Industries from power and retail to transportation and finance right this moment depend on time collection forecasting for projecting product demand, useful resource allocation, monetary efficiency, predictive upkeep, and numerous different purposes. Regardless of the potential of time collection forecasting to remodel enterprise fashions and enhance backside strains, many corporations have but to undertake its applied sciences and reap the advantages. Let’s begin with a definition, and comply with with a quick overview of purposes and strategies.
Time collection forecasting is a method for predicting future occasions by analyzing previous traits, based mostly on the idea that future traits will maintain just like historic traits. Forecasting entails utilizing fashions match on historic knowledge to foretell future values. Prediction issues that contain a time part require time collection forecasting, which gives a data-driven method to efficient and environment friendly planning.
Time collection forecasting purposes
The purposes of time collection fashions are many and wide-ranging, from gross sales forecasting to climate forecasting. In choices that contain an element of uncertainty in regards to the future, time collection fashions have been discovered to be among the many only strategies of forecasting.
Time collection forecasts inform all types of enterprise choices. Some examples:
- Forecasting energy demand to resolve whether or not to construct one other energy era plant within the subsequent 5 years
- Forecasting name volumes to schedule employees in a name heart subsequent week
- Forecasting stock necessities to inventory stock to fulfill demand
- Forecasting provide and demand to optimize fleet administration and different elements of the provision chain
- Predicting gear failures and upkeep necessities to attenuate downtime and uphold security requirements
- Forecasting an infection charges to optimize illness management and outbreak packages
- Predicting buyer rankings by way of to forecasting product gross sales
Relying on the circumstances and on what’s being forecast, forecasts can contain totally different time horizons.
How time collection forecasts are developed
Time collection forecasts are developed based mostly on time collection evaluation, which contains strategies for analyzing time collection knowledge to extract significant statistics and different traits of the info. The aim of time collection forecasting is to foretell a future worth or classification at a selected cut-off date.
Time collection forecasting begins with a historic time collection. Analysts study the historic knowledge and verify for patterns of time decomposition, comparable to traits, seasonal patterns, cyclical patterns, and regularity. These patterns assist inform knowledge analysts and knowledge scientists about which forecasting algorithms they need to use for predictive modeling.
The historic time collection used for knowledge analytics in preparation for forecasting is also known as pattern knowledge. Pattern knowledge is a subset of the info that’s consultant of your complete set of knowledge. Each machine studying or classical forecasting technique incorporates some statistical assumptions. Knowledge scientists study the pattern knowledge to grasp its statistical attributes. This permits them to find out which fashions they’ll select from and what knowledge preprocessing must be utilized to keep away from violating any assumptions of their mannequin choice.
For instance, many time collection forecasting algorithms assume that the time collection doesn’t exhibit a pattern. So earlier than utilizing a forecasting algorithm, the info scientist should apply a wide range of statistical assessments on their pattern knowledge to find out whether or not or not their knowledge displays a pattern. If a pattern is discovered, they’ll elect to both choose a unique mannequin or take away the pattern from their knowledge by way of differencing. Differencing is a statistical approach whereby a non-stationary time collection, or a time collection with pattern, is remodeled right into a stationary time collection.
Many kinds of machine studying forecasting fashions require coaching. Knowledge scientists prepare time collection forecasting fashions on the pattern knowledge. As soon as the mannequin has been skilled, the info scientists check out their predictive modeling or forecasting algorithms on further pattern knowledge to find out the accuracy of their mannequin choice and to tweak the parameters of the mannequin to optimize it additional.
To examine real-world time collection forecasting use instances, see the Veritas storage forecasting and Playtech machine learning case studies.
Time series decomposition
Time series data can exhibit a variety of patterns, so it is often helpful to split a time series into components, each representing an underlying pattern category. This is what decompositional models do.
The decomposition of time series is a statistical task that deconstructs a time series into several components, each representing one of the underlying categories of patterns. When we decompose a time series into components, we think of a time series as comprising three components: a trend component, a seasonal component, and residuals or “noise” (anything outside the trend or seasonality in the time series).
Moving average smoothing is often a first step in time series analysis and decomposition. The moving average removes some of the stochastic nature of the data and allows you to more easily identify whether or not your data exhibits any trend.
