Questions tagged [exponential-smoothing]

A basic forecasting technique for time series data, optionally including trend and/or seasonality, but (usually) excluding causal influences.

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120 views

Holt-winters method, outlier day of week

Hopefully this isn't too off topic. I've just received test results and disagree with some explanations of a question. The TA and I can't seem to resolve our differences and I'm starting to think ...
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140 views

method for predicting a curve

I have data on several curves. the data is of the form: curve_id x y and there are many x/y pairs for each curve and x is limited to some known range. overall, the curves look quite similar in ...
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Fitting a Local Poisson model (Exponential Smoothing) [closed]

I am working through "Forecasting with Exponential Smoothing". I am stuck on exercise 16.4 on the part that states: The data set partx contains a history of ...
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Possible issues using moving average as an input for exponential smoothing?

I'm currently working with an ERP system that applies Exponential Smoothing to what it defines as "demand," which is itself a simple 90-day moving average of sales. I initially didn't trust it ...
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General exponential smoothing to linear functions of past observations

I am just trying to derive an equation in "Forecasting with Exponential Smoothing" page 36 section 3.2. I am given the following $\hat{y}_{t|t-1} = \textbf{w}'x_{t-1}$ $\epsilon_{t} = y_t - \hat{y}...
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Choice of time-series model for store sales prediction

I have a data set of weekly sales for a range of stores (all belonging to one company). I am trying to predict weekly/monthly use of several ingredients in the individual stores. The choice for what ...
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Is Box-Jenkins approach to time-series prediction and forecasting similar to Unobserved Components models approach?

How I understand the Box-Jenkins Method in a nut-shell is that a time-series model has signals that can be identified by weighting its own past lagged values, or weighting its owned past errors or ...
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How to solve or choose the smoothing parameter phi in solving for the Nonlinear trend exponential smoothing?

In forecasting, to solving for the nonlinear trend exponential smoothing can you just choose any value of ϕ or is there a way to solve for it?
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Will ARIMAX or exponential smoothing forecast a short time series better?

The objective requires to predict GROSS NPA for 6 months and provided with 2 years of data i.e., around 24 observations. So, which of the method will provide better forecast?
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Time series Exponential smoothing by Holt winters method

I have basic questions with respect to exponential smoothing techniques, from statsmodels.tsa.holtwinters import ExponentialSmoothing add_model = ExponentialSmoothing(traindata,seasonal_periods=12 ,...
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Forecasting method for different cohorts with large seasonal swings but otherwise stable data

I am attempting to forecast percentage of churn for different cohorts. However, I am unsure how to proceed after selecting an initial method. The churn is fairly stable except for large seasonal ...
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Anomaly detection using exponential weighted moving average

I would like to detect anomaly using exponential weighted moving average. I don't have series of data points. All I have is EMA(t-1) and the data point of the current time(t) DP(t). From these data, ...
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How to align two seasonal time series

I am trying to decompose a time series using Holt Winters method and use it for forecast. I am trying to do this for weekly data of last 25-26 months. The challenge is that the dates of the seasonal ...
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Is it always required to achieve stationarity before performing any time-series analysis?

For example, I know that for ARIMA models stationarity needs to be achieved. What about Exponential Smoothing? Is it also required?
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Can simple exponential forecasting be used for a non stationary series?

I have a non stationary series with trend and seasonal components. I want to use simple exponential smoothing ONLY for forecasting. Does the series need to converted to stationary before using SES? If ...
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Does smoothed data work better for time series forecasting with LSTMs?

I am training a 3-layer LSTM on time series data ($10^6$ training samples) to predict the next point in the time series, where there is no seasonality and the time series has been made stationary (...
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simple exponential smoothing - Ljung-Box test - residual

While reading this page on time series I found this sentence: The Ljung-Box test showed that there is little evidence of non-zero autocorrelations in the in-sample forecast errors, and the ...
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time series forecasting - predicting the next 24 hours

I have much the same problem as predict-the-next-24-hours, I have several years of hourly data of demand, and I would like to predict the next 24 hours. Ignoring the multi-seasonality issues - is it ...
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why is the level equation in the holt winters triple exponential model different from the other two?

the double exponential model is so simple: level: $s_t = \alpha x_t + (1-\alpha)(s_{t-1}+b_{t-1})$ trend: $b_t = \beta (s_t - s_{t-1}) + (1-\beta)b_{t-1}$ both intuitively weigh the new information ...
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How to select between Holt Winters Model and ARIMA

I need to do sales forecasting.My historical data shows stationary pattern & present of trend,Seasonality & cyclic pattern. I would like to check with you that how to select between Holt ...
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Incorporate recent drop in number of units sold in a forecast using exponential smoothing

I'm trying to generate a one-year forecast for the number of units sold by a retail company. I'm using monthly data from 2017 and 2018. The forecast is for 2019, and I'm using the data from the months ...
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R time-series forecasting with neural network, auto.arima and ets

I've heard a bit about using neural networks to forecast time series. How can I compare, which method for forecasting my time-series (daily retail data) is better: auto.arima(x), ets(x) or nnetar(x)....
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What are the differences between two different EWMA estimator?

Someone just showed me a different way of recursively estimating EWMA based on the exponential sum. The estimator has two different recursions: one for the sum and another for the weight. $$ \alpha=e^...
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Statistical demand forecasting

How is batch demand forecasting done in retail like in Walmart where number of products to forecast are very large in number and products are short lived i.e have less than 36 months of historical ...
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How to handle multiple periods in data when using Triple Exponential Smoothing (Holt-Winters method)?

