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

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12 views

Simple ways to forecast US GDP

Forecasting US GDP sure is hard, even the Fed's FRB/US gets it wrong. I am an undergrad doing a US GDP forecasting project, and was wondering if there were simpler ways to do so and produce decent ...
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8 views

Forecast Function for Historical Data (Fitted Values)

I am fairly new to R so my data manipulation experience isn't as strong as it is with other software packages. I have been primarily using the high level functions that others have written. The ...
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2answers
30 views

Traffic volume/flow prediction method

I have traffic volume data (Surrey City, CA) like this I wish to use Artificial neural network (Deep Learning) or ARIMA to predict traffic flow/volume of the urban area with the use of previous ...
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1answer
38 views

A quick question about time series forecasting

I have collected daily sales data X(t) and Y(t) over two different areas . Total sales Z(t) ...
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41 views

Bad results for R's auto.arima

I have a time series for sales data on a weekly and monthly basis. I tried using holt.winter and auto.arima. ...
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1answer
20 views

Starting-point of time-series influences regression?

I've used tslm() under the R-package fpp to analyse two time series, which seem similar: ...
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20 views

Finding AIC in a Holt-Winters Forecast [on hold]

I've recently come across a problem that I can't find any information on about how to solve it. I've got set of time series data and I've tried modeling it using 9-10 different models in R, using the ...
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36 views

Using ARIMA to Create a Model in R

I'm trying to get understand why the values for my model are different when using two different functions. The first one is from Example 9.2 (International Visitors to Australia), using the ...
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23 views

Can I forecast using two year data?

I have sales data for two years (2013-2014). It is a daily data (Converted into weekly data). I want to forecast sales demand for the next year. Which method can I use for the same. I have price only ...
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1answer
33 views

Weird forecasting results

I am testing a forecast framework which I have developed. I am using an ensemble model (mix of Linear, ETS, ARMA, Bayesian,) which was considerably better than mean forecasts when I was comparing them ...
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17 views

Is ARIMA L-step forecasting the same as applying the model to values 1 to L?

My goal is to forecast sensor measurements (e.g. temperature, humidity) in a lightweight (and real-time) fashion. To this end I use ARIMA forecasting as implemented in R, where I retrain the model ...
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24 views

Fit ARIMA model to new data, preserving some coefficients in R

I create a demand forecast for a company that sells, say, toasters. We have one old standby model that's just finally stocked out, and a series of much newer models with shorter time series of sales ...
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13 views

Forecasting Prediction Accuracy

Out of 4 error paramters which one is best for evaluating prediction accuracy? Average error Mean absolute error Mean squared error Mean absolute % error why?
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24 views

How to extrapolate future probability density functions if you have a time series of them as input?

I'm sorry for lack of technical vocabulary, I'm not a mathematician but an undergraduate student in business informatics. This is my current situation: I am given an observations vector ...
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19 views

Confusion about BATS and TBATS output in R

I have following monthly data from 2011-14: 584 584 606 634 647 661 665 655 676 727 778 781 747 781 774 776 840 860 827 801 811 798 789 748 674 672 656 669 659 678 690 703 721 711 699 673 I ...
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0answers
14 views

Longer forecasting with one-step-ahead model

It is totally a noob question but I cannot find any explanation on the subject. Suppose I build a forecasting system for time series $x$, using as inputs $[x_{t-n},...,x_t]$ to predict the next ...
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17 views

Forecasting accuracy

I have applied forecasting methods on some sample data. where ...
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36 views

Analysis of Multiple Time Series Data with Exogenous Shocks

Real Life ProblemThis one is a tough one and some crowd sourcing seems like a good way to get some feedback. I am trying to determine the effect of Non-Farm Payroll surprises on a subsector of the ...
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1answer
34 views

Forecast bayesian GARCH model

I am using this package in R to do Bayesian estimation of GARCH models. I want to forecast $y_t$ (i.e. the mean equation), but it seems that the package has no built-in function for this. The model ...
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37 views

Forecasting values based on day of week and hour

Disclaimer: Not really good at statistics. Scenario: I have some data, sampled by the hour. I want to make some forecasts taking into consideration that the data seems to be influenced by the day of ...
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1answer
27 views

Can I forecast using two year data

I have data for sales of a company over a period of two years. I want to forecast demand for the next year. What is the process for doing the same? Can I do it with two years?
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1answer
21 views

Meaning of a higher out-of-sample $R^2$ but also a higher MSPE

I am comparing several forecasting methods using the out-of-sample $R^2$ and MSPE. Now I have encountered situations in which the $R^2$ of a certain method is higher than that of the other, but the ...
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0answers
6 views

Alogrithmic way to incorporate Seasonality with only PARTIAL data

I currently am trying to include seasonality in a predictive model (regression) on a partial data set. I know for a fact that seasonality exists for what I am trying to predict (e.g. Jan - Feb are ...
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31 views

Restricting a set of predictions to a range of values of non-negative numbers

I am not even sure how to even phrase this question so if anyone could help that would be great. I am analyzing facebook activity and I wish to predict a particular activity (comments, for instance). ...
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26 views

Rolling window forecasts in Python

I asked this question some days ago but haven't got any response. So I've taken it to myself to do the rolling window manually. My limited grasp on regression forecasting has stumped my progress a ...
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20 views

Analysing the correlation between two trends

I've got 2 growing trends. One is the input (the number of published articles on a website) and I want to understand if the other appears to be correlated (the number of daily visitors). The problem ...
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19 views

Reasons for GLM ('identity') performing better than GLM ('gamma') for predicting a gamma distributed variable?

