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Collateral Network (COLT) has swiftly captured all the market attention due to its unique business plan, and mammoth presale growth. According to experts, early Collateral Network investors may get 100x profit by the end of this year. Therefore, bulls have rallied behind Collateral Network, while Dogecoin (DOGE) and Shiba Inu (SHIB) are still in troubled waters.Musk’s Twitter Resignation Hurts Dogecoin
As Elon Musk has stepped out from being the Twitter CEO, experts have been pondering upon its impact on Dogecoin. A few weeks back, Twitter revealed that it would soon allow crypto trading on its platform, and Dogecoin was expected to be its biggest beneficiary, given the billionaire’s support for the meme coin.
However, a change in the Twitter CEO may hurt Dogecoin, which has heavily relied on Musk’s support for growth. The bearish sentiments can also be corroborated by Dogecoin’s price trajectory. The price of Dogecoin (DOGE) has suffered a drop of 9% in the past month. Thus, Dogecoin is available at $0.0717. The immense rise of other meme coins, like Pepe Coin, has also contributed to the market struggle of Dogecoin.Shiba Inu Sees Improvement In Key Categories
In positive news for the Shiba Inu community, the AAA rating of SHIB has been restored by CertiK. The development took place after Shiba Inu fixed several security issues. However, the news has not been able to support the price of Shiba Inu (SHIB). Shiba Inu’s price has plunged by 15% in the past month.
Hence, Shiba Inu is now changing hands at $0.00000856. Meanwhile, Shiba Inu has confirmed preorders for 5000 SHIB-themed cold wallets, whose delivery will start in July 2023. On the sidelines, BitFlyer, a Japanese crypto exchange, recently stated that a few cryptocurrencies, including Shiba Inu, will have to comply with the travel rule to keep operating on its platform.Collateral Network Surges In Popularity
A first-of-a-kind DeFi platform, Collateral Network, has disrupted the lending industry. It is a blockchain crowdlending platform that allows people to take loans against their physical assets at a competitive interest rate. Collateral Network accepts a range of physical assets as collateral to give loans, such as vintage cars, watches, fine wine, art, and many more.
Collateral Network is a borderless and permissionless platform, and people can take a loan by sending their physical assets directly to the company. Collateral Network accepts borrowers’ assets as collateral, and mints non-fungible tokens against them. Prior to minting NFTs, Collateral Network evaluates the assets using artificial intelligence.
Borrowers can get their collateralized assets returned to their addresses after settling the loan. But if borrowers default on their loans, their assets are auctioned to recover the loan amount. Lenders can buy these 100% asset-backed Collateral Network NFTs to lend funds to borrowers and in return, receive a fixed income every week.
Collateral Network (COLT) tokens have been developed on the Ethereum blockchain, with their smart contracts fully audited. The company has a KYC-audited team comprising experienced members. A Collateral Network token can be bought at $0.014, which is predicted to soar by 3500% during the presale round.Find out more about the Collateral Network presale here:
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In the ever-evolving world of cryptocurrencies, the past month has seen some significant movements. Shiba Inu (SHIB) and Cardano (ADA), two popular tokens, have experienced contrasting fortunes, while the upcoming Collateral Network is generating buzz with its imminent COLT presale.Shiba Inu: A Dogged Pursuit of Value
Despite multiple promising developments, memecoin Shiba Inu is experiencing a downtrend lately. Not even Shiba can withstand the crypto market’s downturn.
Shiba Inu, a meme coin that has captured the hearts of many, has been struggling to maintain its momentum. The token, known for its Shiba Inu dog logo, has been trading below the $0.000007 mark, with buyers seemingly hesitant to seize the initiative.
Shiba Inu is a decentralized meme token that grew into a vibrant ecosystem. ShibaSwap, fun tokens, Artist Incubator, and growing 500k+ community are some of the exciting features of this project.
However, the recent developments have been less than favorable. The token is currently trading at $0.00000672, and if the situation does not change by the end of the day, the drop is likely to continue in the coming days.
This downturn in Shiba Inu’s value is a stark contrast to the token’s previous performance, which saw it gain popularity and value rapidly. However, the crypto market is notoriously volatile lately, which likely explains Shiba Inu’s current struggles.Cardano: A Week to Forget
The Cardano token also experienced negative developments as of late. Its delistment from popular trading platforms may convince investors to look for another platform.
