SubQuery (SQT) Price Prediction Up To $0.3560 | SQT Forecast

What will SubQuery (SQT) be valued at in 2024, 2025, 2025, and up to 2053? In this SubQuery price prediction, we will look more closely at SubQuery's current market position concerning trading volume, market capitalization, and past price movements to support investors set price targets and assess confidence levels. We will also break down expert analysis and technical indicators that give a better view of where SQT might head in the coming months & years.

We utilize one of the most common statistical approaches to time-series forecasting known as the ARIMA model for the price prediction of cryptocurrencies.
ARIMA-ARMA plus differencing to create a stationary time series. A stationary series is one whose statistical properties are constant over time. Differencing cancels out any trend or pattern this method might have, enabling the model to interpret data structure and provide a good forecast.

SubQuery (SQT) has steadily grown over the past year, peaking at an all-time high of 0.05564912871686154. Following a market-wide correction, SubQuery settled at 0.001440, serving as a stabilizing influence during this cycle.
According to our current SubQuery price prediction, the value of SubQuery is expected to INCREASE by 5669%, reaching 0.08306 by mid May 2027. Since Apr 2025, SubQuery has INCREASED by (((cprice) - price_n-30)/ price_n-30) *100.
Based on this trend, we anticipate a possible stabilization in the coming month. By the end of Jun 2025, the value of SubQuery is projected to INCREASE by -10% and reach approximately 0.001289.
Additionally, the update from the development team of SubQuery in the last months shows an active project and development. Online discussions, especially on social platforms like Twitter and Reddit, have been replete with great excitement in the past month for features that are to come.
Overall, SubQuery growth gives a return on investment of -10% in the short run, while for the long term, projections in May 2027 exceed 5669%.

The ARIMA model is defined by three parameters: p, d, and q.

• p: the number of past observations that the model will consider. This is the AR component which is expressed as below: $$X_t={\varphi }_1{X}_{t-1}+{\varphi }_2{X}_{t-2}+\cdots +{\varphi }_p{X}_{t-p}+Z_t$$
  $$X_t=\sum _{i=1}^p{\varphi }_i{X}_{t-i}+Z_t$$
• d: the degree of differencing, which is the number of times needed to subtract the previous day’s price from today’s price to make the data consistent at all points in time. (The I component)
• q: the number of past errors in prediction. This is the MA component and is expressed as: $$X_t=Z_t+{\theta }_1{Z}_{t-1}+{\theta }_2{Z}_{t-2}+\cdots +{\theta }_q{Z}_{t-q}$$
  $$X_t=Z_t+{\theta }_1{Z}_{t-1}+{\theta }_2{Z}_{t-2}+\cdots +{\theta }_q{Z}_{t-q}$$

The ARIMA(p, d, q) model is the combination of the AR(p) and MA(q) and differencing: $$\mathrm{\Delta }d{X}_t=\sum _{i=1}^p{\varphi }_i\mathrm{\Delta }d{X}_{t-i}+Z_t+\sum _{i=1}^q{\theta }_i{Z}_{t-i}$$ Where:

• 𝑋𝑡: the time series X at a time t, and 𝑋𝑡−𝑖 is X at a previous point in time.
• 𝑍𝑡: the error terms or white noise, and 𝑍𝑡−𝑖 is Z at a previous point in time.
• 𝜙: the parameters of the autoregressive part of the model.
• 𝜃: the parameters of the moving average part of the model.
• ∇ 𝑑𝑋𝑡: the differenced time series (d times).