Category: Holt winters machine learning

Techniques of time series forecasting ranging from the simple Holt Winters to the complex, DNNs and Multiple Temporal Aggregation are available on some but not all platforms. Increasingly, AI differentiates the usefulness of these apps. Call center operations seems to be the one industry most strongly driving sophisticated AI-based forecasting solutions. Clearly there are many others that can benefit ranging from retail to healthcare to any of the many gig-economy business models where too many workers means unnecessary cost and too few means potentially unhappy customers.

The fact that this data is already present in electronic form facilitates creating the modeling features for different model types. But platforms that service broad industry types or even other industry specific platforms are rapidly catching up.

Gartner identifies the sophistication of staff scheduling and forecasting techniques as one of the most significant variables currently differentiating these platforms, with only a few offering a full range of forecasting algorithms.

You can find these in the broad-case HCM human capital management suites that seek to integrate all aspects of HR, or the more focused WFM workforce management applications that typically seek to at least integrate:. And while there are applications targeting companies with as few as 25 to 50 hourly scheduled workers, the real payoff will come when you have many more. Some platforms claim to be able to handle detailed forecasts for as many as 10, workers but a sweet spot seems to be in the hundreds to a few thousand.

In this range there will also be significant historical data to help make your forecasts more accurate. Each of these may be best served by different techniques.

These might arise from:. These need to be understood and isolated in the historical data before putting them through your forecasting routine and anticipated as new events in the forward forecast. When evaluating forecasting applications or working with different techniques, these four are currently most common though not all are available in all platforms.

Holt Winters Triple Exponential Smoothing. Holt Winters has been the go-to technique for some years. The advantage is that this can be performed on a spreadsheet. The disadvantage is that it is easy to over fit the data by selecting the wrong smoothing coefficient for each factor.

ARIMA has proven accurate in many complex situations and requires a professional level understanding to select the right version. For example you could set one seasonality to account for 30 minute intervals 48 time periods in 24 hours and a second to weekly trends time periods of 30 minutes.

As this illustration shows, ARMIA and its variants are able to produce sophisticated forecasts taking multiple patterns into account. Image source: Rob Hyndman.Holt-Winters forecasting is a way to model and predict the behavior of a sequence of values over time—a time series. Holt-Winters is one of the most popular forecasting techniques for time series. We want to fix that, so we wrote this post: a visual introduction to Holt-Winters. Holt-Winters is a model of time series behavior.

Forecasting always requires a model, and Holt-Winters is a way to model three aspects of the time series: a typical value averagea slope trend over time, and a cyclical repeating pattern seasonality. The three aspects of the time series behavior—value, trend, and seasonality—are expressed as three types of exponential smoothing, so Holt-Winters is called triple exponential smoothing.

The model predicts a current or future value by computing the combined effects of these three influences.

When You Want Holt-Winters Instead of Machine Learning

Seasonality can be confusing. A season is a fixed length of time that contains the full repetition. Within the season, there are periods, which is the granularity of prediction. If you want to model a value for every hour of every day within a week, your season is hours long and your period is 1 hour.

The hardest parts of Holt-Winters forecasting are understanding how the model works, and choosing good parameters. The usual way to explain Holt-Winters is by showing a bunch of complicated equations with Greek letters and subscripts.

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The pattern is obvious: the plot repeats the values [0, 1, 0, 0, 0]. Can you tell me what the next 5 values are going to be?

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Of course you can, because I just told you! They are [0, 1, 0, 0, 0]. Recall that Holt-Winters has a trend component. If we set its parameter to zero, Holt-Winters ignores the trend slopeso the model simplifies. In our plot, the values relative to 0.

Forecasting with trend is just an enhancement of this.

holt winters machine learning

Instead of using a fixed average as the foundation, you just have to incorporate the slope of the line. You already know that, by definition, the example series repeats itself every five points, i. How can you figure it out?

What are the consequences of being wrong?

holt winters machine learning

The right seasonality is crucial to Holt-Winters forecasting. The forecast, which is the red line in the chart, becomes less accurate and turns into garbage. To get good results, you need to give the model good parameters.This article provides a brief explanation of the Holt-Winters Forecasting model and its application in the business environment.

