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Prediction of the future events. It is a special case of [prediction], in the context of [time-series].

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Measuring the effect of an outside force on time series with trend and seasonality

Let's say I have a time series that shows daily traffic to my website. My website is getting more popular so there's a trend up, and the traffic is based on day of week so it's cyclical with a period ...
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8 views

level shift outlier model

Does somebody happen to know how to calculate the forecast with the LS formula since it got denominator? I got confused because of that. Here's the model I've been using for the forecast.
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1answer
13 views

When i run auto.arima I get different values for the parameter “d” and ndiffs using x regressors

Im new using r. Im performing a kpss test on my "y" variable and running ndiffs procedure, and in both cases get 1 for the parameter "d" , but when I run auto.arima with x regressors I get 0 for the ...
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14 views

Production Curves for Agriculture

The idea here is to fit a curve to production data in agriculture. By a production curve I mean for example the output of a mine over time, peak oil production or the yield of a farm over time within ...
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1answer
26 views

Difference between forecasting model, forecasting method and forecasting function

I am new to the field of Business Analytics. Can anyone distinguish between forecasting model, forecasting method and forecasting function? According to the book forecasting function is is an equation ...
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0answers
6 views

Assess expected performance on trials with different parameters?

I work at an online retailer, and we sell our products by sending out push notifications to specific places about specific products. A particular notification is called an offer. I'm trying to assess ...
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0answers
6 views

Smoothing Predictions from HMM

I would say I do not have a strong foundation on stats, however, I am trying to use statistical tools for my research. I am using a hidden Markov model (HMM) to forecast day-ahead (hourly) solar ...
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0answers
22 views

Using multinomial logistic regression to make predictions [on hold]

I have run a multinomial logistic model in SAS with 5 independant variables and I need to use the results from this model to make forecasts of use of care. I have used the predicted probabilities from ...
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71 views

Is there a theoretical reason why simple models perform better than complex models on time series forecasting tasks?

Empirically, simple forecasting methods such as damped trend exponential smoothing, STL, or even random walks typically outperform more complex models such as higher order ARIMA models or ML based ...
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18 views

Differencing causes negative predictions

I have a non - stationary time series sequence which is based on counts. To convert the sequence into stationary I applied differencing, which converted the sequence into stationary but the sequence ...
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0answers
23 views

Panel Data and Prediction/Forecasting

I have balanced panel data for around 130 countries, over three years. I ran a fixed effects regression using 'country' as my panel variable, and adding dummies for 'year'. I want to forecast the ...
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1answer
26 views

How to you measure the accuracy of a model that gives quantile forecasts or distributions of forecasts?

I've come across some recent demand forecasting approaches that present methods where instead of generating just a point forecast, the model outputs a set of forecast quantiles, or a distribution of ...
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1answer
147 views

Need advice on evaluating forecast accuracy in R

I'm trying to evaluate some software for forecast accuracy. It works by summing up all the orders from a number of locations for each month, then determines the best model out of a series of models ...
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3answers
807 views

Over fitting on purpose

Would it make sense to overfit a model on purpose? Say I have a use case where I know the data will not vary much respect to the training data. I'm thinking here about traffic prediction, where the ...
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0answers
26 views

Choosing a model

I am working on a sales forecast right now and I have created 4 models but I am unsure which one to use. I have 17 Quarters of data(4 Full years + 1 QTR) and I am only looking to forecast 2 quarters ...
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2answers
28 views

Measuring error from regression results, is MSE appropriate here?

I used boosted regression trees on a dataset I was working on to predict how much a customer will spend in a given year. Here is a sample of the output: ...
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2answers
43 views

Modeling Techniques for Sales Forecasting

I am working on a sales forecast right now and I am not sure what type of model to use. I have 17 Quarters of data(4 Full years + 1 QTR) and I am only looking to forecast 2 quarters into the future ...
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0answers
21 views

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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0answers
27 views

Interpreting forecast predictions of log transformed data

Using the forecast function in R, I make a 1-step prediction for a log-transformed data set Y, ( Y = log(X) ). This prediction gives me a mean and a 95% prediction interval. How valid is this ...
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50 views

Testing the validity of models for adjusting bookmaker odds into probabilities of real-world events

I'm looking at the use of bookmaker odds to predict the outcome of sporting events in which only two results are possible. A problem with using bookmaker odds to predict outcomes is that they include ...
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0answers
22 views

How to include outside temperature in visitor prediction model

I am developing a prediction model for visitors of a building. Outside temperature impacts the number of visitors - but i don't know how to include it as a regressors in the model. This is an example ...
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1answer
13 views

Difference between regression tree and autoregressive tree model

I am using autoregressive tree model for forecasting but m confused between regression tree and autoregressive tree model. Are these same
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1answer
39 views

Time Series Cross Validation in R

I am trying to do time series cross validation in R using tsCV() function from the forecast package. I have a doubt regarding the forecast horizon parameter "h" in this function. If the value of h is >...
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0answers
23 views

Time series analysis where computing the exact value is possible (but expensive)

I have a stationary time series where it is actually possible for me to compute the exact next value. These computations are very expensive, and to speed things up I want to employ following scheme: ...
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1answer
36 views

My neural network will run okay, but occasionally (every 1000) it provides an error. [closed]

I am using a neural network to forecast the direction of gold prices. I have created a neural network neuralnet within R. My programme runs well and i can get a prediction accuracy of about 51%. ...
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0answers
26 views

Multivariant time series forcasting

I have a multivariate time series data with Timestamps as( 'season' with four categories: 1,2,3,4, 'month' with 12 categories and 'day' with 5 categories excluding Saturday and Sunday) and I have to ...
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0answers
17 views

Switching attention - is there a statistical tool to mimic perceptive response?

