What Is an Application-Specific Integrated Circuit (ASIC) Miner?

An application-specific integrated circuit (ASIC) is an integrated circuit chip designed for a specific purpose. An ASIC miner is a computerized device that uses ASICs for the sole purpose of “mining” digital currency. Generally, each ASIC miner is constructed to mine a specific digital currency. So, a Bitcoin ASIC miner can mine only bitcoin. One way to think about bitcoin ASICs is as specialized bitcoin mining computers optimized to solve the mining algorithm.

Developing and manufacturing ASICs as mining devices is costly and complex. However, because ASICs are built especially for mining cryptocurrency, they do the job faster than less powerful computers. As a result, ASIC chips for cryptocurrency mining have become increasingly efficient, with the latest generation hashing at 158 terahashes per second but only using 34.5 joules per terahash.

Understanding Application-Specific Integrated Circuit (ASIC) Miners

Instead of being general-purpose integrated circuits—like RAM chips or PC or mobile device microprocessors—ASICs employed in cryptocurrency mining are specific integrated circuits designed solely to mine cryptocurrencies.

Initially, Bitcoin’s creator(s) intended for bitcoin to be mined on central processing units (CPUs) of commonly used laptops or desktop computers. However, Bitcoin ASICs surpassed both CPUs and graphics processing units (GPUs) because of their reduced electricity consumption and greater computing capacity. After gaining traction in mid-2013, when other hardware mining devices started hitting bottlenecks in their mining, Bitcoin ASIC miners increased and retained their lead.

A hash is a long hexadecimal number used to identify blocks in a blockchain, called the block header hash or block hash. To mine a block, miners begin adding values to a hash to generate new ones until a number less than the target difficulty (original hash) is reached. This is called hashing. The more hashes that can be performed in a set period, the more likely a miner is to earn bitcoin. ASIC miners are optimized to compute hash functions efficiently and quickly.

Although mining cryptocurrencies can be an expensive proposition of declining profitability, many people are drawn to it. Despite the uncertain return on investment, would-be cryptocurrency miners are willing to incur high upfront expenses for pricey ASICs and pay significant ongoing costs for electricity in return for the prospect of earning cryptocurrency.

Development of the ASIC Miner

Cryptocurrency mining is required by a proof of work (PoW) blockchain like Bitcoin to carry out its operations. The mining process involves solving a block’s hash by randomly generating numbers until reaching a number below the target difficulty number. The first miner to find the solution to the puzzle closes the block. Each winner in the bitcoin mining competition receives a reward (a specific amount of bitcoin) along with the transaction fees for the transactions in that block.

In Bitcoin’s early days, any computer with adequate processing power could mine bitcoin. However, those days are long gone; bitcoin’s soaring popularity and growing acceptance have attracted hordes of crypto miners.

At the same time, cryptocurrency mining has become exponentially more difficult because the mining difficulty changes as miners enter and exit the network. Over time, the number of miners has constantly grown, which increased the difficulty. These developments have resulted in a race to harness the most “hashing power,” the term used to describe how many hashes per second a miner can generate (or the combined hashes per second of a networked mining rig or pool). ASIC miners came about as a result of this quest for more hashing power; modern Bitcoin ASICs can hash at more than 150 terahashes per second (nine zeros, or 150 x 10¹² hashes per second).

ASIC devices were popularized by Bitmain (headquartered in China), which dominates ASIC Bitcoin mining activities through its Antminer ASIC product range.

ASIC Miner Advantages

Though GPU and CPU mining rigs rely on components that have more than one function, ASIC miners are designed for the sole purpose of mining cryptocurrency. This singular focus makes an ASIC miner much more powerful and energy-efficient than a comparable GPU miner.

Because each cryptocurrency has its own cryptographic hash algorithm, an ASIC miner is designed to mine using that specific algorithm. For example, Bitcoin ASIC miners are designed to hash the SHA-256 algorithm, while Litecoin (LTC) uses scrypt (pronounced es-crypt). Though this means that an ASIC miner could technically mine any other cryptocurrency based on the same algorithm, most miners who invest in ASIC hardware designed to mine bitcoin or Litecoin stick to mining that specific cryptocurrency.

ASIC Miner Considerations

Before investing thousands of dollars in an ASIC mining rig, here are some factors to be considered:

• What coins can be mined? The list of cryptocurrencies that can be mined with ASICs is far smaller than those that can be mined with a GPU rig. Cryptocurrencies that can be mined with ASICs include Bitcoin, Litecoin, and several others.
• Rig location: Though GPU mining rigs can be located in one’s home, ASIC miners are louder and generate much more heat. This means that one’s home is not ideal for an ASIC miner, and alternate locations like a basement or garage with cooling need to be considered.
• Power consumption: The latest generation of ASIC machines are more energy-efficient than GPU rigs but consume tremendous power nevertheless. An ASIC miner based in one’s home may necessitate upgrading the electrical wiring system to handle the increased power load.
• Choosing a Bitcoin mining pool: Mining pools enable miners to combine the power of their ASIC miner rigs to mine bitcoin and share the rewards for successfully minted blocks. Factors to be considered when choosing a pool include its reputation, size, and payment rules.
• Return on Investment: Is the return on investment sufficiently high enough to justify the upfront cost of an ASIC miner and ongoing operating expenses?

What Is an Autoregressive Integrated Moving Average (ARIMA)?

An autoregressive integrated moving average, or ARIMA, is a statistical analysis model that uses time series data to either better understand the data set or to predict future trends. 

A statistical model is autoregressive if it predicts future values based on past values. For example, an ARIMA model might seek to predict a stock’s future prices based on its past performance or forecast a company’s earnings based on past periods.