Cardano, another popular token, has had a week to forget. The token, Cardano, which has been hailed as a potential “Ethereum killer” due to its robust technology and strong development team, has seen its price drop by 30% over the past week.
The drop is due to Cardano’s delisting from popular trading platforms like Robinhood, which contributed to its worst week since May 2023.
Despite the recent downturn, Cardano has a strong foundation and a dedicated community. The question now is whether Cardano can rebound from this setback and regain its previous momentum.Collateral Network: A New Opportunity
Collateral Network aims to disrupt the lending industry by offering decentralized lending protocols for real-world assets on the Ethereum blockchain. The Collateral Network platform accepts a wide range of assets as collateral, including real estate, fine art, vintage cars, gold, fine wines, watches, diamonds, and collectibles.
For lenders, Collateral Network offers fixed passive income, tangible NFTs backed by real physical assets, and security in case of borrower default. For borrowers, the platform offers fast turnaround, privacy, transparency, low cost, and borderless borrowing.
Also, token holders that are borrowers can use the token to get discounts on borrowing fees and interest. The more tokens you hold the lower the interest rate. On the other hand, COLT token holders that are lenders can use the token to get discounts on trading fees in the marketplace. The more tokens you hold the higher the discounts on fees.
The token’s initial starting price is $0.01 and has already risen to $0.0168. Analysts predict a 3,500% (35x) price increase by the time COLT is released on major exchanges.Find out more about the Collateral Network presale here:
The crypto market has been bearish this weekend, and it looks like the week might still be bearish. Most of the coins have declined over the last few days, but today the market has been quite stable. Though most coins have not made a significant recovery, the decline has also been minimal. In the past 24 hours, the entire global marketcap had declined by 0.74%, which is quite small. th of this month? Well, here are price predictions for theBitgert (BRISE) Price Prediction
The st announcement are key factors that will grow th and 29th August.Safemoon (SFM) Price Prediction
The Safemoon has not been doing over the 7 days. The Safemoon coin might be experiencing some rebound today, but the market is still bearish. Therefore, might see the Safemoon coin drop if the bears will remain in control. The Safemoon coin was trading at $0.0003919 and might push to $0.0004501 if the buying pressure grows on 28th and 29th August.Baby Doge Price Prediction
The Baby Doge coin is still in the red for the past 24 hours. The Baby Doge selling pressure is easing as the crypto market grows stable. So Baby Doge might make some recovery, but the market is still bearish.
The crypto market has been bearish this weekend, and it looks like the week might still be bearish. Most of the coins have declined over the last few days, but today the market has been quite stable. Though most coins have not made a significant recovery, the decline has also been minimal. In the past 24 hours, the entire global marketcap had declined by 0.74%, which is quite small. Bitgert (BRISE) , Baby Doge, and Safemoon have been affected differently by the current gear market. The Bitgert coin, has been experiencing a surge in buying pressure, while Baby Doge and Safemoon coins have been stable. How will Bitgert (BRISE) , Baby Doge, and Safemoon prices perform on the 28th and 29of this month? Well, here are price predictions for the Bitgert , Safemoon, and Baby Doge chúng tôi Bitgert coin is the only among the three that has been surging this month. Even today, Bitgert is bullish, growing 5% in 24 hours, while Safemoon and Baby Doge stayed stable. With a bullish Bitgert coin, the price prediction is a 50% increase between today and tomorrow. The Bitgert price as of writing was $0.000000801742, and with the growing Bitgert buying pressure, Bitgert price might end the day at 0.000001203. However, crypto experts believe that Bitgert has the potential to break last week’s ATH of $0.00000146437. Bitgert has got so many factors driving its bullish price, but the Bitgert roadmap V2 is one of them. Bitgert also partnered with the Centcex team to build products and projects for the Bitgert ecosystem. The Centcex partnership, roadmap V2 products, and the widely anticipated September 1announcement are key factors that will grow Bitgert price passed 0.000001203 between 28and chúng tôi Safemoon has not been doing over the 7 days. The Safemoon coin might be experiencing some rebound today, but the market is still bearish. Therefore, might see the Safemoon coin drop if the bears will remain in control. The Safemoon coin was trading at $0.0003919 and might push to $0.0004501 if the buying pressure grows on 28and chúng tôi Baby Doge coin is still in the red for the past 24 hours. The Baby Doge selling pressure is easing as the crypto market grows stable. So Baby Doge might make some recovery, but the market is still bearish. If the Baby Doge buying pressure rises, the price might grow from the current $0.000000001353 to $0.000000002602 in the next 24 hours. So Baby Doge might be the coin to watch on 28and 29August.