I am investigating different methods for fitting my target variable (observed wind speed: positive, real, with small values being most probable) using generalized linear modeling (GLM) and - in a ...
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30 views

Improving forecasting output obtained from Winter, ARIMA and TBATS method

I am trying to forecast commodity price for next year. I have collected and plotted monthly average prices from last 10 years.Plot has been attached. I used Holt's-Winter method on prices till 2014 ...
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30 views

Rolling volatility estimation using GARCH family of models in python

Problem: Correct usage of GARCH(1,1) Aim of research: Forecasting volatility/variance. Tools used: Python 3.3 with arch library I am trying to obtain out-of-sample estimation of volatility using a ...
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1answer
31 views

How to calculate $\phi$ (phi) - a first order autocorrelation coefficient

I have a dataset of historical quarterly earnings per share for 8 years. I am trying to use the following formula for the purpose of estimating earnings: $E(Q_t) =Q_{t-4} + \phi_1(Q_{t-1} - Q_{t-5}) ...
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23 views

Distribution of output from accuracy {forecast}?

I'm trying to work out a method for "online" or live model evaluation for models used in forecasting. One approach is to use the R package strucchange, but it ...
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10 views

Optimal Predictor under symmetric loss [self-study]

I am seeking to prove that, under symmetric squared error loss $C(e)=e^2$ where $e$ is the forecast error h periods ahead $e=y_{t+h}-\hat y_{t+h}$, the optimal predictor $\hat y_{t+h}$ = ...
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32 views

Moving window forecasting in Python

I am looking to create some code that will out-of-sample forecast the HAR-RV model. The model itself is formulated as the following, and the betas are estimated through HAC-OLS or Newey-West. ...
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18 views

Forecasting on data you influence?

I have a question about forecasting on data you influence, like trying to reach a specific (composite) sales target and pushing various components of the aggregate sales count in order to make each ...
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70 views

Forecasting quarterly seasonal time series

I have to build a forecasting model to predict one very seasonal time series. I want to predict it with external variables. My first approach was to de-seasonalize the data (time series frequency is ...
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60 views

Machine Learning: curve completion using sets of completed curves

I am very new to the world of machine learning and i am wondering if a) machine learning is able to solve the problem b) whats the best way to do it (pref with example) I have a set of curves for a ...
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1answer
20 views

How could i use r to add data to a table? [closed]

I am looking for an approach. We have a historical table with the consumption per week of a list of items (19.000 item). We want to predict the consumption for the next n weeks. We are thinking on ...
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13 views

Forecast horizon for a multiple regression regression model for forecasting

I have fit a multiple regression model for forecasting with 35 observations training sample cross validated with a test sample of 15 observations. Data used was the time series (1961-2010. There are ...
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15 views

Time series model [duplicate]

I have been give a task for this: "Construct the best-fit time series model for the mean and variance process underlying the portfolio returns, use the best time series model to forecast the mean and ...
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1answer
46 views

Interpreting VECM result

X1 , X2 , X3 and X4 are time series which are stationary at level. I want to establish long term relation between them. I am planning to use it as forecasting model for my work. I want to create this ...
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1answer
24 views

Forecasting model - Scale mismatch

I have the following data: As you can see I want to create a regression model, which forecasts a variable, which I have also on a quartely basis. However, my volume is only on yearly basis. Is ...
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1answer
75 views

forecast(method ='arima') ; auto.arima() function, how to avoid forecast not in line with history?

I am working on an alogorithm in R to automatize a monthly forecast calculation. I am using, among others, the forecast(method='arima') function from the forecast package to calculate forecast. It is ...
2
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1answer
33 views

Forecasting with two or more causal factors using the Holt-Winters method (in R)

Is there something similar to the Holt-Winters forecasting method in R, which can be used to model two or more explanatory factors?
3
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0answers
64 views

How are outliers dealt with in R after detected? [closed]

Once outliers in time series are detected in R how exactly are they dealt with before forecasting? I dont want commands to use i would like the method. Please do not give any answers to do with ...
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0answers
51 views

ARIMA vs. Random Forest

We have some power load functions that of course are driven heavily by a workday rhythm that we need to forecast, and after some light research into the topic, I see that using ARIMA would seemingly ...
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3answers
230 views

Putting less weight on certain data points in a series for forecasting

I have a data set that contains outliers (big orders) i need to forecast this series taking the outliers into consideration. I already know what the top 11 big orders are so i dont need to detect them ...
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0answers
3 views

Forecast joint distribution conditional on multiple external forecasts of marginal distributions?

I need to forecast the joint distribution of the vector $y_i(t+h)$, where $t$ is the time of forecast, and $h$ is a forecast horizon. For instance, $y_1(t+1),y_2(t+1)$ could be wind speed and the ...
2
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1answer
23 views

Exponential smoothing method that can be used in seasonal forecasting without trend

I'm working on the task of forecasting. The data I have is seasonal. I use exponential smoothing methods, but my references (e.g. for the Holt-Winters method) are for using such methods for seasonal ...
2
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1answer
39 views

Prediction intervals in ARIMAX accounting for forecast uncertainty in future $X$?

I have a problem with my SPSS software and ARIMAX forecasts. Consider a series $Y$ that depends on a different series $X$, which is not known in advance with certainty, but must be forecasted itself. ...
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1answer
25 views

Exponential smoothing state space model - stationary required?

I came across with the Exponential smoothing state space model for time series forecasting. My question is if it does require that the time series is stationary? Is there any paper that explicitly ...