Understanding Autoregressive Integrated Moving Average (ARIMA)

An autoregressive integrated moving average model is a form of regression analysis that gauges the strength of one dependent variable relative to other changing variables. The model’s goal is to predict future securities or financial market moves by examining the differences between values in the series instead of through actual values.

An ARIMA model can be understood by outlining each of its components as follows:

• Autoregression (AR): refers to a model that shows a changing variable that regresses on its own lagged, or prior, values.
• Integrated (I): represents the differencing of raw observations to allow the time series to become stationary (i.e., data values are replaced by the difference between the data values and the previous values).
• Moving average (MA):  incorporates the dependency between an observation and a residual error from a moving average model applied to lagged observations.

ARIMA Parameters

Each component in ARIMA functions as a parameter with a standard notation. For ARIMA models, a standard notation would be ARIMA with p, d, and q, where integer values substitute for the parameters to indicate the type of ARIMA model used. The parameters can be defined as:

• p: the number of lag observations in the model, also known as the lag order.
• d: the number of times the raw observations are differenced; also known as the degree of differencing.
• q: the size of the moving average window, also known as the order of the moving average.

For example, a linear regression model includes the number and type of terms. A value of zero (0), which can be used as a parameter, would mean that particular component should not be used in the model. This way, the ARIMA model can be constructed to perform the function of an ARMA model, or even simple AR, I, or MA models.

ARIMA and Stationary Data

In an autoregressive integrated moving average model, the data are differenced in order to make it stationary. A model that shows stationarity is one that shows there is constancy to the data over time. Most economic and market data show trends, so the purpose of differencing is to remove any trends or seasonal structures. 

Seasonality, or when data show regular and predictable patterns that repeat over a calendar year, could negatively affect the regression model. If a trend appears and stationarity is not evident, many of the computations throughout the process cannot be made and produce the intended results.

How to Build an ARIMA Model

To begin building an ARIMA model for an investment, you download as much of the price data as you can. Once you’ve identified the trends for the data, you identify the lowest order of differencing (d) by observing the autocorrelations. If the lag-1 autocorrelation is zero or negative, the series is already differenced. You may need to difference the series more if the lag-1 is higher than zero.

Next, determine the order of regression (p) and order of moving average (q) by comparing autocorrelations and partial autocorrelations. Once you have the information you need, you can choose the model you’ll use.

Pros and Cons of ARIMA

ARIMA models have strong points and are good at forecasting based on past circumstances, but there are more reasons to be cautious when using ARIMA. In stark contrast to investing disclaimers that state “past performance is not an indicator of future performance…,” ARIMA models assume that past values have some residual effect on current or future values and use data from the past to forecast future events.

The following table lists other ARIMA traits that demonstrate good and bad characteristics.

The Bottom Line

The ARIMA model is used as a forecasting tool to predict how something will act in the future based on past performance. It is used in technical analysis to predict an asset’s future performance.

ARIMA modeling is generally inadequate for long-term forecastings, such as more than six months ahead, because it uses past data and parameters that are influenced by human thinking. For this reason, it is best used with other technical analysis tools to get a clearer picture of an asset’s performance.

What Is an Autoregressive Model?

A statistical model is autoregressive if it predicts future values based on past values. For example, an autoregressive model might seek to predict a stock’s future prices based on its past performance.

Understanding Autoregressive Models

Autoregressive models operate under the premise that past values have an effect on current values, which makes the statistical technique popular for analyzing nature, economics, and other processes that vary over time. Multiple regression models forecast a variable using a linear combination of predictors, whereas autoregressive models use a combination of past values of the variable.

An AR(1) autoregressive process is one in which the current value is based on the immediately preceding value, while an AR(2) process is one in which the current value is based on the previous two values. An AR(0) process is used for white noise and has no dependence between the terms. In addition to these variations, there are also many different ways to calculate the coefficients used in these calculations, such as the least squares method.

These concepts and techniques are used by technical analysts to forecast security prices. However, since autoregressive models base their predictions only on past information, they implicitly assume that the fundamental forces that influenced the past prices will not change over time. This can lead to surprising and inaccurate predictions if the underlying forces in question are in fact changing, such as if an industry is undergoing rapid and unprecedented technological transformation.

Nevertheless, traders continue to refine the use of autoregressive models for forecasting purposes. A great example is the Autoregressive Integrated Moving Average (ARIMA), a sophisticated autoregressive model that can take into account trends, cycles, seasonality, errors, and other non-static types of data when making forecasts.

Example of an Autoregressive Model

Autoregressive models are based on the assumption that past values have an effect on current values. For example, an investor using an autoregressive model to forecast stock prices would need to assume that new buyers and sellers of that stock are influenced by recent market transactions when deciding how much to offer or accept for the security.

Although this assumption will hold under most circumstances, this is not always the case. For example, in the years prior to the 2008 Financial Crisis, most investors were not aware of the risks posed by the large portfolios of mortgage-backed securities held by many financial firms. During those times, an investor using an autoregressive model to predict the performance of U.S. financial stocks would have had good reason to predict an ongoing trend of stable or rising stock prices in that sector. 

However, once it became public knowledge that many financial institutions were at risk of imminent collapse, investors suddenly became less concerned with these stocks’ recent prices and far more concerned with their underlying risk exposure. Therefore, the market rapidly revalued financial stocks to a much lower level, a move which would have utterly confounded an autoregressive model.

It is important to note that, in an autoregressive model, a one-time shock will affect the values of the calculated variables infinitely into the future. Therefore, the legacy of the financial crisis lives on in today’s autoregressive models.

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