SFM should be around $0.00024.
SafeMoon price prediction 22 Jul 2023: SafeMoon’s price for 22 Jul 2023 according to our analysis should range between $0.00022 to $0.00026 and the average price of SFM should be around $0.00024.
SafeMoon price prediction 23 Jul 2023: SafeMoon’s price for 23 Jul 2023 according to our analysis should range between $0.00022 to $0.00026 and the average price of SFM should be around $0.00024.
SafeMoon price prediction 24 Jul 2023: SafeMoon’s price for 24 Jul 2023 according to our analysis should range between $0.00022 to $0.00025 and the average price of SFM should be around $0.00023.
SafeMoon price prediction 29 Jul 2023: SafeMoon’s price for 29 Jul 2023 according to our analysis should range between $0.00021 to $0.00025 and the average price of SFM should be around $0.00023.
SafeMoon price prediction 3 Aug 2023: SafeMoon’s price for 3 Aug 2023 according to our analysis should range between $0.00021 to $0.00024 and the average price of SFM should be around $0.00023.
SafeMoon price prediction 13 Aug 2023: SafeMoon’s price for 13 Aug 2023 according to our analysis should range between $0.00021 to $0.00024 and the average price of SFM should be around $0.00023.
SafeMoon price prediction September 2023: SafeMoon’s price for September 2023 according to our analysis should range between $0.00022 to $0.00025 and the average price of SFM should be around $0.00024.
SafeMoon price prediction October 2023: SafeMoon’s price for October 2023 according to our analysis should range between $0.00023 to $0.00026 and the average price of SFM should be around $0.00024.
SafeMoon price prediction November 2023: SafeMoon’s price for November 2023 according to our analysis should range between $0.00023 to $0.00027 and the average price of SFM should be around $0.00025.
SafeMoon price prediction December 2023: SafeMoon’s price for December 2023 according to our analysis should range between $0.00024 to $0.00028 and the average price of SFM should be around $0.00026.
SafeMoon price prediction 2024: SafeMoon’s price for 2024 according to our analysis should range between $0.00032 to $0.00048 and the average price of SFM should be around $0.0004.
SafeMoon price prediction 2025: SafeMoon’s price for 2025 according to our analysis should range between $0.00042 to $0.00063 and the average price of SFM should be around $0.00053.
SafeMoon price prediction 2026: SafeMoon’s price for 2026 according to our analysis should range between $0.00056 to $0.00084 and the average price of SFM should be around $0.0007.
SafeMoon price prediction 2027: SafeMoon’s price for 2027 according to our analysis should range between $0.00074 to $0.0011 and the average price of SFM should be around $0.00092.
SafeMoon price prediction 2028: SafeMoon’s price for 2028 according to our analysis should range between $0.00097 to $0.0014 and the average price of SFM should be around $0.0012.
SafeMoon price prediction 2029: SafeMoon’s price for 2029 according to our analysis should range between $0.0012 to $0.0019 and the average price of SFM should be around $0.0016.
SafeMoon price prediction 2030: SafeMoon’s price for 2030 according to our analysis should range between $0.0017 to $0.0025 and the average price of SFM should be around $0.0021.
SafeMoon price prediction 2031: SafeMoon’s price for 2031 according to our analysis should range between $0.0022 to $0.0033 and the average price of SFM should be around $0.0028.
SafeMoon price prediction 2032: SafeMoon’s price for 2032 according to our analysis should range between $0.0029 to $0.0044 and the average price of SFM should be around $0.0037.
SafeMoon price prediction 2033: SafeMoon’s price for 2033 according to our analysis should range between $0.0039 to $0.0058 and the average price of SFM should be around $0.0049.
SafeMoon price prediction 2034: SafeMoon’s price for 2034 according to our analysis should range between $0.0051 to $0.0077 and the average price of
What’s happening with top meme coins?
Big Eyes Coin launches a 250% bonus during presale (Secret code below)
Huge whale movement in Dogecoin (DOGE)
Shiba Inu’s Shibarium blockchain may be a game changer
Meme coins surfaced in early 2023 and had an amazing two-year price streak with the help of Elon Musk and Mark Cuban. However, they witnessed a decline in interest and engagement towards the end of 2023, however, the meme coins are bouncing back and they are coming in heavily riding the hype wave.