The Holt-Winters algorithm is used for forecasting and It is a time-series forecasting method.

What is the Holt-Winters Forecasting Algorithm and How Can it be Used for Enterprise Analysis?

Time series forecasting methods are used to extract and analyze data and statistics and characterize results to more accurately predict the future based on historical data. The Holt-Winters forecasting algorithm allows users to smooth a time series and use that data to forecast areas of interest.

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Exponential smoothing assigns exponentially decreasing weights and values against historical data to decrease the value of the weight for the older data. In other words, more recent historical data is assigned more weight in forecasting than the older results.

Time-Series forecasting methods and, in particular, the Holt-Winters forecasting algorithm can be helpful in providing forecasts for planning purposes by using historical data in a meaningful way. Because the results are smoothed, and the user can select the best option for the TYPE of data to be analyzed, the enterprise can avoid assigning too much weight or importance to older data that may no longer be as valid because of changing buying behaviors, market competition or other factors.

When users select the appropriate forecasting algorithm for the data they wish to analyze, they can produce and share reports and data that will provide clear direction and decision support.

In order to achieve the right results, it is imperative that a user select the right forecasting algorithm, based on the pattern and underlying data. These augmented analytics tools use machine learning to auto-detect and recommend the best algorithm so users do not have to guess at the right selection. Smart Visualization ensures that data and its interpretation are clearly depicted in simple, natural language. To provide flexible business intelligence and forecasting tools and ensure data democratization among business users, as well as accurate planning methods, an enterprise must select tools that are easy-to-use and easy to implement.

The solution must include a full suite of advanced analytics tools to empower business users and create Citizen Data Scientists whose contribution to the organization will be a real asset and a true contributor to business results. The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist.

All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists. Skip to Main Content. Other posts. Find Flexible, Intuitive Business Intelligence! Mar 31, Forecast and Plan with Confidence and Predictive Analytics!

Mar 23, Mar 19, Mar 16, Mar 12, ML gets a lot of hype, but its classical predecessors are still immensely powerful, especially in the time series space. Error, trend, seasonality forecast ETSautoregressive integrated moving average ARIMAand Holt-Winters are three classical methods that are not only incredibly popular but also excellent time series predictors.

Anais Dotis dives into how the Holt-Winters forecasting algorithm works. Anais Dotis-Georgiou is a developer advocate at InfluxData with a passion for making data beautiful using data analytics, AI, and machine learning. She takes the data that she collects and does a mix of research, exploration, and engineering to translate the data into something of function, value, and beauty. Skip to main content. New York, NY. Add to Your Schedule. Anais Dotis InfluxData. Who is this presentation for?

Data scientists, developers, and analysts. Description ML gets a lot of hype, but its classical predecessors are still immensely powerful, especially in the time series space.

Prerequisite knowledge Experience with calculus. InfluxData Anais Dotis-Georgiou is a developer advocate at InfluxData with a passion for making data beautiful using data analytics, AI, and machine learning. Cloudera O'Reilly. Google Cloud IBM. Contributing Sponsors.

Exabyte Sponsors. Kyligence Pitney Bowes Talend. Content Sponsor. Google Cloud. Impact Sponsors. Supporting Sponsor. Non Profit. Contact us confreg oreilly.Exponential Smoothing Techniques.

It reduces the effect of irregular variations in time series data. Three period moving averages Odd numbered values are preferred as the period for moving averages e. If we want to calculate moving averages with even number of observations such as 2 or 4then we have to take average of moving averages to centre the values.

For the first time period, we cannot forecast left blank. Some use the average of values of first few observations instead average of let us say first four observations: 46,56,54 and This depends on data. But in presence of trend, single exponential smoothing is inadequate as shown in the graph below link.

This method involves computing level and trend components. Forecast is the sum of these two components. As shown in the below picture, equation for level component is similar to the previously discussed single exponential smoothing. The second equation, trend component, as the name suggests captures the trend. Finally we sum these two components to arrive at the forecasts.

I suggest you to read this blogpost for more on this. Double Exponential Smoothing Holt's method in Python. Double exponential smoothing works fine when there is trend in time series, however it fails in presence of seasonality.

