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

learn more… | top users | synonyms (1)

0
votes
3answers
31 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 ...
0
votes
1answer
28 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 ...
0
votes
0answers
44 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 ...
0
votes
0answers
21 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 ...
1
vote
1answer
27 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 ...
0
votes
0answers
12 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 ...
1
vote
0answers
17 views

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 ...
0
votes
0answers
20 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 ...
0
votes
0answers
30 views

What types of statistical analysis technique available to compare two different time series [closed]

I am currently looking for suggestion to compare or study the two different period time series like sales in 2000 and 2001. As it is sales of the same product and i would like to compare those two ...
0
votes
0answers
20 views

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 ...
0
votes
0answers
11 views

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 ...
1
vote
2answers
36 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 ...
3
votes
0answers
30 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 ...
0
votes
0answers
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 ...
2
votes
2answers
79 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 ...
0
votes
0answers
22 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 ...
0
votes
0answers
20 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 ...
0
votes
0answers
16 views

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.
2
votes
1answer
52 views

R: forecast function accuracy for ARIMA models

I have a problem with the forecast function for ARIMA models in R. It calls predict that calls ...
0
votes
0answers
13 views

Can I include future index into current forecasting in R models

I am new to R. Now my team is building forecasting models for monthly sales. Our sales correlates quite well with a industry index. As a forward index, we can get both past 5 years' index and next 3 ...
0
votes
1answer
41 views

What statistical method to correct systematic error in the output of a economic optimization model?

I am working with an economic optimization model which attempts to model the dynamics of a certain commodity market (prices, quantities, production etc.) for different frequencies (monthly, quarterly, ...
0
votes
0answers
13 views

Binary event probability optimization

I have a relatively small sample of binary events (50-100 events) that occurred during a time of day (the success rate is closely related to the time of day). I'm grouping these events into hour ...
0
votes
1answer
32 views

Alternative to forecast() and ets() in Python?

I'm looking for a Python alternative to R's ETS() from forecast(). It's my understanding that ETS() is one of the best performing forecasting program and I would like to use it. However I am ...
1
vote
1answer
20 views

Forecast error differences when using weekly vs monthly data

Why is forecast error (MAPE) much higher when using weekly periodicity vs monthly? I am using exactly the same data for both.
2
votes
1answer
41 views

Is positive coefficient of price correct in a multiple regression model

I am currently undertaking forecasting of energy sales (kWh) for our industrial customers. From historical data gathered from 1993 to 2013, a graph of price per kwh against sales kwh shows a positive ...
0
votes
0answers
19 views

Forecasting ar(p) for several counties

I have a data set of prices, these prices vary across time and across area. I have 18 areas with 32 time periods. What i want to do is forecast these prices, i have found that a AR(3) process fits ...
0
votes
0answers
19 views

Obtaining the Psi Weights of a seasonal ARIMA in R

I am trying to quantify the effect of a future random shocks on my seasonal ARIMA model. If I have understood the theory correctly, the easiest way is to express my seasonal ARIMA model in its "random ...
0
votes
1answer
64 views

Standard techniques for forecasting revenue growth of a company?

I was curious what sort of time series models were the standard for doing this type of analysis. I have weekly sales data for the company - I could cook up my own time series model but would like to ...
0
votes
0answers
16 views

training period selection forecast (error analysis)

I have been lately testing the best training period length to perform a forecast. I have tested it for various days of training period length, among them 60 days and 30 days. My methodology is quite ...
0
votes
0answers
21 views

Statistical Methods for Calculating Vending Machine Refill

Am looking into statics to help support a project I am undertaking. The project scope concerns intelligent replenishment / refill of vending machines. During an onsite service, a technician must ...
0
votes
0answers
33 views

How 'good' are Holt-Winters forecasts with unusual alpha, beta and gamma values?

