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

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prediction for data including weekly and annually seasonality and dummy variables for holidays

I have a three years of daily data for number of orders a trucking company receives everyday. Number of orders are high during weekdays and they have a huge decrease for weekend. I used msts to ...
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1answer
16 views

prediction using historic data with unusual annual trends

I have 4 years of daily data. there is a decreasing trend for the data for the first 3 years but the trend increase for the 4th year. I wanted to find a fitted model using the first 3 years and then ...
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19 views

wrong predictions for weekend but good predictions for weekdays

I have a set of 3 years of daily data. I saw weekly and annual seasonality in the data so I used msts time series and tbats ...
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27 views

Time series forecasting accuracy measures: MAPE and MASE

We come to this toy example showing MAPE and MASE are not consistent when measuring forecasting accuracy. Data consist of 100 white noise and 100 $AR(1)$ time series with length $N=500$, mean $\mu=1$ ...
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28 views

Stock Closing price forecasting using ARIMA Model in R ( Entry level R programmer and Statistics learner)

I am an entry level R programmer and trying to learn statistics. i have downloaded the daily stock Adjusted Close price of one stock from sep 2011 to till date. As per my study plan, i have plotted ...
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12 views

TBATS missing value error

I used tbats to find the best fit model to a 3 years of daily data. It couldnt find a model and showed the following error: " Missing values encountered. Using longest contiguous portion of time ...
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1answer
24 views

How to compute RMSE for TBATS

Some forecasting models in R give error terms as their output. But for TBATS, I couldnt find out that how I can see what the RMSE for my data set is. Is there any specific command that I have to use ...
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1answer
22 views

Data with weekly and annually seasonality but the first day in time series is not the begining of a week

I know it might look naive but I have a very basic question. I have a three years of historic data which has weekly and annual seasonality. January first as my first data is on Wednesday so my time ...
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17 views

Testing the accuracy of transformed data

I have run my data through a model in r, i ran ARIMA to forecast. The model forces a log transformation to be applied to the data. To test the accuracy of the fitted model formed by ARIMA would i need ...
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1answer
42 views

Difference between the forecast and simulate functions in the {forecast} package in R

I have been using the forecast package in R to make forecasts based on an ARIMA model and have noticed a difference in the output of the forecast and simulate functions when calculating confidence ...
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69 views

How to best make millions of forecasts using time series data?

I need to make roughly 50 million forecasts every night. The data is daily, hierarchical (~50 million base series), intermittent/sparse (for many of the time series, lots of days have 0's), and not ...
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1answer
36 views

No fitted ARIMA model

I wanted to fit an ARIMA model to a daily database for three years but auto.arima couldn't find a model and showed the following error: ...
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1answer
69 views

Forecasting a seasonal time series in R

Forecasting airline passengers seasonal time series using auto arima Hi, I am trying to model some airline data in an attempt to provide an accurate monthly forecast for June-December this year using ...
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12 views

what is the best prediction interval for a forecasting model with daily and annually seasonalitis?

If we have a data set which has daily and annually seasonality, is it reasonable to use the forecasting model for one year ahead? I mean, I want to have a 48 hours forecast for a logistic provider ...
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27 views

Creating auto arima for two following time series with two different non linear slopes

I'm trying to model (and predict) the following time series, which consist of two periods (enrollment period and non enrollment) as the following: I believe that this model should consist of two ...
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19 views

What does a fitted value mean in dshw forecasting package?

I have a double seasonal data. I wrote the following code to find the best fit model and find fitted values: orders <- read.csv("DataForR.csv", header = TRUE), NumOrders <- orders$Orders, ...
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1answer
36 views

Transforming a time series with a negative number

I have been given data to forecast however it has a negative figure within the data which then, when doing a log transformation to make the series stationary, the ARIMA script i have written won't ...
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26 views

ARIMAX model or ARDL?

I would like to study the impact the advertising of a product on its sales (weekly data for 5 years). As the final aim is to forecast what would be the impact on sales of a change in the advertising ...
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13 views

Wrong prediction for data with weekly and annually cycles

I have one year daily data which has weekly and annually seasonality. There is a problem with seasonality definition. If my weekly seasonality has 7 cycles (one for each day) and annual seasonality ...
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17 views

Different fitting models using auto arima and tbats

I have one year of daily data for forecasting. while using auto.arima to find the best fit model, it gives me ARIMA(3,1,3). However, when I used tbats to find the best fit model, it gave me the ...
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13 views

How often to update a forecasting model

If I have a data set with daily, weekly and annually seasonality, how often should I update my forecasting model? As I have heard, forecasting models out there can have a good prediction for up to a ...
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1answer
19 views

Statistical softwares providing multiple seasonality forecasting

Does anybody know which software provide forecasting with more than two seasonalities? I know R includes tools for double seasonal holt winter forecasting but I am looking for a software which can ...
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10 views

Question of Holt-Winters, parameter chosen

I am using Holt-Winters to do a time-series forecasting. The package chose gamma equal to 1 for me. I am wondering what that means. The prediction works pretty well overall. When will you use this ...
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12 views

Double Seasonal Holt winter method using dshw

I have a two weeks data set which have intraday and intraweek cycles so I decided to use dshw in r. Although it gave me a pretty good MAE and RMSE, when I wanted to see SSE, it showed me a null value. ...
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18 views

Panel data forecasting from Arellano-Bond GMM estimation

I want to come up with predictions of final energy demand per capita (fe) for a panel of countries. Explanatory variables are GDP per capita (gdp) and population density (pop) -- all variables are ...
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36 views

How to compare forecasting methods?