Big Eyes Coin is the top contender in the meme space. The Ethereum-based coin made a splash with its exciting presale, raising over $32 million. Early BIG adapters are set to receive a 250% bonus with the code BULLRUN250 for a limited period. Meanwhile, meme coin giants like Dogecoin and Shiba Inu are slowly rising and filling the headlines with subtle hints of an impending bull run.Dogecoin – Top Dog is Rising
With a recent increase from $0.069 to $0.0721 and increased investor sentiment towards higher-risk assets, Dogecoin (DOGE) has shown signs of a healthy recovery. Although Dogecoin’s online presence and well-known connection to Elon Musk have boosted its popularity, it is crucial to recognize the strength of the Dogecoin community.
Some investors believe that this community-driven cryptocurrency will succeed in the coming bull run because they recognize its actual potential. Despite having an endless supply, investors are still hoarding the coin, maintaining the token’s value. Investors are becoming more attracted to the allure of a completely decentralized digital monetary system as existing banking systems approach collapse. Dogecoin (DOGE) is still widely used today.Shibarium to Reinvent SHIB
Investors in SHIB have been buzzing since Shibarium, Shiba Inu’s blockchain, was just announced. Shibarium is positioned to offer the community a quicker and more affordable platform for trading SHIB and other tokens, which is an encouraging development.
Yet, it is crucial to recognize that building a blockchain from scratch is a difficult and drawn-out process. Shibarium’s commercialization and success are thus not assured. Shibarium, however, is anticipated to raise the value of SHIB and introduce a new use case for it, which can increase demand and ultimately cause a price hike.
For the Shiba Inu community and the larger crypto market, the beta release of Shibarium is a big development. With quicker and more affordable transactions and staking rewards, Shibarium may rise to new heights.Big Eyes Coin – Another DOGE in the making?
Big Eyes is set to become BIG, with a step into the future De-Fi and a strong commitment to the oceans, this meme token may be a blessing for investors who like to invest in a cause.
Big Eyes coin is proving to be more than an average meme coin, although supplemented with cute looks and really cute cat posters. This coin is set to make an actual splash on the meme ecosystem.
In terms of tokenomics, the total supply will be capped at 200 billion BIG tokens, with 70% sold in an online presale. Another 20% will go to crypto exchanges, while the remaining 10% will be evenly split between the crypto’s charities and its marketing budget.
There is a presale madness taking place in the crypto world, and investors should consider coins like Big Eyes that have already garnered a huge following from around the globe. Make use of their exciting bonuses and loot boxes now and watch them explode during the coin launch.Find out more about Big Eyes Coin (BIG):
This article was published as a part of the Data Science Blogathon.Introduction
is used to predict future values based on previously observed values and one of the best tools for trend analysis and future prediction.What is time-series data?
It is recorded at regular time intervals, and the order of these data points is important. Therefore, any predictive model based on time series data will have time as an independent variable. The output of a model would be the predicted value or classification at a specific time.Time series analysis vs time series forecasting
Let’s talk about some possible confusion about the Time Series Analysis and Forecasting. Time series forecasting is an example of predictive modeling whereas time series analysis is a form of descriptive modeling.
For a new investor general research which is associated with the stock or share market is not enough to make the decision. The common trend towards the stock market among the society is highly risky for investment so most of the people are not able to make decisions based on common trends. The seasonal variance and steady flow of any index will help both existing and new investors to understand and make a decision to invest in the share market.
To solve this kind of problem time series forecasting is the best technique.Stock market
Stock markets are where individual and institutional investors come together to buy and sell shares in a public venue. Nowadays these exchanges exist as electronic marketplaces.
That supply and demand help determine the price for each security or the levels at which stock market participants — investors and traders — are willing to buy or sell.
The concept behind how the stock market works is pretty simple. Operating much like an auction house, the stock market enables buyers and sellers to negotiate prices and make trades.Definition of ‘Stock’
A Stock or share (also known as a company’s “equity”) is a financial instrument that represents ownership in a companyMachine learning in the stock market
The stock market is very unpredictable, any geopolitical change can impact the share trend of stocks in the share market, recently we have seen how covid-19 has impacted the stock prices, which is why on financial data doing a reliable trend analysis is very difficult. The most efficient way to solve this kind of issue is with the help of Machine learning and Deep learning.
In this tutorial, we will be solving this problem with ARIMA Model.