Additionally, Triple Exponential Smoothing includes a seasonal component as well. It is also called Holt-Winters method. There are two models under these: Multiplicative Seasonal Model Additive Seasonal Model For detailed methodology you can go through this excellent paper. Labels: timeseries. Newer Post Older Post Home.Comment 0. Machine Learning ML gets a lot of hype, but its Classical predecessors are still immensely powerful, especially in the time-series space.

Actually, all of the statistical methods have a lower prediction error than the ML methods do. My hope is that after finishing this three-part blog post series, you'll have a strong conceptual and mathematical understanding of how Holt-Winters works.

I focus on Holt-Winters for three reasons.

When Holt-Winters is better than machine learning

If you understand Holt-Winters, then you will easily be able to understand the most powerful prediction method for time series data among the methods above. Before you select any prediction method, you need to evaluate the characteristics of your dataset. To determine whether your time series data a good candidate for Holt-Winters or not, you need to make sure that your data:. Single Exponential Smoothing SES is the simplest exponential smoothing method exponential smoothing is just a technique for smoothing time-series data where exponentially decreasing weights are assigned to past observations.

With this method, the forecasted value is equal to the last observed value. Also, taking the percent difference between the actual and predicted values can be a good way to uncover seasonality. A forecast from SES is just an exponential weighted average. A smoothing parameter relates the previous smoothed statistic to the current observation and is used to produce a weighted average of the two.

There are a variety of methods to determine the best smoothing parameter. However, minimizing the RSS residual sum of squared errors is probably the most popular we will cover this in Part Two.

The weights decrease exponentially, thereby lending most recent observation the largest impact on the prediction. Notice how the sum of these weights approaches 1. More simply, this guarantees that your prediction is on the same scale as your observations.

If the sum of the weights was equal to 1. The sum of the weights converging to 1 is a geometric convergence.

When Holt-Winters is better than machine learning

Mathematicians love to rewrite formulas. Next, we'll take a look at how we get the Component form of SES because it is the same form that is most commonly used to express Holt-Winters. In this way, we get to encapsulate the iterative nature of Eq.Nicolas Vandeput. Data Science for Supply Chain Forecast.

This article is the forth in the Holt-Winters serie. We recommend you to read the first articles first. You can see all the articles here. With the first two models we saw, we learned how to predict the trend and the level of the demand. After that, we added a third layer of intelligence to our model: the possibility to damp the trend.

Now we will add a forth layer of intelligence to it: the seasonality. The idea is that the model will learn a multiplicative seasonal factor for each period and apply it in the future. As for the trend and the levelthe seasonality will be learned via an exponential weighting method with the learning parameter. Note that we will also discuss the case of additive seasonality in another post.

holt winters machine learning

You can see on the dummy data set below how the seasonality factors help to follow the historical demand. See how the level changes over time but the seasonality is not so impacted even though it could vary over time. If you need help with the notations or to get refreshed on the basics you should read this article first.

There is some literature on how to initialize seasonal factors for holt-winters algorithms. Note that if you have an important trend in the history you might face an issue and you should potentially first remove the trend in your data. We should use a scientific approach here: test different methods and see which one gives the best result against a proper test data set.

We will initialize the first forecast as a null value, and the second forecast point as simply the first observation.

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See that we already have to de-seasonalize the level. We will have to do the same for the trend initialization:. Pay attention that we use the seasonal factor that was calculated the previous season:.

They are both de-seasonalized. See how is updated by the most recent demand observation divided by the seasonality. As the trend is the difference between two consecutive levels, it is also de-seasonalized.

The seasonality factor is then estimated based on the most recent observation the demand divided by the level and the previous estimation just as for and. The parameter will also determine how much weight we should give to the most recent observation compared to the previous estimation. Business wise it is rather exceptional to assume that the demand seasonality could drastically change from one year to another.

With a high you might face overfitting. As soon as we are out of the historical period we will keep the seasonal factors, level and trend constants well except the trend if it is damped of course. I use here a dummy list for the time series. I think this is easier for you to test the code without the burden to download an extra data set. Note that in this example we have a seasonality of 10 periods this is recorded in the variable slen.

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Then we will create a function that will initialize the seasonal factors.

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