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Long-term bullish on BTC, now is the time to buy

Based on the six types of Bitcoin price prediction models mentioned below, we can combine the predictions of these models to speculate the Bitcoin price at the end of 2025:

Time series analysis models (such as ARIMA, GARCH) may predict a reasonable price between the historical high (such as the 2021 high) and the recent trend based on Bitcoin's historical price volatility and trends. Assuming that the price continues to rise, but taking into account cyclical adjustments, a possible estimate range is $125,000 to $150,000.
Machine learning models (such as LSTM and neural networks) make predictions based on a variety of features such as current market data, trading volume, social media sentiment, etc. Based on the complexity and learning ability of these models, they may predict that the Bitcoin price will break through the historical high, and the estimated value may reach $150,000 to $200,000.
Models based on economic principles take into account changes in supply and demand, such as the impact of the halving event in 2024 on 2025, as well as the continued entry of institutional investors and possible macroeconomic changes. These factors may push the price closer to or above $200,000.
On-chain analysis models look at Bitcoin's holding behavior and liquidity. If long-term holders start selling, it may cause a price correction; but if demand continues to increase, the price may continue to rise. The prediction of this model may be between $150,000 and $180,000.
Comprehensive models and artificial intelligence forecasts may combine all of the above factors to provide a more comprehensive forecast. These models are able to take into account more macro and micro data, and the forecast may be between $180,000 and $250,000.

Combining the forecasts of these models, considering the different focuses of each model and the unpredictability of the market, a reasonable estimate is that the price of Bitcoin may be between $150,000 and $200,000 at the end of 2025. This range attempts to balance the different outputs of various forecasting models, while also taking into account market volatility and uncertainty.

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