I'm using this python script for Holt-Winters forecasting (https://gist.github.com/andrequeiroz/5888967) that I believe chooses values of alpha, gamma and beta via RMSE optimisation. Sometimes the ...
0
votes
0answers
45 views

Arima model - multi step forecast

The following code shows a forecast of the next 24 hours of my electricity prices with two exogenous variables. My problem is, that I don't know how to build a forecast for the next 3 days or more ...
0
votes
0answers
30 views

Forecasting time series with missing data and irregular intervals

I have a data set of medical drug stock levels at health centres and I want to forecast monthly consumption over the following 3-6 months. However about 30%-40% of the data is missing and some of the ...
0
votes
1answer
33 views

MAPE is high for daily sale prediction

I have daily sales data from 2011 to 2013. I have to do prediction for 2014.I have used arima and exponential method to predict the daily sale, but it is not giving the better result. MAPE is around ...
0
votes
1answer
46 views

Mcomp rolling forecasts with re-estimation

I'm looking to run rolling one-step ahead forecasts on the Mcomp holdout data (future data), with re-estimation at each point, i.e. re-estimation over the entire historical and already forecast ...
0
votes
0answers
40 views

Forecasting agricultural commodity prices with R

I would like to create a predictive model in order to forecast the price of an agricultural raw material. I got time series for the prices and the production of this raw material, and also for the ...
0
votes
0answers
26 views

Kalman Filter Correction efficiency

I was wondering if Kalman Filter used in a way to correct and reduce forecast errors is useful in real life forecast.Since we are using output forecast data and measurement data from t-1 to correct ...
1
vote
0answers
31 views

Prediction model on online game economy

I want to study the economy of an online game. In specific I want to examine if there is a possibility to create a prediction model. I would try to describe the whole concept and I am asking for ...
0
votes
1answer
42 views

Kalman filter transition matrix

Hi guys I am trying to writ e a code on python to correct forecast data using Kalman Filter. I am following the equations and recommendations in this link : ...
0
votes
1answer
62 views

How to forecast time-series bounded by [0,1], i.e., forecast relative frequencies?

I am working with time series values which are all in the closed interval [0, 1]; these values represent relative frequencies, i.e., empirical probabilities. I would like to create a model such that ...
1
vote
1answer
39 views

Combinef in R HTS package- constrain to keep forecasts positive?

When using the combinef function from Rob Hyndman's very useful hts package for forecasting hierarchical and grouped time series, there does not seem to be a way to constrain the optimally combined ...
3
votes
2answers
90 views

Avoid negative results in Holt Winters forecasting

I understood that Holt Winters forecasting may results in negative values due to trending. I did reduce trending component value, but still forecast values are negative territory. Our data set will ...
2
votes
3answers
74 views

Gaps in time series and time series validity

After doing some reading on CrossValidated, I understood that we can use "imputation" techniques to fill in the gaps (if they are random). But I am not clear on following questions: How many ...
3
votes
1answer
70 views

How do you create variables reflecting the lead and lag impact of holidays / calendar effects in a time-series analysis?

I am working on a time-series project in which I am forecasting the daily activity of something (let's call it 'Y') based on three years of historical data. I know that Y is affected by calendar ...
0
votes
0answers
19 views

kalman filter initialization parameter

Hello I don't have any idea of how to start implementing Kalman Filter in python! I have a DataFrame ( table) with in one column my forecast values and in another column my actual datas (real). The ...
6
votes
2answers
167 views

Faith in an extrapolated result

I would like to be able to predict when I will exhaust a particular resource. My situation is analogous* to a water tank. Each day zero or more rain will fall, filling up the tank. I can not tell ...
0
votes
0answers
21 views

linearity of a time series

I am currently trying to correct forecast data using Kalman filter (python). I do not know where to start. I wanted to know how can I do a test to Know if my time series is linear or non linear? Is ...
0
votes
1answer
86 views

How to dampen forecast to improve accuracy?

According to Armstrong there is ample empirical evidence that dampening trends in uncertain and complex long term forecasting helps improve accuracy/reduce forecasting errors. What I'm not able to ...
1
vote
1answer
90 views

What econometric model to forecast a seasonal commodity demand while incorporating exogenous Information?

I have a monthly commodity demand and try to forecast this series for the next 5 years. Here is a plot: Of course, the natural approach to forecast this seasonality would be some kind exponential ...
1
vote
1answer
58 views

Forecasting a time series with weights

I'd like to forecast (or predict) a time series with weights. The following works using the regular linear modelling techniques ...