To know about seasonality please refer to my previous blog, And to get a basic understanding of ARIMA I would recommend you to go through this blog, this will help you to get a better understanding of how Time Series analysis works.Implementing stock price forecasting
I will be using nsepy library to extract the historical data for SBIN.Imports and Reading Data
The data shows the stock price of SBIN from 2023-1-1 to 2023-11-1. The goal is to create a model that will forecast the closing price of the stock.
Let us create a visualization which will show per day closing price of the stock-plt.figure(figsize=(10,6)) plt.grid(True) plt.xlabel('Dates') plt.ylabel('Close Prices') plt.plot(sbin['Close']) plt.title('SBIN closing price') plt.show() plt.figure(figsize=(10,6)) df_close = sbin['Close'] df_close.plot(style='k.') plt.title('Scatter plot of closing price') plt.show() plt.figure(figsize=(10,6)) df_close = sbin['Close'] df_close.plot(style='k.',kind='hist') plt.title('Hisogram of closing price') plt.show()
First, we need to check if a series is stationary or not because time series analysis only works with stationary data.
Testing For Stationarity:
To identify the nature of the data, we will be using the null hypothesis.
H0: The null hypothesis: It is a statement about the population that either is believed to be true or is used to put forth an argument unless it can be shown to be incorrect beyond a reasonable doubt.
H1: The alternative hypothesis: It is a claim about the population that is contradictory to H0 and what we conclude when we reject H0.
#Ho: It is non-stationary
#H1: It is stationary
If we fail to reject the null hypothesis, we can say that the series is non-stationary. This means that the series can be linear.
If both mean and standard deviation are flat lines(constant mean and constant variance), the series becomes stationary.
from statsmodels.tsa.stattools import adfullerdef test_stationarity(timeseries): #Determing rolling statistics rolmean = timeseries.rolling(12).mean() rolstd = timeseries.rolling(12).std() #Plot rolling statistics: plt.plot(timeseries, color='yellow',label='Original') plt.plot(rolmean, color='red', label='Rolling Mean') plt.plot(rolstd, color='black', label = 'Rolling Std') plt.legend(loc='best') plt.title('Rolling Mean and Standard Deviation') plt.show(block=False) print("Results of dickey fuller test") adft = adfuller(timeseries,autolag='AIC') # output for dft will give us without defining what the values are. #hence we manually write what values does it explains using a for loop output = pd.Series(adft[0:4],index=['Test Statistics','p-value','No. of lags used','Number of observations used']) for key,values in adft.items(): output['critical value (%s)'%key] = values print(output) test_stationarity(sbin['Close']) After analysing the above graph, we can see the increasing mean and standard deviation and hence our series is not stationary. Results of dickey fuller test Test Statistics -1.914523 p-value 0.325260 No. of lags used 3.000000 Number of observations used 5183.000000 critical value (1%) -3.431612 critical value (5%) -2.862098 critical value (10%) -2.567067 dtype: float64
We see that the p-value is greater than 0.05 so we cannot reject the Null hypothesis. Also, the test statistics is greater than the critical values. so the data is non-stationary.
For time series analysis we separate Trend and Seasonality from the time series.result = seasonal_decompose(df_close, model='multiplicative', freq = 30) fig = plt.figure() fig = result.plot() fig.set_size_inches(16, 9) from pylab import rcParams rcParams['figure.figsize'] = 10, 6 df_log = np.log(sbin['Close']) moving_avg = df_log.rolling(12).mean() std_dev = df_log.rolling(12).std() plt.legend(loc='best') plt.title('Moving Average') plt.plot(std_dev, color ="black", label = "Standard Deviation") plt.plot(moving_avg, color="red", label = "Mean") plt.legend() plt.show()
