What is Time Series Forecasting ?



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What is Time Series Forecasting ?

Time series forecasting is a crucial part of machine learning that is sometimes underestimated. It is significant since there are numerous prediction issues with a temporal component. These issues are ignored since it is the time component that makes time series problems tough to solve.

In the standard machine learning methodology, we usually randomly divide the data into training, test, and cross-validation data.

Predictions for initial data are made when the actual outcome may not be known until a future stage. Although the future is predicted, all earlier observations are nearly always regarded equally. Perhaps with some slight temporal dynamics to overcome the concept of “concept drift,” such as using only the most recent year of observations rather than all available data.

A time series dataset is different. A time dimension is added by time series, which provides an explicit order dependence between observations.

Types of Forecasting

Components of Time Series

Time series analysis is a collection of approaches for better understanding a dataset. The breakdown of a time series into four main pieces is perhaps the most helpful of these:

  1. Level — The series’ baseline value if it were a straight line.
  2. Trend — The series’ optional and frequently linear growing or decreasing tendency throughout time.
  3. Seasonality — Repeating patterns or cycles of behavior that can occur throughout time.
  4. Noise — The optional variability in the observations that the model cannot explain.

Where is Time-Series analysis used?

Time Series analysis aids in the analysis of previous data, which subsequently becomes an important aspect in projecting the future. Most companies rely on time series forecasting to determine the activities and decisions that will be done. Organizations do this by gathering enormous volumes of historical data and comparing it to the present trend, allowing them to make more informed decisions. Here are two of the most common applications of Time Series Forecasting.

1. Stock Market Analysis

Stock prices are essentially discrete-time models with data points (such as price) that are independent of time. Some of the major components, such as Trend and Seasonality, may be calculated using Time Series forecasting and Algorithms to assist investors to predict price movement.

2. Budgetary Analysis

Maintaining a consistent income of cash flow is vital for a company because it allows stakeholders to offer an accurate prediction of its revenues and expenditures in that financial year. That is why budgeting is essential. It enables firms to plan their budgets for the following year based on the current year’s allocation and costs.

Popular Algorithms in the Industry

All of the algorithms shown below undertake some type of basic analysis of the data provided in order to determine some of the relevant properties for predicting purposes.

Autoregressive (AR)

In order to estimate future trends, the autoregressive model learns the behavioral pattern of prior data. In the regression model, the prior time period’s response variable has become the new predictor, and the errors have been assumed regarding mistakes in any basic linear regression model. This is straightforward to comprehend. It is worth noting that the equation relies on t-1 and so on until t-n for a forecast of time t. This is known as lag prediction since it is based on data points from the prior period of time.

Moving Average (MA)

The Moving Average technique, unlike the AR model, leverages prior projected mistakes (or noise) in a regression-like model to develop an averaged trend over the data.

Moving average is defined as the weighted sum of present random mistakes and prior errors, as indicated in the following equation:

  • c refers to some white noise with zero mean and small variance
  • θ1 refers to the coefficient of the first data point
  • ϵt-1 and ϵt refer to the past and current period respectively

Autoregressive Moving Average (ARMA)

The ARMA method is just a combination of the Moving Average and Autoregression algorithms discussed above. The velocity and pattern of the trend are extracted by Autoregressive, while the white noise effects are captured by Moving Average, and the two are combined to form ARMA.

  • p is the order of the Autoregressive model
  • q is the order of the Moving Average model

Autoregressive Integrated Moving Average (ARIMA)

ARIMA is one of the most often utilized algorithms in Time Series forecasting. ARIMA seeks to explain the autocorrelation between data points, whilst other models characterize the trend and seasonality of the data points.

To make it stationary, an ARIMA algorithm-generated model may be described as a differenced time series forecasting model.

  • α represents some value of white noise
  • p is the order of Autoregressive term (lagged predictions)
  • q is the order of Moving Average (lagged errors)

Generally ARIMA is expressed in a format that looks like this:

ARIMA(p, d, q)

Exponential Smoothing (ES)

The exponential smoothing procedure is used to generate a relatively smooth time series predicting trend by exponentially decreasing the weight of earlier data values, resulting in weighted averages. To maximize model performance to a slowly fluctuating mean, the degree of smoothing (the width of the moving average) is modified.

The most basic version of exponential smoothing is as follows:

  • α is the degree of smoothing ranging 0 < α < 1. In other words, how much importance is to be applied to that particular observation in that particular frame in time.

Exponential smoothing methods are suitable for any time series forecasting or analysis since they demand little computing power.

To summarize, several alternative Time Series forecasting algorithms and analytic approaches may be used to obtain the necessary information. It all relies on the application use cases and complexity, whether employing Autoregressive algorithms to generate trend patterns for forecasting or the ARIMA model to infer the correlation pattern of the data. Because most time series forecasting assessments are easy, choose the simplest and easiest model is the best way to go.

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