Classical decomposition is one of the most popular types of time series decomposition. There are two main types of classical decomposition: decomposition based on rates of change and decomposition based on predictability. Further, decomposition based on rates of change can be either additive or multiplicative decomposition:
- In an additive time series, the three components (trend, seasonality, and residuals) add together to make the time series. An additive model is used when the variations around the trend do not vary with the level of the time series.
- In a multiplicative time series, the three components multiply together to make the time series. A multiplicative model is appropriate if the trend is proportional to the level of the time series.
Time series regression
Regression models are among the most common types of time series analysis and forecasting techniques. Regression models describe a mathematical relationship between the forecasted variable and a single predictor variable. The most well-known regression model is a linear model. However, nonlinear regression models are extremely popular. Multiple regression models describe a relationship between a forecasted variable and several predictor variables. Understanding regression models is the basis for understanding more sophisticated time series forecasting methods.
Exponential smoothing is the basis for some of the most powerful forecasting methods. Exponential smoothing produces forecasts based on weighted averages of past observations. In other words, these models produce forecasts where the forecast most closely resembles recent observations. Exponential smoothing techniques are extremely popular because they can be very effective predictors and can be applied to a wide variety of data and use cases.
Common types of exponential smoothing include single exponential smoothing (SES), double exponential smoothing (DES), and triple exponential smoothing (TES, also known as the Holt-Winters method). SES forecasts are weighted averages of the time series itself while DES forecasts are weighted averages of both the trend and the time series. Finally, Holt Winters or TES forecasts are weighted averages of the seasonality, trend, and time series.
The ETS model (referring to the explicit modeling of error, trend, and seasonality) is another type of exponential smoothing technique. ETS is similar to Holt-Winters but was developed after Holt-Winters. It uses a different optimization method for the model initialization and also overcomes some esoteric shortcomings of Holt-Winters that exist in relatively uncommon time series scenarios.
Autoregressive integrated moving average, or ARIMA, models are another time series forecasting method. They are among the most widely used time series forecasting techniques — as widely used as exponential smoothing methods. While exponential smoothing methods generate forecasts based on historical components of the data, ARIMA models take advantage of autocorrelation to produce forecasts. Autocorrelation is when a time series displays correlation between the time series and a lagged version of the time series.
There are two main types of ARIMA models, non-seasonal ARIMA models and seasonal ARIMA, or SARIMA, models. To define ARIMA and SARIMA, it’s helpful to first define autoregression. Autoregression is a time series model that uses observations from previous time steps as input to a regression equation to predict the value at the next time step. So, in an autoregressive model, the forecasts correspond to a linear combination of past values of the variable. And in a moving average model, the forecasts correspond to a linear combination of past forecast errors. The ARIMA models combine the two approaches.
One of the underlying assumptions of an ARIMA model is that the time series is stationary. Stationary time series is a time series whose components do not depend on when the time series is observed. In other words, the time series doesn’t exhibit trend or seasonality. Because ARIMA models require the time series to be stationary, differencing may be a necessary preprocessing step before using an ARIMA model for forecasting.
The SARIMA model extends ARIMA by adding a linear combination of seasonal past values and/or forecast errors.
Neural networks are growing in popularity. Neural networks aim to solve problems that would be impossible or difficult to solve with statistical or classical methods. Two of the most popular time series forecasting neural networks are artificial neural networks (ANNs) and recurrent neural networks (RNNs). ANNs were inspired by the way the nervous system and brain processes information. RNNs were designed to be able to remember important information about recent inputs, which they can then use to generate accurate forecasts.
A long short term memory network (LSTM) is a type of RNN that is especially popular in the time series space. It has forget gates and feed forward mechanisms that allow the network to retain information, forget extraneous inputs, and update the forecasting procedure to model and forecast complex time series problems.
Anais Dotis-Georgiou is a developer advocate for InfluxData with a passion for making data beautiful with the use of data analytics, AI, and machine learning. She takes the data that she collects and applies a mix of research, exploration, and engineering to translate the data into something of function, value, and beauty. When she is not behind a screen, you can find her outside drawing, stretching, boarding, or chasing after a soccer ball.
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