I was driving this morning to work. Another car made sudden lane change. I noticed an interesting thing: for a brief moment my attention focused in this car. It almost physically felt that everything ...
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1answer
30 views

Ljung-Box test on a out of sample residue with good forecast

To test the fbprophet library, I created a very simple synthetic series and generated a model like this: ...
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0answers
24 views

Johansen test R - long term relationship covariance

I have two time series which are I(1) and co-integrated. I would like to make long term forecasts for one of the time series, given an assumed fixed value for the other. I used the Johansen test (ca....
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1answer
26 views

Macroeconomic effects. Effects of a time serie on another

I have a monthly time series for the provision in a financial institution. Take real data until december 2017 and predict it with a Bat model until June 2018 using R and I have an error of 0.12%. This ...
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1answer
42 views

Forecasting new period with Regression Model

Have a dataset which consists of item names (rows) and monthly values of sales (columns). My task is to predict value of sales for next month and I'm trying to use regression models for that. But the ...
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0answers
21 views

logistic regressions per item

I have sales performance data with boolean outcome of true or false that i want to analyse the main influence on outcome. ...
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0answers
24 views

How to refine a logistics population model? [closed]

I have to refine a logistic population model so that it more accurately fits a set of data and was wondering how to do this. The only way I can find is to use a variable carrying capacity however I ...
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0answers
79 views

AR(p) forecast is almost always above actuals in pseudo out-of-sample forecasting

I have a trouble with my out-of-sample forecasts. The task is to evaluate the out-of-sample performance in forecasting the CPI(price index). In order to do this, I have estimated simple AR(1) with ...
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2answers
49 views

Is there a way to add external regressors for exponential smoothing based models?

I'm trying to forecast a daily time series while taking into consideration the effect of weekends and holidays using different methods in R. For arima() it was ...
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0answers
22 views

Sales Forecasting

I understand the sales of the product I am trying to predict is a function of the need for the product and its price. Should I model these separately and create a strategy matrix with segments like ...
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0answers
25 views

Why is WAPE always lower than MAPE [closed]

I'm running a time series forecast using ARIMA. I am using the following calculations for MAPE and WAPE. I expected to get a higher WAPE than MAPE because I'm not removing the zero actuals from my ...
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38 views

sarima model with additional regressors

I have fitted a SARIMA model for daily data with 5 regressor variables. In addition, I used Fourier terms to capture the seasonal patterns in the model according to the prof. Hyndman post on daily ...
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1answer
49 views

Can I apply ARIMA(p, d, q) model to testing dataset and make forecast with the testing dataset? Just like the scenario of regression model?

After I fit a sarima model with some historical sales data (for example A dataset), I get coefficients of sma1 and ar1. And I'd like to apply this model to current sales data (for example B dataset) ...
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2answers
42 views

Forecast sales and then ungroupto find individual sales

I hope you're doing well. I am trying to solve a problem for a brewery. A brewery has 50 beer types in total out of which only 8 to 10 beers are available on tap for a single day i.e only 8 to 10 ...
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2answers
30 views

Forecasting sales: different methods

It's a bit untypical question I guess, but I hope you can help me. I know a little bit of statistics. I'm not a specialist, but I find it really interesting, so I learn in my free time. I need your ...
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1answer
58 views

What is a correct implementation of the moving average model

I would like to implement a moving average model in python as when I try to use the statsmodels library, specifically the ARMA(p,q) function and setting $p=0$ I get a lot of convergence errors in the ...
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0answers
36 views

How to test if time series data is random

I have many time series data sequences and I would like to produce forecasts for their behaviour in the future. In order to do this I am looking into several methods, but the underlying concept is ...
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0answers
32 views

How to interpret the Ljung-Box test output?

I am trying to forecast the univariate time series using two different neural networks. After training and testing, I have residuals (Actual - Prediction) with me for both networks say ...
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1answer
76 views

What more advanced methods can I use to predict future sales other than polynomial fitting?

I am using the polynomial fitting method to forecast the sales of a product throughout different years, where the polynomial is of degree 1. The error is measured by the sum of the squared residuals. ...
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2answers
79 views

How to forecast daily time series with weekends and holidays?

I'm having troubles choosing which approach to adopt when trying to forecast daily time series while taking into consideration special days like weekends and national holidays. The two methods I'm ...
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1answer
47 views

What does that mean if neural network never overfits on a dataset?

I'm trying to predict a parameter with use of neural networks. The parameter is highly linearly correlated with some of its predictors (Pearson's r~0.9)(However, the influence of time may not be ...
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0answers
40 views

Are Bias-Variance trade-off and Mean Squared Prediction Error (MSPE) the same?

I was reading about the Bias-Variance trade-off in the textbook "Elements of statistical learning". Is the expected forecast error listed there the same as the MSPE?
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1answer
23 views

Can I roll-up forecasts to analyze against benchmark methods?

There's a software product (call it Main) we use to allocate parts to various locations. The way it works is it sums up the demand history of all locations to the part level. For example, if part A ...