Now we are going to create an ARIMA model and will train it with the closing price of the stock on the train data. So let us split the data into training and test set and visualize it.train_data, test_data = df_log[3:int(len(df_log)*0.9)], df_log[int(len(df_log)*0.9):] plt.figure(figsize=(10,6)) plt.grid(True) plt.xlabel('Dates') plt.ylabel('Closing Prices') plt.plot(df_log, 'green', label='Train data') plt.plot(test_data, 'blue', label='Test data') plt.legend() model_autoARIMA = auto_arima(train_data, start_p=0, start_q=0, test='adf', # use adftest to find optimal 'd' max_p=3, max_q=3, # maximum p and q m=1, # frequency of series d=None, # let model determine 'd' seasonal=False, # No Seasonality start_P=0, D=0, trace=True, error_action='ignore', suppress_warnings=True, stepwise=True) print(model_autoARIMA.summary()) Performing stepwise search to minimize aic ARIMA(0,1,0)(0,0,0) intercept : AIC=-16607.561, Time=2.19 sec ARIMA(1,1,0)(0,0,0) intercept : AIC=-16607.961, Time=0.95 sec ARIMA(0,1,1)(0,0,0) intercept : AIC=-16608.035, Time=2.27 sec ARIMA(0,1,0)(0,0,0) : AIC=-16609.560, Time=0.39 sec ARIMA(1,1,1)(0,0,0) intercept : AIC=-16606.477, Time=2.77 sec Best model: ARIMA(0,1,0)(0,0,0) Total fit time: 9.079 seconds SARIMAX Results ============================================================================== Dep. Variable: y No. Observations: 4665 Model: SARIMAX(0, 1, 0) Log Likelihood 8305.780 Date: Tue, 24 Nov 2023 AIC -16609.560 Time: 20:08:50 BIC -16603.113 Sample: 0 HQIC -16607.293 - 4665 Covariance Type: opg ============================================================================== ------------------------------------------------------------------------------ sigma2 0.0017 1.06e-06 1566.660 0.000 0.002 0.002 =================================================================================== Ljung-Box (Q): 24.41 Jarque-Bera (JB): 859838819.58 Prob(Q): 0.98 Prob(JB): 0.00 Heteroskedasticity (H): 7.16 Skew: -37.54 Prob(H) (two-sided): 0.00 Kurtosis: 2105.12 =================================================================================== model_autoARIMA.plot_diagnostics(figsize=(15,8)) plt.show() model = ARIMA(train_data, order=(3, 1, 2)) fitted = model.fit(disp=-1) print(fitted.summary()) ARIMA Model Results ============================================================================== Dep. Variable: D.Close No. Observations: 4664 Model: ARIMA(3, 1, 2) Log Likelihood 8309.178 Method: css-mle S.D. of innovations 0.041 Date: Tue, 24 Nov 2023 AIC -16604.355 Time: 20:09:37 BIC -16559.222 Sample: 1 HQIC -16588.481 ================================================================================= --------------------------------------------------------------------------------- const 8.761e-06 0.001 0.015 0.988 -0.001 0.001 ar.L1.D.Close 1.3689 0.251 5.460 0.000 0.877 1.860 ar.L2.D.Close -0.7118 0.277 -2.567 0.010 -1.255 -0.168 ar.L3.D.Close 0.0094 0.021 0.445 0.657 -0.032 0.051 ma.L1.D.Close -1.3468 0.250 -5.382 0.000 -1.837 -0.856 ma.L2.D.Close 0.6738 0.282 2.391 0.017 0.122 1.226 Roots ============================================================================= Real Imaginary Modulus Frequency ----------------------------------------------------------------------------- AR.1 0.9772 -0.6979j 1.2008 -0.0987 AR.2 0.9772 +0.6979j 1.2008 0.0987 AR.3 74.0622 -0.0000j 74.0622 -0.0000 MA.1 0.9994 -0.6966j 1.2183 -0.0969 MA.2 0.9994 +0.6966j 1.2183 0.0969 ----------------------------------------------------------------------------- # Forecast fc, se, conf = fitted.forecast(519, alpha=0.05) # 95% confidence fc_series = pd.Series(fc, index=test_data.index) lower_series = pd.Series(conf[:, 0], index=test_data.index) upper_series = pd.Series(conf[:, 1], index=test_data.index) plt.figure(figsize=(12,5), dpi=100) plt.plot(train_data, label='training') plt.plot(test_data, color = 'blue', label='Actual Stock Price') plt.plot(fc_series, color = 'orange',label='Predicted Stock Price') plt.fill_between(lower_series.index, lower_series, upper_series, color='k', alpha=.10) plt.title('SBIN Stock Price Prediction') plt.xlabel('Time') plt.ylabel('Actual Stock Price') plt.legend(loc='upper left', fontsize=8) plt.show()
Time Series forecasting is really useful when we have to take future decisions or we have to do analysis, we can quickly do that using ARIMA, there are lots of other Models from we can do the time series forecasting but ARIMA is really easy to understand.
I hope this article will help you and save a good amount of time. Let me know if you have any suggestions.
Prabhat Pathak (Linkedin profile) is a Senior Analyst and innovation Enthusiast.
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