Let's say I've got the the following time series (duration = 2.5 years) grouped by hour: ...
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Forecast daily data with weekly and monthly seasonality using exponential smoothing

I have to forecast data that exhibits dual seasonality. For example, the first day of the week can show seasonality and also the first week of the month can show seasonality. I am planning to use ...
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Dealing with extreme shocks like global recession in time series

I'm following Rob Hyndman's forecasting otext to practice on some financial data for fun and I am having difficulties in trying to properly deal with large shocks similar to the 2008 recession. My ...
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Hyperparameter-free method for Moving Average/ Exponential smoothing?

I want to find hyperparameter-free method for Moving Average/ Exponential smoothing. Is there any related paper or python code? S(t)= alpha * F(t) + (1-alpha) * S(t-1) Any methods can avoid the ...
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Question about the weighting factor of Exponential Weighted Moving Average (EWMA/EMA)

Hiii, I have one question about the weighting factor of EMA. As I learned, Exponential Weighted Moving Average has a weighting factor, Lamda, and its formula is: S(t) = Lamda * Y(t) + (1-Lamda) * S(...
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R - Holt-Winters - irregular frequency

I originally posted this on Stack Overflow, and it was suggested that this question would be better suited for CV: With reference to the HoltWinters function in R, how does one deal with time ...
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How do I compare time series forecast models? (ARIMA vs HoltWinter)

I'm working on a toy problem to try and get a better understanding for time series forecasting. I have a sample data set, which I'll include, that shows daily e-commerce sales from 2015 through Feb, ...
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Closest ARIMA models to Holt-Winter's Mixed Model and Time Series Decomposition Models

Can you please tell which ARIMA model will be closest to Holt-Winter's mixed model and Time Series Decomposition (additive/multiplicative) models And that ARIMA model maybe used in replacement of the ...
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How to get a forecast equation for $\hat{y}$ using ETS state space model

The ets(AAA) state space model (Rob Hyndman's handbook) is as below State equation is \begin{equation} Y_t = L_{t-1} + b_{t-1} + S_{t - m} + \varepsilon_t \end{equation} The measurement equations ...
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Search for optimal alpha in EWMA

All literature about finding the best alpha for a EWMA points to use RMSE to measure the fit between the EWMA and the signal. As alpha increases, the series get less and less smoothed out, and as a ...
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What is the best calculation method to account for individual change, volatility, observation windows and time decays in time series data? ARIMA, ETS?

I am looking at applying a theoretical best calculation method to some particular time series (ts) data. Ideally the calculation method would encompass relative change in individual ts, volatility of ...
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Prediction intervals exponential smoothing statsmodels

I've been reading through Forecasting: Principles and Practice. I am working through the exponential smoothing section attempting to model my own data with python instead of R. I am confused about how ...
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What do the “coefficients” in R's HoltWinters function represent?

I'm using the HoltWinters function in R and I'm trying to understand what the "coefficients" represent in the object that is returned by that function. They don't seem to match in any obvious way the ...
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Positive smoothing with the fda-package (Functional data analysis)

In the book Functional Data Analysis with R (Ramsay&Silverman) there is described the possibility to do the "positive smoothing" if it’s needed instead of the "normal smoothing". In the books ...
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How to forecast individual customer's spend (for millions of customers)?

Which forecasting model fits better to forecast the customers spend in the next upcoming visit? We have millions of customers, so ARIMA or any other time series modeling for each of the customers is ...
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When we are proving why ARIMA(0,1,1) is equal to simple exponential smoothing, why can we considered theta to be equal to (1-alpha)

I know this is a very basic question, but its not clarified within my lectures. Essentially when you have ARIMA(0,1,1) You can simplify the theta 1 term in order to obtain SES via stating its (1-...
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Historical average with exponential smoothing model [duplicate]

This topic similar with this one R Time Series Analysis forecast result always remains same But I perfrom exponential smoothing model in R. ...
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114 views

Understanding Intuition for ETS Damping Selection via AIC/BIC

I'm trying to understand how ETS selects whether to use a damped model via information criteria (I'm not sure which of AIC, AICc or BIC are used). I have a time series and I'm comparing two ETS ...
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470 views

Negative Forecast using Holt-Winters

I tried to use Holt-Winters for forecasting, but it gives me negative values, but since these are demand quantities they cannot be negative. ...
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Intuition about Exponential Smoothing parameters?

If I use Triple Exponential Smoothing with Additive Seasonality and let a statistical program optimize alpha, beta and gamma for me, is there something I can conclude about my data based on the ...
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Robust alternative to exponential smoothing?

Despite being easy to calculate and understand, exponential smoothing is excessively affected by outliers and thus performs poorly when the data has a non-Gaussian probability distribution, such as a ...
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Can I say Holt-Winters Method is an example of interpolation?

I believe it fits under the definition from wiki: In the mathematical field of numerical analysis, interpolation is a method of constructing new data points within the range of a discrete set of ...
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Use Holt-Winters or ARIMA?

My question is around the conceptual difference between Holt-Winters and ARIMA. As far as I understand, Holt-Winters is a special case of ARIMA. But when is one algorithm preferred over the other? ...
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How to derive the variance of a weighted moving average?

I have a problem understanding a piece of a paper. Greatly appreciate any hint or help. It says: A sensor records $Z(i)$ at intervals of 1 second and calculates background values $U(i)$ using ...
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ETS() function, how to avoid forecast not in line with historical data?

I am working on an alogorithm in R to automatize a monthly forecast calculation. I am using, among others, the ets() function from the forecast package to calculate forecast. It is working very well. ...