IPredict, a sequence prediction Framework
Sequence prediction consists in predicting the next item(s) of a sequence of items, given a set of training sequences and a finite alphabet of items (symbols). This task has numerous applications such as web page prefetching, product recommendation, weather forecasting and stock market prediction.
This frameworks has implementations for the following sequence predictors:
- Compact Prediction Tree (CPT) 
- Compact Prediction Tree Plus (CPT+) 
- First order Markov Chains (PPM)  1
- Dependency Graph (DG)  2
- All-k-Order Markov Chains (AKOM)  3
- TDAG  4
- LZ78  12
The following datasets are compatible with this framework and can be used to perform experiments with the proposed sequence prediction models.
BMS is a popular dataset in the field of association rule mining made available for KDD CUP 2000. It contains web sessions from an e-commerce website, encoded as sequences of integers, representing web pages. %(parse)
FIFA contains web sessions recorded on the 1998 FIFA World Cup Web site and holds over one million web page requests. Originally, the dataset is a set of individual requests containing metadata (e.g. client id and time). We converted requests into sequences by grouping requests by users and splitting a sequence if there was a delay of more than an hour between two requests. Our final dataset is a random sample from the original dataset.
SIGN is a dense dataset with long sequences, containing 730 sequences of sign-language utterances transcripted from videos.
KOSARAK is a dataset containing web sessions from a Hungarian news portal available at http://fimi.ua.ac.be/data. It is the largest dataset used in our experimental evaluation.
BIBLE is the religious Christian set of books used in plain text as a flow of sentences. The prediction task consists in predicting the next character in a given sequence of characters. The book is split in sentences where each sentence is a sequence. This dataset is interesting since it has a small alphabet with only 75 distinct characters and it is based on natural language.
 T. Gueniche, P. Fournier-Viger, V. S. Tseng, "Compact prediction tree: A lossless modelfor accurate sequence prediction". Proceedings of the Ninth Advanced Data Mining and Applications, Springer, 2013, pp. 177-188.
 T. Gueniche, P. Fournier-Viger, R. Raman, V. S. Tseng, "CPT+: Decreasing the time/space complexity of the Compact Prediction Tree". Proceedings of the 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2015), Springer, 12 pages (to appear).
 J. G. Cleary, I. Witten, "Data compression using adaptive coding and partial string matching".IEEE Transactions on Communications, vol. 32, pp. 396-402, 1984.
 V. N. Padmanabhan, J. C. Mogul, "Using predictive prefetching to improve world wide web latency". ACM SIGCOMM Computer Communication Review, vol. 26, pp. 22-36, 1996.
 J. Pitkow, P. Pirolli, "Mining longest repeating subsequences to predict world wide websurfing". Proceedings of the Second USENIX Symposium on Internet Technologies and Systems, 1999, pp. 1.
 P. Laird, R. Saul, "Discrete sequence prediction and its applications". Machine Learning,vol. 15, pp. 43-68, 1994.
 J. Ziv, A. Lempel, "Compression of individual sequences via variable-rate coding". IEEETransactions on Information Theory, vol. 24, pp. 530-536, 1978.