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
0answers
6 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
13 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
15 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
28 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
23 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
26 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
0answers
37 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
25 views

ets() or stlf()

Every where I read, experts suggested to use ets() to better determine alpha, beta, gamma ...
0
votes
0answers
29 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
21 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
25 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
28 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
52 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
30 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 ...
2
votes
2answers
69 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
62 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
59 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
14 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
164 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
20 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
81 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
74 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
42 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 ...
2
votes
0answers
40 views

forecast improvement using Kalman FIlter clearing

I have been facing a wall after doing a forecast of wind speed time series data using ARIMA with python. I have result with a nrmse growth going from 2% to 15% and now what I want is to use kalman ...
1
vote
0answers
63 views

Forecasting using auto.arima

I have the weekly revenue data for an electronics company the decomposed plot of which is as follows: I have decided to keep the seasonality and apply a suitable forecasting technique. I tried ...
0
votes
0answers
52 views

Removing seasonality from data

I have a dataset depicting weekly revenue over time for a computer company. The plot for the data looks like this: I decomposed the data into its additive components using the ...
3
votes
2answers
32 views

Lagged Dependents

I am in a scenario where I am trying to forecast 2014 call volume in a call center based on prior call volumes in 2013 and 2012. How do I difference 2014 call volume, and how do I lag 2012 and 2013 ...
4
votes
1answer
151 views

Detecting Outliers in Time Series (LS/AO/TC) using tsoutliers package in R. How to represent outliers in equation format?

Comments: Firstly I would like to say a big thank you to the author of the new tsoutliers package which implements Chen and Liu's time series outlier detection which was published in the Journal of ...
1
vote
0answers
18 views

Exponentially weighted moving average control limits

Hi I have collected some process data for 3 years and I want to mimic a EWMA prospective analysis, to see if my set smoothing parameter would have detect all the important changes (without too many ...
0
votes
0answers
38 views

temperature prediction algorithm

I found an interesting problem in a contest on temperature prediction: https://www.hackerrank.com/contests/expansion-challenge/challenges/temperature-predictions It is not about forecasting the ...
0
votes
0answers
57 views

Forecasting in R using forecast package

I'm trying to forecast hourly data for 30 days for a process. I have used the following code: ...
0
votes
1answer
35 views

Cluster analysis on time series samples

In the follow-up to this Ways to understand 2-dimensional time-series data I'm working on 2D time series data where two attributes are depth and temperature. When I plotted depth-vs-temp curve and ...
0
votes
0answers
31 views

Discovering Values with Forecast

So, I have numbers that I know are going to be between 1.01 and 1000. Each value corresponds to 1 second. Is this is a time-series? I've been searching all over, but I can't figure out if these ...
1
vote
2answers
77 views

Ways to understand 2-dimensional time-series data

I'm working on 2D time series data where two attributes are depth and temperature. When I plotted depth-vs-temp curve and saw its variation over time, the fluctuation occurs at few places only. I'm ...
0
votes
1answer
80 views

Machine-Learning algorithms for Forecasting

For work, I'm working on an app where you essentially forecast the failure rate of the overall machine through different factors such as the historical failure rates for the components used to build ...
0
votes
1answer
49 views

Need clarity on alpha, beta, gamma optimization in Triple Exponential Smoothing Forecast

I asked a variation of this question, but I want to be more direct. Take the exact same Triple Exponential Smoothing Model (Holt-Winters with a moving level, trend, and seasonal component)--- Would ...
0
votes
1answer
45 views

Implement different variation of Holt Winter technique using R for 52 weeks of data

As per my job requirement I have to do forecasting using only Holt Winter technique in R.I have weekly data for 2 and half years & I have to predict weekly.I'm planning to build time series with ...
0
votes
0answers
24 views

forecasts ahead of t+1 in time-varying linear state space models

When the matrices in the model are constant, then performing forecasts is straight-forward. However, when using a time-varying model like dynamic regression I'm not sure how to proceed since we don't ...
2
votes
2answers
50 views

Forecasting: Different Model for 1 month, 2 month, 6 month forecasts?

I'm still trying to expand my statistics and forecasting technique knowledge. Right now I'm forecasting seasonal contact patterns, so the simplest model I can understand with seasonality is a ...
5
votes
1answer
54 views

When forecasting sequential data is it best to use auto-regressive models or build a more traditional n x p dataset with features?

I'm familiar with the use of auto-regressive models when it comes to forecasting a single vector of time-series data. Is anybody familiar with a more traditional modeling approach, i.e. - creating ...
1
vote
0answers
27 views

What method(s) should I use to achive statistical ranking?

I'm attempting a project where I need to statistically rank available cars based on several variables such as cost, mpg, seating, milage, etc.. I wish to rank these cars in order decide which car ...
6
votes
3answers
192 views

stochastic vs deterministic trend/seasonality in time series forecasting

I have moderate background in time series forecasting. I have looked at several forecasting books, and I don't see the following questions addressed in any of them. I have two questions: How would ...
0
votes
0answers
35 views

Plotting a Probit Time Series in R

I've searched around a bit, but am having trouble finding an answer to my specific question that isn't too technical. I want to predict the probability that we are at a turning point – arbitrarily ...
1
vote
2answers
183 views

Predictive Models for the Soccer World Cup 2014

The Goldman Sachs model was already discussed here. I wonder whether there are other statistical forecasts publicly available, or even better, models including raw data. Specifically, I thought that ...
1
vote
0answers
26 views

Verification of assumptions in TBATS model

I have a question about using BATS/TBATS models implemented in the forecast package for R. In De Liv­era, Hyndman & Snyder (2011) the models are used without any following analysis. Is it OK to ...
1
vote
0answers
48 views
1
vote
1answer
31 views

Training and test data for Holt Winters Method

I am new to forecasting. I have weekly data. I want to forecast using the Holt-Winters method. Should I make training and test subsets of the data? How important are the training and test data?
0
votes
1answer
31 views

How can I use the results of GARCH in order to improve a forecast?

I am kind of confused with what I should actually do with predicted volatility values that I obtained via a ARCH/GARCH model other than feeling happy that I know when volatility rises/falls. Is there ...
0
votes
1answer
62 views

Please help me understand white noise and MA(q) [closed]

I am reading the section about moving average models in Hyndman & Athanasopoulos Forecasting: principles and practice. I am trying to understand the MA(q) model in words. What is white noise? Is ...