DOGS (DOGS) Price Prediction Up To $0.03901 | DOGS Forecast

What will DOGS (DOGS) be valued at in 2024, 2025, 2025, and up to 2053? In this DOGS price prediction, we will look more closely at DOGS'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 DOGS 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.

DOGS (DOGS) has steadily grown over the past year, peaking at an all-time high of 0.003063. Following a market-wide correction, DOGS settled at 0.0001388, serving as a stabilizing influence during this cycle.
According to our current DOGS price prediction, the value of DOGS is expected to INCREASE by 6099%, reaching 0.008602 by mid Apr 2027. Since Mar 2025, DOGS 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 May 2025, the value of DOGS is projected to INCREASE by 8% and reach approximately 0.0001501.
Additionally, the update from the development team of DOGS 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, DOGS growth gives a return on investment of 8% in the short run, while for the long term, projections in Apr 2027 exceed 6099%.

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).