Today’s energy and retail to transportation and finance industries rely on forecasting product demand, resource allocation, financial performance, predictive integrity, and time-series forecasting for myriad other applications. Despite the potential for time-series forecasting to transform business models and improve revenue, many companies have not yet adopted the technology to benefit. Let’s start with the definition and get an overview of the application and methods.
Time series forecasting is a method for predicting future events by analyzing past trends, based on the assumption that future trends are similar to past trends. Forecasting involves predicting future values using a model that fits historical data. Forecasting problems that include time components require time series forecasting. This provides a data-driven approach to effective and efficient planning.
Time series prediction application
Time-series models have a wide range of uses, from sales forecasts to weather forecasts. Time series models have proven to be one of the most effective forecasting methods for decisions that include factors of uncertainty about the future.
Time series forecasting informs all kinds of business decisions. Some examples:
- Forecasting electricity demand to decide whether to build another power plant in the next 5 years
- Forecasting volume to schedule call center staff next week
- Forecast inventory requirements to inventory to meet demand
- Supply and demand forecasts to optimize fleet management and other aspects of the supply chain
- Predict equipment failures and maintenance requirements to minimize downtime and maintain safety standards
- Prediction of infection rates to optimize disease management and outbreak programs
- Forecast customer evaluation up to product sales forecast
Forecasts may include different time periods, depending on the situation and what you are forecasting.
How to create a time series forecast
Time series forecasts are created based on Time series analysisConsists of a method of analyzing time series data to extract meaningful statistics and other characteristics of the data. The goal of time series forecasting is to predict future value or classification at a particular point in time.
Time series forecasts start with a past time series. Analysts examine historical data to check for time-resolved patterns such as trends, seasonal patterns, periodic patterns, and regularity. These patterns help data analysts and data scientists know about the predictive algorithms used for predictive modeling.
The historical time series used for data analysis in preparation for forecasts is often referred to as sample data. Sample data is a subset of the data that represents the entire dataset. All machine learning or classical forecasting methods incorporate some statistical assumptions. Data scientists examine sample data to understand its statistical attributes. This allows you to determine which models you can choose and what data preprocessing you need to apply so that you do not violate the model selection assumptions.
For example, many time series prediction algorithms assume that time series do not show trends. Therefore, before using predictive algorithms, data scientists should apply various statistical tests to the sample data to determine if the data is trending. If you find a trend, you can choose another model or use the diff to remove the trend from your data. Differences are a statistical technique that transforms a non-stationary time series or a prone time series into a stationary time series.
Many types of machine learning predictive models require training. Data scientists train time series forecasting models with sample data. Once the model is trained, the data scientist tests predictive modeling or predictive algorithms with additional sample data to determine the accuracy of model selection and fine-tune the model’s parameters for further optimization.
Time series decomposition
Since time series data can show different patterns, it is often useful to divide the time series into components. Each component represents the underlying pattern category. This is what the decomposition model does.
Time series decomposition is a statistical task that decomposes a time series into several components, each representing one of the underlying categories of the pattern. When you decompose a time series into components, you think of the time series as being composed of three components: a trend component, a seasonal component, and a residual or “noise” (other than the trend or seasonality of the time series).
Moving average smoothing is often the first step in time series analysis and decomposition. Moving averages remove some of the stochastic nature of the data, making it easier to identify if the data is showing any trends.
Classical decomposition is one of the most common types of time series decomposition.There are two main Types of classic decomposition: Decomposition based on rate of change and decomposition based on predictability. In addition, rate-based decompositions can be either additive decompositions or multiplicative decompositions.
- In an additive time series, three elements (trend, seasonality, and residual) are added to create a time series. If the fluctuations around the trend do not change with the level of the time series, the additive model is used.
- In a multiplicative time series, the three components are multiplied together to create a time series. If the trend is proportional to the level of the time series, then the multiplication model is appropriate.
Time series regression
Regression models are one of the most common types of time series analysis and prediction methods. Regression models describe the mathematical relationship between a predictor and a single predictor. The most well-known regression model is the linear model. However, nonlinear regression models are very popular. The multiple regression model describes the relationship between a predictor and some predictors. Understanding regression models is the basis for understanding more sophisticated time series prediction methods.
Exponential smoothing is the basis of some of the most powerful forecasting methods. Exponential smoothing produces predictions based on weighted averages of historical observations. In other words, these models produce predictions whose predictions most closely resemble recent observations. Exponential smoothing techniques are very popular because they are very effective predictors and can be applied to a 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 Holt-Winters). .. The SES forecast is the weighted average of the time series itself, and the DES forecast is the weighted average of both the trend and the time series. Finally, Holt Winters or TES forecasts are seasonal, trend, and time-series weighted averages.
The ETS model (see Explicit Modeling of Errors, Trends, and Seasonality) is another type of exponential smoothing technique. ETS is similar to Holt-Winters, but was developed after Holt-Winters. It uses different optimization techniques to initialize the model and overcomes some of the esoteric drawbacks of Holt-Winters that exist in relatively rare time series scenarios.
The autoregressive integrated moving average (ARIMA) model is another time series prediction method. These are one of the most widely used time series forecasting methods and are as widely used as the exponential smoothing method. The exponential smoothing method produces forecasts based on the historical component of the data, while the ARIMA model uses autocorrelation to generate forecasts. Autocorrelation is when the time series displays the correlation between the time series and the delayed version of the time series.
There are two main types of ARIMA models: non-seasonal ARIMA models and seasonal ARIMA (SARIMA) models. To define ARIMA and SARIMA, it is convenient to define autoregressive first. Autoregressive is a time series model that uses observations from the previous time step as input to the regression equation to predict values at the next time step. Therefore, in the autoregressive model, the prediction corresponds to a linear combination of the past values of the variables. Also, in the moving average model, the forecast corresponds to a linear combination of past forecast errors. The ARIMA model is a combination of the two approaches.
One of the basic assumptions of the ARIMA model is that the time series is stationary. A constant time series is a time series in which the component does not depend on when the time series is observed. In other words, the time series shows no trends or seasonality. Because the ARIMA model requires a stationary time series, it can be a preprocessing step that requires a difference before using the ARIMA model for forecasting.
The SARIMA model extends ARIMA by adding a linear combination of seasonal historical values and prediction errors.
Neural networks are becoming more popular. Neural networks are intended to solve problems that cannot or are difficult to solve by statistical or classical methods. Two of the most popular time series predictive neural networks are artificial neural networks (ANN) and recurrent neural networks (RNN). ANN was inspired by the way the nervous system and brain process information. RNNs are designed to store important information about recent inputs and can be used to generate accurate predictions.
Long Short Term Memory Networks (LSTMs) are a type of RNN that is especially popular in time series space. We forget about gates and feedforward mechanisms that allow networks to retain information, forget irrelevant inputs, and update forecasting procedures to model and predict complex time series problems.
Anais Dotis-Georgiou InfluxData He is passionate about beautifying data using data analytics, AI and machine learning. She takes the data she collects and applies a combination of research, research, and engineering to transform the data into something that is functional, valuable, and aesthetically pleasing. When she’s not behind the screen, you can find her drawing, stretching, boarding, and chasing a soccer ball.
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Overview of time series forecasting
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