I have several intermittent data. Based on those data, I would like to compare several forecasting methods (Exponential Smoothing, Moving Average, Croston, and Syntetos-Boylan), and decide whether ...
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30 views

prediction of polls

Just as an example Scotland has poll to decide whether they need to be independent from UK or not. Here is BBC's summary of different polls: ...
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190 views

Forecast accuracy calculation

We are using STL (R implementation) for forecasting time series data. Every day we run daily forecasts. We would like to compare forecast values with real values and identify average deviation. For ...
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30 views

standard errors of the fitted values of a time series regression

I really want to understand how the math is working here. I am trying to get the standard error of the fitted values for a time series regression model.In the non-time series regression,I know I can ...
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1answer
55 views

Forecasting at individual versus grouped level

I have monthly usage data (spanning 3 years) for a customer base of around 200K, and I need to generate 1-month ahead forecasts for each of them. There are a couple of exogenous variables that would ...
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23 views

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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1answer
57 views

Under-forecasting in Regression

I have to do forecasting of sales that is how much sales of a product is going to happen in a particular store. I have time series data for last two years and doing forecasting for 2014. The variables ...
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16 views

Determining whether a dataset can be forecasted

Given a dataset $D$ representing past history of a certain variable, is there a metric $\alpha(D)$ which determines whether the dataset can be forecasted or not, i.e. $\alpha(D)$ tells us whether $D$ ...
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3answers
75 views

How to Use Neural Networks to Forecast Time Series Data with Predictor Variables?

I have browsed a lot of topics here, but the ones I see were all about forecasting a single variable, depending on its historical values. Whereas I want to predict a variable, by estimating a ...
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55 views

Transforming time series to compensate for change in variance

I have a time series (shown below) that comes from a sensor whose calibration was changed in the middle of last year. As part of this change, the sensor's reading of the variance (or volatility) of ...
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49 views

Four tricky time series questions with a “seasonal twist”

A ski-hotel has the most guests in the third quarter in every year (check the data below after the four questions). Can you answer these four questions (every year has 4 values, the first is quarter ...
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28 views

Reproducing ARIMA error terms

When forecasting a moving average (MA) model using R's forecast, why does using residuals(fit) produce different results than ...
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1answer
32 views

Difference between imputation and forecast

what is the difference between imputation and forecasting? All i know, forecasting is the term used in time series analysis, which means predicting the future value by considering the trend of the ...
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21 views

Time series Data Analysis and Forecasting by country and time factor

cty year qtr tl Argentina 2009 Q4 3 Argentina 2010 Q1 2 Argentina 2010 Q2 7 Argentina 2010 Q3 7 Argentina 2010 Q4 10 Argentina 2011 Q1 7 Argentina 2011 Q2 7 Argentina 2011 Q3 1 Argentina 2011 ...
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Ensemble model performs better with worse performing consitutent models?

I have a forecast model I am developing that uses some very unreliable input data, missing data (due to sensors or comms failures) is the rule, not an exception. The quantity being forecast is a daily ...
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21 views

Does this kind of overlap between in-sample data and forecast cause inflated $R^2$?

I am using a simple UIP model to forecast exchange rates using interest rates with a twelve month horizon. The equation I use is: $E(t+12) - E(t) = α + β(I*(t) - I(t))$. I apply OLS linear regression ...
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Why is $R^2$ poor for AR model selection used for forecasting?

There is a related question here, about how to calculate the R-squared on a regression with ARIMA errors. I found the answer quite useful, and hoped for some elaboration, particularly on Rob's closing ...
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package in R for BMA of a logistic model?

I am trying to perform analysis similar to Gerlach et al. (2002). it involves predicting the posterior probability of a particular binary outcome using the previous 5 observations. I was just ...
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42 views

Call Centre Models

Can anyone suggest me, which Mathematical(Statistical) methods can be used to predict incomingcalls by a given time interval at a call centre? Please cite any ...
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34 views

Understanding the Rank Probability Score

The ranked probability score (RPS) is a measure of how good forecasts that are expressed as probability distributions are in matching observed outcomes. Both the location and spread of the forecast ...
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15 views

Forecasting the business cycle?

I am wondering what is the best way to forecast the business cycle based on the past. Currently I feed the seasonally-adjusted GDP index data to a Hodrick–Prescott filter, extract the cyclical ...
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2answers
86 views

Which forecasting method for load profiles

I'm new to this forum and I'm quite new to forecasting. Currently I'm trying to learn the basics about exponential smoothing, ARIMA etc. Now I want to forecast the total energy consumption of a rather ...
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27 views

Techniques to forecast discrete events in a time series?

I'm currently looking at time series data for patients who have been admitted to a hospital. The time series itself models risk probabilities, where high risks are marked by peaks. At various points ...
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35 views

Prediction using Support Vector (SV) method in R

I came to know that using SVM method we can predict the future value more accurately than other normal methods (like ARIMA). My question is how do we give the future index value (let's say 101 when we ...
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What is the procedure to compare two different period time series

I am currently working on the task that I would like to compare two different period time series like Sales in 2012 vs Sales in 2013. Kindly suggest me any statistical procedure.