Closed. This question is off-topic. It is not currently accepting answers. This question appears to be off-topic because EITHER it is not about statistics, machine learning, data analysis, data mining, or data visualization, OR it focuses on programming, debugging, or performing routine operations within a statistical computing platform. If the latter, you could try the … Read more
I am working on time series to forecast the number of inbound calls for next 1 month on a daily basis. I built a model using auto.arima and forecasted. My forecasting data is showing gradually increasing no. of calls irrespective of holiday or weekend. (I have data for 2 months Aug-1-2016 to Dec-31- 2016. Here … Read more
Closed. This question is off-topic. It is not currently accepting answers. This question appears to be off-topic because EITHER it is not about statistics, machine learning, data analysis, data mining, or data visualization, OR it focuses on programming, debugging, or performing routine operations within a statistical computing platform. If the latter, you could try the … Read more
I have a seasonally decomposed data set. The data set has strong seasonality. Now I am trying to fit the 'seasonal part' of dataset into ARIMA model and tried to forecast (with SPSS). The problem is, I get exactly same values in forecasts as that of actual values. So, MAPE is coming to be 0.000 … Read more
I've got two time series (parameters of a model for males and females) and aim to identify an appropriate ARIMA model in order to make forecasts. My time series looks like: The plot and the ACF show non-stationary (the spikes of the ACF cut off very slowly). Thus, I use differencing and obtain: This plot … Read more
I've got two time series (parameters of a model for males and females) and aim to identify an appropriate ARIMA model in order to make forecasts. My time series looks like: The plot and the ACF show non-stationary (the spikes of the ACF cut off very slowly). Thus, I use differencing and obtain: This plot … Read more
I fail to see the difference between Moving Average (MA): $x_t=epsilon_t+β_1epsilon_{t−1}+…+β_qepsilon_{t−q}$ Autoregressive (AR): $x_t=epsilon_t+β_1x_{t−1}+…+β_qx_{t−q}$ $x_t$ is the observed value and $epsilon$ is the residual. But since both assume a stationary time series with $mu = 0$, isn't the residual and the observed value identical? I.e., $epsilon_i = x_i – mu = x_i$? This would imply … Read more
I have been trying to forecast the sales revenue of different product groups (the displayed sales revenue is aggregated over all products for each day e.g. smartphones with different prices as one group) but haven't found the right approach yet. I am pretty much a novice in time series analysis / forecasting and use Python. … Read more
What is the equation for an ARIMA(1, 1, 0) Model? Please note that I fit the model to a time series in R and received an "ar1" coefficient of 0.6017 and that was the only coefficient estimate provided by R. Best Answer For base R the equation for an ARIMA(1,1,0) model is $Delta Y_t = … Read more
I have a time series that shows a nonstationary seasonal autoregressive component as well as known heteroshedasticity. In order to model the series, I have fit a seasonal ARIMA model for the mean with the auto.arima model in the forecast package in R and a GARCH model on the residuals of the ARIMA model. Is … Read more