained results are compared with results of ARIMA as a time series model to predict the stock price. This obtained performance and its comparisons are done on five most important and international indices including S&P500, DAX, FTSE100, NASDAQ and DJI. The paper is structured as follows: the 2nd part reviews the available.

Bitcoin Price Prediction Based on Other Cryptocurrencies Using Machine Learning and Time Series Analysis Negar Malekia, Alireza Nikoubina, Masoud Rabbania, , Yasser Zeinalib a. School of Industrial Engineering, College of Engineering, University of Tehran, P.O. Box: 11155-4563, Tehran, Iran. b. School of Industrial Engineering, Sharif University of Technology, Tehran, Iran. Abstract. complexity to the model-building process, the model can capture the influence of external factors (e.g., the state of the economy) as well as management controllables (e.g., elimination period duration). The superior performance of both the ARIMA and ARIMAX models against the commonly use Bitcoin is probably the most famous cryptocurrency in the world that is recognized both inside and outside the community. Many people still feel FOMO (fear-of-missing-out) regarding the purchase at the end of 2018, when the digital currency price decreased by $3,000. Yet, the market has a highly volatile nature, and the cryptocurrency prices can change dramatically within the next few months

Top Five Bitcoin Indicators. 1. Ichimoku Clouds. An Ichimoku Cloud is made up of five lines, each displaying averages over certain periods of time which are determined by the trader. When two of the lines cross, this area between them is shaded in, forming a cloud. When the current price is above the cloud, this means that the trend is up. Still, the model may suffer with vanishing gradient problem but chances are very less. •This article was limited to architecture of LSTM cell but you can see the complete code HERE. The code also implements an example of generating simple sequence from random inputs using LSTMs. I tried the program using Deep Learning Studio

I've been using R to do load forecasting for a while and I can suggest you to use forecast package and its invaluable functions (like auto.arima). You can build an ARIMA model with the following command: model = arima(y, order, xreg = exogenous_data) with y your predictand (I suppose dayy), order the order of your model (considering seasonality. Bitcoin price prediction on Wednesday, July, 14: minimum price $39076, maximum $44958 and at the end of the day price 42017 dollars a coin. BTC to USD predictions on Thursday, July, 15: minimum price $38622, maximum $44436 and at the end of the day price 41529 dollars a coin. USD TO BTC TODAY. USD to BTC exchange rate stood at 0.2510 Bitcoins per 10000 Dollars. Today's range: 0.2490-0.2523. Time-Series Forecasting: Predicting Microsoft (MSFT) Stock Prices Using ARIMA Model. After largely successfully LSTM Model, lets try to recreate that success with an ARIMA Model. First a little about Time series and then we'll discuss the implementation of ARIMA on Microsoft stock price dataset of over 20 years. Let's do it! Time-series & forecasting models. Traditionally most machine. In order to do this, we collected a dataset containing prices and the social media activity of 181 altcoins in the form of 426,520 tweets over a timeframe of 71 days. The containing public mood was then estimated using sentiment analysis. To predict altcoin returns, we carried out linear regression analyses based on 45 days of data. We showed.

* for trading perspective it is of interest to predict the direction of a change in price or trend, rather than its numerical value, the practical application of BART model was also demonstrated in the forecasting of the direction of change in price for a 90-day period*. To this end, a model of binary classiﬁcation was used in the methodology for assessing the degree of attractiveness of. Predicting Stock Price Direction using Support Vector Machines Saahil Madge Advisor: Professor Swati Bhatt Abstract Support Vector Machine is a machine learning technique used in recent studies to forecast stock prices. This study uses daily closing prices for 34 technology stocks to calculate price volatility and momentum for individual stocks and for the overall sector. These are used as.

Implementing Neural Networks and Autoencoders to Detect Anomalies on Bitcoin historical Price. Now let's use an unsupervised learning technique with some help from Neural Networks (NN). This way is known to be much more flexible and accurate for anomaly detection. Nowadays, NNs are the new norm because of their amazing power and great results. We'll use them in an unusual way: we'll. The problem of predicting daily Covid-19 cases is a hard one. We're amidst an outbreak, and there's more to be done. Hopefully, everything will be back to normal after some time. References. Sequence Models PyTorch Tutorial; LSTM for time series prediction; Time Series Prediction using LSTM with PyTorch in Python; Stateful LSTM in Kera Short-term fluctuations in stock prices are notoriously difficult to predict (1, 2).For decades, economists have created complicated mathematical models that ultimately fail to describe short-term share price movements ().Indeed, the range of factors that influence share prices is so broad that many eminent scholars describe short-term price changes as random (3-5)

The ARIMA model aims to explain data by using time series data on its past values and uses linear regression Multiple Linear Regression Multiple linear regression refers to a statistical technique used to predict the outcome of a dependent variable based on the value of independent variables to make predictions. Understanding the ARIMA Model. The following descriptive acronym explains the. Predicting Stock Prices Using Technical Analysis and Machine Learning Jan Ivar Larsen. Problem Description In this thesis, a stock price prediction model will be created using concepts and techniques in technical analysis and machine learning. The resulting prediction model should be employed as an artificial trader that can be used to select stocks to trade on any given stock exchange. The. The latest cryptocurrency hedge fund report by PricewaterhouseCoopers found respondents predicting a $100,000 bitcoin price by end of year. PricewaterhouseCoopers (PwC), one of the big four accounting firms, has released its 3rd Annual Global Crypto Hedge Fund Report 2021 in conjunction with the Alternative Investment Management Association (AIMA) and Elwood Asset Management Predicting different stock prices using Long Short-Term Memory Recurrent Neural Network in Python using TensorFlow 2 and Keras

Based on LSTM and an attention mechanism, a wavelet transform is used to denoise historical stock data, extract and train its features, and establish the prediction model of a stock price. We compared the results with the other three models, including the LSTM model, the LSTM model with wavelet denoising and the gated recurrent unit (GRU. In addition, four models, i.e., ARIMA, SVR, ANFIS, and LSTM, were employed to make predictions based on the previous exchange rate. Moreover, the. In this project, we attempt to apply machine-learning algorithms to predict Bitcoin price. For the ﬁrst phase of our investigation, we aimed to understand and better identify daily trends in the Bitcoin market while gaining insight into optimal. However, compared with the TCN model, the overall performance of the LSTM model for the Niño \(3.4\) index prediction is worse, which can also be verified from the RMSE and PCC values of the.