Forecasting involves estimating the value or distribution of a random variable which has not yet been observed.

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Prediction intervals for forecasts using spectral analysis

I have circadian data which typically have a period of around 24 hours so using spectral analysis seems appropriate. I've used spectrum resampling which is quite robust to changes in period which ...
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18 views

Best way to deal with forecasting with noisy data?

I have a bunch of sales data. It is from distributors of 2000 different items, who service big companies and large distributors to a number of small independent stores. They sell some items which do ...
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1answer
18 views

Demand bottom-up forecasting and substitution effect

If retailer has many products the is likely to be a substitution effect within product groups (clusters). Hence, there is a notion of the "unit of demand" that is supposed to gather products based ...
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16 views

Forecasting demand with out-of-stock data

Usually retailers have a service level that is below 1.0, which means that share of products is out-of-stock some of the time. What is the best practice or possible ways of using out-of-stock data to ...
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1answer
34 views

Density forecasting

I am having some troubles obtaining density forecasts of any returns series. As I couldn't find any numerical examples on the Internet, I would like to ask you guys for some help. My goal is to ...
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15 views

Calculating the optimal Holt Winters parameters (not in R)?

The Holt Winters (HW) technique requires the following parameters: Alpha, Beta and Gamma. The accuracy of the forecasts depends on these parameters. Some software packages (like in R) are able to find ...
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14 views

How do I Forecast new Yts given new Xt's using a Dynamic Linear Model?

I am trying to forecast predict new observations of interest rates given new data using the DLM modeling framework. Essentially, my problem is this: I have a training set (a set of data i want to ...
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1answer
42 views

Does ARIMA require normally distributed data? [duplicate]

I want forecast inflation using ARIMA model. My questions are: Does ARIMA require normally distributed input data? (Because my data—inflation—is not normal.) If ARIMA require normally ...
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23 views

What are some tests for the predictability of time-series?

I have 2500 time series which I want to test the predictability and based on that, choose the best one to forecast. Ideally I want to use a simple model like ARMA-GARCH for forecasting. Are there ...
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1answer
24 views

Multi-step ahead forecasting with Weighted Moving Average?

The Weighted Moving Average method is usually used for smoothing purposes. However, it can be used to forecast $Y(t+1)$ based on the last n observed data. In real-world problems, forecasting in very ...
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18 views

auto.arima and DLM give different values for loglikelihood

I want to estimate an ARIMA model on my timeseries, then represent it in state space format, mainly because it will be more responsive to change in pattern. I used ...
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14 views

Nonlinear forecasting methods [closed]

Can anyone recommend a nonlinear forecasting method for time-series performance data? It doesn't depend on seasonality so Holt-Winters isn't appropriate. Edit: the data is arrears percentages for the ...
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28 views

Why no MAX models?

I'm diving into the field of system identification, black box modeling and forecasting. A lot still has to become clear to me, but one question that came to my mind (and to which the answer might ...
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2answers
16 views

Forecasting methods for monthly sales

I have to make predictions about sales on a monthly base and I already have historical data from January 2011 until June 2015. What forecasting method should I use if my data is influenced by ...
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1answer
88 views

Why can't my (auto.)arima-model forecast my time series?

For testing I generated a very simple time series with a clear recurring pattern. I expected that auto.arima will generate a model, that can forecast that pattern, but óbviously it doesn't. Can anyone ...
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6 views

Forecasting using polynomial function of R and how to validate with available methods such as ETS

Trying to build a forecasting function to model a polynomial function to fit the time series data and to use the same to predict the future periods and i have used the concepts of lag as the predictor ...
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1answer
23 views

Nonlinear forecasting

I'm working with time series data (which fluctuates constantly) and currently have 27 data points to forecast with. Would anyone be able to recommend a nonlinear forecasting method using formulas to ...
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1answer
55 views

Why can't we use top-down methods in forecasting grouped time series?

As I asked in here I was trying to forecast grouped time series with two grouping variables and I find some limitation of hierarchical forecasting methods. In particular, using hts package from R, we ...
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29 views

Constant in arima model whether to include or exclude?

I have a very basic question on including constant in Arima models. I'll illustrate this by an example. I have the following ACF and PACF of a weekly time series that is differenced at lag 1 (trend) ...
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2answers
68 views

How do I forecast weekly sales using historical numbers? [on hold]

I want to predict the weekly sales forecast using 2 years of historical numbers using the history of weekly sales. Could you let me know what the preferred statistical approach is and what software ...
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1answer
31 views

Work with results of tbats decomposition

I made a time series decomposition with tbats. There is weekly and yearly seasonality in the data (and maybe also monthly - not really important for the question) ...
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24 views

Online time series forecasting with DLM

I have estimated a univariate time series model, consisting of a random walk and an AR component. Now the goal is to make forecast about a couple of steps ahead as new data comes in, in an online ...
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10 views

How to forecast how many passenger will be coming or be lost if a new departure time added or a current one removed?

I want to forecast how many passenger will be coming or be lost if a new departure time added or removed from time table based on historical data? I have historical data from 2010 to 2015 on how many ...
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23 views

Best forecast method for my data

I have a large amount of statistical data on tennis matches over the last 10 years and want to be able to forecast the percentage of points a server will win on his own serve based on past data. For ...
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1answer
35 views

Benchmarking time series forecasting model

Problem: I'm building a time series forecasting model for daily data wherein, the aim is to forecast for the next one week. So, to validate the model, I'm using a moving window based validation ...
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17 views

DLM, regression and multiple time series

I'm working with incoming traffic data at multiple spots along a long road. Let's denote the traffic at time t at point $j$ by $x_j(t)$. For each spot, a univariate model, such as local level plus ...
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2answers
65 views

Assigning Weights to An Averaged Forecast

So I've been learning how to forecast over this summer and I've been using Rob Hyndman's book Forecasting: principles and practice. I've been using R, but my questions aren't about code. For the ...
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1answer
71 views

Do we have to model spurious auto-correlation in time series?

