0
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0answers
13 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
21 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
20 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
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0answers
23 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
1answer
26 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
51 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
28 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
65 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
56 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
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
70 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
38 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 ...
4
votes
1answer
147 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 ...
0
votes
0answers
37 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 ...
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
79 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
46 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 ...
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
50 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 ...
6
votes
3answers
184 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 ...
1
vote
0answers
25 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 ...
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
60 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 ...
3
votes
1answer
310 views

Choosing the right forecasting technique

I'm currently attempting to forecast visitor data for stores. My dataset includes daily visitor totals of three years. Note that the dataset isn't complete (stores can be closed for a few days, etc). ...
1
vote
1answer
60 views

time series — seasonal adjustment

I'm concerned to seasonal adjustment procedure and want to know the criteria for this purpose can anyone please give me the answer of the following question. what should be the criteria for seasonal ...
0
votes
0answers
32 views

Interpretation of TBATS Components

I've searched everywhere for this answer, and come up empty handed. I'm building a forecasts model for interface traffic reported in 5 minute intervals over a 1 year period. To account for ...
1
vote
1answer
74 views

R-squared to compare forecasting techniques

Is it appropriate when forecasting to use $R^2$ as the measure of how well exponential smoothing fits a data set for the purpose of time-series forecasting? I understand that it is appropriate for ...
4
votes
2answers
95 views

Why do the 95% confidence limits in ARIMA models widen at the forecasts?

Can someone please explain why when I do an ARIMA model the forecast's 95% confidence interval widen?
0
votes
1answer
102 views

How to forecast multivariate time-series 'accurately' with a large number of unknown factors using R?

I am relatively new to statistics and not formally trained but have been given a complex problem to solve and need some guidance. I realise that I am out of my depth a bit here but would appreciate ...
2
votes
0answers
116 views

Time Series: Seasonality and trend

I am interested in financial time series and I have a small question regarding the use of the forecast package. The time series I am interested in is a monthly one and present clear evidences of ...
2
votes
1answer
75 views

The relationship between trading profitibility and forecasting accuracy

is forecasting accuracy and trading profitability related? How to explain the existence of the relationship? Thank you.
0
votes
0answers
21 views

L-Step Ahead Forecasting and Yule Walker in Time Series

Im trying to get my head around the l-step ahead forecasting method for my time series exam. I've looked in a few books and my lecture notes but the actual method does not make much sense to me. It ...
1
vote
1answer
56 views

Increasing the accuracy of tbats() forecasts by factoring for correlations between different time-series?

This question builds on my previous question Forecasting Hourly Time Series based on previous weeks and same period in previous year/s. My project is to forecast the number of ~400 different types of ...
0
votes
1answer
37 views

Looking for help with ITSM software (or other comparable software)

I'm looking for someone who is familiar with the ITSM software. I have some data that needs to be fit with an ARIMA/SARIMA model and then forecast using Holt-Winters/Seasonal method. I then need to ...
0
votes
0answers
21 views

Suggestions - forecasting models

I have been assigned a task where I need to solve a business case. Let me explain what information I am looking for: I have historical data of several products and I need to forecast the time ...
1
vote
0answers
82 views

Forecasting Hourly Time Series based on previous weeks and same period in previous year/s

The Problem I have been tasked with a similar problem to that described in Forecasting hourly time series with daily, weekly & annual periodicity. My data shows the number of times that one of ...
0
votes
1answer
54 views

What are the difference between an ARMA(2,1) model and an ARMA(13,9) model?

I was wondering if there are any other things that I can say about the differences between these 2 models, besides the comments about the ACF and PACF. Specifically, I was wondering which model would ...
6
votes
1answer
174 views

How do I use math to predict the next number in the series?

Here's a series of data I'm observing: 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1 How do I use math to predict whether the next number in ...
6
votes
0answers
159 views

Multivariant time series in R. How to find lagged correlation and build model for forecasting

I'm new in the page and pretty new in statistics and R. I'm working on a project for college with the objective of finding the correlation between rain and water flow level in rivers. Once the ...
1
vote
2answers
95 views

How to produce the minimum forecast error using R?

Considering that we want to use optimize() on the interval [0,1] how can I write an R code for finding the value of β that produces the minimum forecast error without using external packages like ...
0
votes
2answers
75 views

Is there any tool that can do Vector ARIMA modeling in time series

Vector ARIMA model is used in multiple time series analysis. I am just wondering if there is any software or tool can be used to build the model. Some tools,like R, can only be used to predict the ...
1
vote
1answer
39 views

Using results of regression on raw observation values to approximate results of regression on relative change between observations (Simple, Linear)

this is my first time on Stack Exchange so if I did something wrong please tell me. I have a time series dataset. There is an observation $(y,x)$ for each continuous time $t$. Let’s say for each day ...
0
votes
0answers
75 views

What is the difference between forecasting based on ARIMA and logistic curve? R

I'm making a project connected with identifying the dynamics of sales. My database concerns 26 weeks (so equally in 26 time-series observations) after launching the product. This is what my database ...
1
vote
1answer
112 views

Forecasting with holiday dummy variables

I have an example of call center data for 2013. There are 261 days of data (excluding weekends). For 2013, I have included a holiday dummy variable (holiday) for ...