I am analyzing a data set of power consumption with the aim of forecasting. The times when there is consumption are rather sparse. If there is consumption then there is likely one in the next time ...
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25 views

Granger Causality and Regression

I have an enquiry regarding the Granger Causality analysis. It is said that it is performed to check whether “X causes Y”, or to put it differently, whether X contains any predictive information with ...
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49 views

Train neural network for forecasting

I am trying to use time series neural network to predict future values. I have time series data from 2010-2014 and I need to predict the values from 2015-2020 using time series neural network. I am ...
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1answer
38 views

ARIMA modeling white noise probabilities vs. residual autocorrelation/PACF

I have moderate understanding of statistics and time series analysis. I trying to forecast a weekly time series with lots of outliers and trend shifts. After correcting all of the outliers, I'm left ...
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13 views

Time series forecasting using ANN

I have an array of data recorded from vibration analysis of a bearing.I want to know how to forecast 30 day later. I don't know machine learning and I'm not so familiar with neural network for example ...
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8 views

The relationship between the intercept in logit and model power?

Say I have 12 regressions, each is forecasting a future event further out in the future. Regression 1 is 1 period forward and regression 12 is twelve periods forward. I've noticed that the power of my ...
2
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1answer
123 views

Understand order of time series

I am trying to build a time series model. I looked at the ACF/PACF and adf test of the series and thought that an ARMA(p,q) model will be suitable for the data. However when I run auto.arima(), it's ...
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1answer
56 views

Why is forecasting of ARMA models performed by Kalman filter

What are the advantages of expressing an ARMA model as a state-space-model and do forecasting using a Kalman filter? This methodology is for example used in the SARIMAX implementation of ...
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89 views

Determining order of ARIMA model using Box-Jenkins. Correct approach / argumentation?

I obtained a couple of time series from estimating my (mortality-)model which I now aim to forecast with an appropriate ARIMA(p,d,q) model, which should be chosen with the use of the Box-Jenkins ...
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12 views

Forecast top down hierarchy proportions

In a hierarchy time serie, I would do like to forecast the proportion of my sub levels. Do I have to use a time series also in thiscase ? Put in other words, I have a hierarchy composed by ...
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36 views

Forecasting time series with lagged variables and machine learning

I want to forecast a time series based on the lagged variables of the model and train it using a machine learning algorithm like Random Forest, SVM, Neronal Network, etc. So I want to forecast A ...
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forecasting imputed data

My data set consists of a 15 year time series of monthly water quality measurements (10 different measurements). The data set has ~30% missingness. I applied multiple imputation using the Amelia II ...
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34 views

Forecasting daily subscriptions: which method should I use?

I am interested in predicting the data for a day, based on the data given from the 14 previous days. The data I am working with is the number of subscriptions to a website per day. Each day, the ...
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2answers
61 views

How to deal with a single Yearly spike with ARIMA?

I have a time series which shows an yearly spike around summer but otherwise is predictable by an AR(1) model. The tests on the data also show that the time series shows stationarity and is ...
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62 views

Difference between Time delayed neural networks and Recurrent neural networks

I would like to use a Neural Network to predict financial time series. I come from an IT background and have some knowledge of Neural Networks and I have been reading about these: TDNN RNN I have ...
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14 views

Hierarchy predictive top down approach

I'm having a problem with using a hierarchical top down forecasting approach. According to my understanding, when I split an aggregated value on the levels below it, I have to know the percentages ...
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1answer
21 views

Cross-validating the tbats/bats function in forecast

Is there a way to cross validate the tbats/bats function in the forecast package in R? I have been trying to get CV weighted parameters which then I can pass to a function for revised estimates. ...
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1answer
43 views

Forecasting: Turn a basic formula to an ARIMA model

What ARIMA model best represents a formula like this one. $$R_{T_i}=\frac{R_{T_{i-12}}+R_{T_{i-24}}}{2}\times{TREND}$$ I thought that an ...
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1answer
26 views

Forecasting with no seasonality

I have a set of data, let's say average weight of employees, captured every month over a period of 5 years (2010 - 2014). I cannot find a seasonality trend in the data over these years. Also, I have ...
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26 views

Eviews and Forecasting Linear Regression with AR(1) Error Term

This question is geared towards those who are familiar with Eviews and forecasting with linear regression in the case of AR(1) error terms. Consider the classical linear regression model where the ...
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45 views

Is there a name for this fallacy?

I sometimes encounter a view that only perfect forecasting is really forecasting. For example, if I claim that I have a model which forecasts election results, people will think I'm making the ...
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36 views

Seasonality in residuals [closed]

I'm running a simple OLS with two seasonally adjusted independent variables and the dependent variable is also seasonally adjusted. I'm seeing distinct seasonality in the residuals of the estimation. ...
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1answer
62 views

Correct procedures to detect and correct outliers for aggregated/SKU time series

Background I am currently working with sets of product sales time series at SKU-level for a FMCG company. Data are available in a weekly format for multiple years and sales data for hundreds of ...